List of fixed Common Vulnerabilities and Exposures in Cloudera Data Services on premises 1.5.5 SP4

Review the Common vulnerabilities and Exposures (CVEs) that were fixed in Cloudera Data Services on premises 1.5.5 SP4 release.

CVE IDDescriptionLibrary Path
CVE-2007-3641 archive_read_support_format_tar.c in libarchive before 2.2.4 does not properly compute the length of a certain buffer when processing a malformed pax extension header, which allows user-assisted remote attackers to cause a denial of service (crash) and possibly execute arbitrary code via a crafted (1) PAX or (2) TAR archive that triggers a buffer overflow.

ml-runtime-pbj-conda-standard

CVE-2007-3644 archive_read_support_format_tar.c in libarchive before 2.2.4 allows user-assisted remote attackers to cause a denial of service (infinite loop) via (1) an end-of-file condition within a pax extension header or (2) a malformed pax extension header in an (a) PAX or a (b) TAR archive.

ml-runtime-pbj-conda-standard

CVE-2007-3645 archive_read_support_format_tar.c in libarchive before 2.2.4 allows user-assisted remote attackers to cause a denial of service (crash) via (1) an end-of-file condition within a tar header that follows a pax extension header or (2) a malformed pax extension header in an (a) PAX or a (b) TAR archive, which results in a NULL pointer dereference, a different issue than CVE-2007-3644.

ml-runtime-pbj-conda-standard

CVE-2011-1777 Multiple buffer overflows in the (1) heap_add_entry and (2) relocate_dir functions in archive_read_support_format_iso9660.c in libarchive through 2.8.5 allow remote attackers to cause a denial of service (application crash) or possibly execute arbitrary code via a crafted ISO9660 image.

ml-runtime-pbj-conda-standard

CVE-2011-1778 Buffer overflow in libarchive through 2.8.5 allows remote attackers to cause a denial of service (application crash) or possibly execute arbitrary code via a crafted TAR archive.

ml-runtime-pbj-conda-standard

CVE-2011-4461 Jetty 8.1.0.RC2 and earlier computes hash values for form parameters without restricting the ability to trigger hash collisions predictably, which allows remote attackers to cause a denial of service (CPU consumption) by sending many crafted parameters.

databus-producer
dex_thunderhead-dbuswxmclient
obs_agent

CVE-2012-6708 jQuery before 1.9.0 is vulnerable to Cross-site Scripting (XSS) attacks. The jQuery(strInput) function does not differentiate selectors from HTML in a reliable fashion. In vulnerable versions, jQuery determined whether the input was HTML by looking for the '<' character anywhere in the string, giving attackers more flexibility when attempting to construct a malicious payload. In fixed versions, jQuery only deems the input to be HTML if it explicitly starts with the '<' character, limiting exploitability only to attackers who can control the beginning of a string, which is far less common.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2013-4235 shadow: TOCTOU (time-of-check time-of-use) race condition when copying and removing directory trees

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2013-10075 Apache::Session versions through 1.94 for Perl re-creates deleted sessions. The session stores Apache::Session::Store::File and Apache::Session::Store::DB_File will create a session that does not exist. This can lead to sessions being revived, potentially with data that was to be deleted.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2015-5237 protobuf allows remote authenticated attackers to cause a heap-based buffer overflow.

admissiond
catalogd
cdc-profilers
cdc_profilers
cml-addon-hadoop-cli-7.1.9.20000-24
cml-addon-hadoop-cli-7.3.1.200-90
cml-addon-hadoop-cli-7.3.1.709-1
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-runtime-3.5.4-7.1.9.1078
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-server-2.4.8-7.1.9.1078
dex-livy-server-3.3.2-7.1.9.1078
dex-livy-server-3.5.4-7.1.9.1078
dex-livy-server-3.5.4-7.3.1.709
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-history-server-3.3.2-7.1.9.1078
dex-spark-history-server-3.5.4-7.1.9.1078
dex-spark-history-server-3.5.4-7.3.1.709
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat
dex-spark-runtime-3.5.4-7.1.9.1078
dex-spark-runtime-3.5.4-7.3.1.709
hive
impalad_coord_exec
impalad_coordinator
impalad_executor
ozone-parcel-image

CVE-2015-8553 Xen allows guest OS users to obtain sensitive information from uninitialized locations in host OS kernel memory by not enabling memory and I/O decoding control bits. NOTE: this vulnerability exists because of an incomplete fix for CVE-2015-0777.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2015-9251 jQuery before 3.0.0 is vulnerable to Cross-site Scripting (XSS) attacks when a cross-domain Ajax request is performed without the dataType option, causing text/javascript responses to be executed.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2016-1252 The apt package in Debian jessie before 1.0.9.8.4, in Debian unstable before 1.4~beta2, in Ubuntu 14.04 LTS before 1.0.1ubuntu2.17, in Ubuntu 16.04 LTS before 1.2.15ubuntu0.2, and in Ubuntu 16.10 before 1.3.2ubuntu0.1 allows man-in-the-middle attackers to bypass a repository-signing protection mechanism by leveraging improper error handling when validating InRelease file signatures.

cdsw-s2i-builder-buildah

CVE-2016-2510 BeanShell (bsh) before 2.0b6, when included on the classpath by an application that uses Java serialization or XStream, allows remote attackers to execute arbitrary code via crafted serialized data, related to XThis.Handler.

trino

CVE-2016-6524 These are all security issues fixed in the jupyter-notebook-6.2.0-1.4 package on the GA media of openSUSE Tumbleweed.

hue

CVE-2016-10735 In Bootstrap 3.x before 3.4.0 and 4.x-beta before 4.0.0-beta.2, XSS is possible in the data-target attribute, a different vulnerability than CVE-2018-14041.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2017-11164 In PCRE 8.41, the OP_KETRMAX feature in the match function in pcre_exec.c allows stack exhaustion (uncontrolled recursion) when processing a crafted regular expression.

cml-addon-hadoop-cli-7.3.1.200-90
ml-runtime-pbj-workbench-r4.5-standard
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2017-14992 Lack of content verification in Docker-CE (Also known as Moby) versions 1.12.6-0, 1.10.3, 17.03.0, 17.03.1, 17.03.2, 17.06.0, 17.06.1, 17.06.2, 17.09.0, and earlier allows a remote attacker to cause a Denial of Service via a crafted image layer payload, aka gzip bombing.

cdsw-s2i-builder-buildah

CVE-2018-3721 lodash node module before 4.17.5 suffers from a Modification of Assumed-Immutable Data (MAID) vulnerability via defaultsDeep, merge, and mergeWith functions, which allows a malicious user to modify the prototype of "Object" via __proto__, causing the addition or modification of an existing property that will exist on all objects.

cdsw-web

CVE-2018-8115 A remote code execution vulnerability exists when the Windows Host Compute Service Shim (hcsshim) library fails to properly validate input while importing a container image, aka "Windows Host Compute Service Shim Remote Code Execution Vulnerability." This affects Windows Host Compute.

cdsw-s2i-builder-buildah

CVE-2018-8883 Netwide Assembler (NASM) 2.13.02rc2 has a buffer over-read in the parse_line function in asm/parser.c via uncontrolled access to nasm_reg_flags.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-10254 Netwide Assembler (NASM) 2.13 has a stack-based buffer over-read in the disasm function of the disasm/disasm.c file. Remote attackers could leverage this vulnerability to cause a denial of service or possibly have unspecified other impact via a crafted ELF file.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-12608 An issue was discovered in Docker Moby before 17.06.0. The Docker engine validated a client TLS certificate using both the configured client CA root certificate and all system roots on non-Windows systems. This allowed a client with any domain validated certificate signed by a system-trusted root CA (as opposed to one signed by the configured CA root certificate) to authenticate.

cdsw-s2i-builder-buildah

CVE-2018-14040 In Bootstrap before 4.1.2, XSS is possible in the collapse data-parent attribute.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-14041 In Bootstrap before 4.1.2, XSS is possible in the data-target property of scrollspy.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-14042 In Bootstrap before 4.1.2, XSS is possible in the data-container property of tooltip.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-16382 Netwide Assembler (NASM) 2.14rc15 has a buffer over-read in x86/regflags.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-16487 A prototype pollution vulnerability was found in lodash <4.17.11 where the functions merge, mergeWith, and defaultsDeep can be tricked into adding or modifying properties of Object.prototype.

cdsw-web

CVE-2018-16517 asm/labels.c in Netwide Assembler (NASM) is prone to NULL Pointer Dereference, which allows the attacker to cause a denial of service via a crafted file.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-19213 Netwide Assembler (NASM) through 2.14rc16 has memory leaks that may lead to DoS, related to nasm_malloc in nasmlib/malloc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-19214 Netwide Assembler (NASM) 2.14rc15 has a heap-based buffer over-read in expand_mmac_params in asm/preproc.c for insufficient input.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-19215 Netwide Assembler (NASM) 2.14rc16 has a heap-based buffer over-read in expand_mmac_params in asm/preproc.c for the special cases of the % and $ and ! characters.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-19216 Netwide Assembler (NASM) before 2.13.02 has a use-after-free in detoken at asm/preproc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-20538 There is a use-after-free at asm/preproc.c (function pp_getline) in Netwide Assembler (NASM) 2.14rc16 that will cause a denial of service during certain finishes tests.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-20676 In Bootstrap before 3.4.0, XSS is possible in the tooltip data-viewport attribute.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2018-20677 In Bootstrap before 3.4.0, XSS is possible in the affix configuration target property.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-6290 An infinite recursion issue was discovered in eval.c in Netwide Assembler (NASM) through 2.14.02. There is a stack exhaustion problem resulting from infinite recursion in the functions expr, rexp, bexpr and cexpr in certain scenarios involving lots of '{' characters. Remote attackers could leverage this vulnerability to cause a denial-of-service via a crafted asm file.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-6291 An issue was discovered in the function expr6 in eval.c in Netwide Assembler (NASM) through 2.14.02. There is a stack exhaustion problem caused by the expr6 function making recursive calls to itself in certain scenarios involving lots of '!' or '+' or '-' characters. Remote attackers could leverage this vulnerability to cause a denial-of-service via a crafted asm file.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-7147 A buffer over-read exists in the function crc64ib in crc64.c in nasmlib in Netwide Assembler (NASM) 2.14rc16. A crafted asm input can cause segmentation faults, leading to denial-of-service.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-8331 In Bootstrap before 3.4.1 and 4.3.x before 4.3.1, XSS is possible in the tooltip or popover data-template attribute.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-8343 In Netwide Assembler (NASM) 2.14.02, there is a use-after-free in paste_tokens in asm/preproc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2019-10086 In Apache Commons Beanutils 1.9.2, a special BeanIntrospector class was added which allows suppressing the ability for an attacker to access the classloader via the class property available on all Java objects. We, however were not using this by default characteristic of the PropertyUtilsBean.

hive

CVE-2019-11358 jQuery before 3.4.0, as used in Drupal, Backdrop CMS, and other products, mishandles jQuery.extend(true, {}, ...) because of Object.prototype pollution. If an unsanitized source object contained an enumerable __proto__ property, it could extend the native Object.prototype.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2019-16884 runc through 1.0.0-rc8, as used in Docker through 19.03.2-ce and other products, allows AppArmor restriction bypass because libcontainer/rootfs_linux.go incorrectly checks mount targets, and thus a malicious Docker image can mount over a /proc directory.

cdsw-s2i-builder-buildah

CVE-2019-19794 The miekg Go DNS package before 1.1.25, as used in CoreDNS before 1.6.6 and other products, improperly generates random numbers because math/rand is used. The TXID becomes predictable, leading to response forgeries.

cdsw-s2i-builder-buildah

CVE-2019-19921 runc through 1.0.0-rc9 has Incorrect Access Control leading to Escalation of Privileges, related to libcontainer/rootfs_linux.go. To exploit this, an attacker must be able to spawn two containers with custom volume-mount configurations, and be able to run custom images. (This vulnerability does not affect Docker due to an implementation detail that happens to block the attack.)

cdsw-s2i-builder-buildah

CVE-2019-1010266 lodash prior to 4.17.11 is affected by: CWE-400: Uncontrolled Resource Consumption. The impact is: Denial of service. The component is: Date handler. The attack vector is: Attacker provides very long strings, which the library attempts to match using a regular expression. The fixed version is: 4.17.11.

cdsw-web

CVE-2020-7656 jquery prior to 1.9.0 allows Cross-site Scripting attacks via the load method. The load method fails to recognize and remove "<script>" HTML tags that contain a whitespace character, i.e: "</script >", which results in the enclosed script logic to be executed.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2020-8908 A temp directory creation vulnerability exists in all versions of Guava, allowing an attacker with access to the machine to potentially access data in a temporary directory created by the Guava API com.google.common.io.Files.createTempDir(). By default, on unix-like systems, the created directory is world-readable (readable by an attacker with access to the system). The method in question has been marked @Deprecated in versions 30.0 and later and should not be used. For Android developers, we recommend choosing a temporary directory API provided by Android, such as context.getCacheDir(). For other Java developers, we recommend migrating to the Java 7 API java.nio.file.Files.createTempDirectory() which explicitly configures permissions of 700, or configuring the Java runtime's java.io.tmpdir system property to point to a location whose permissions are appropriately configured.

trino

CVE-2020-11022 In jQuery versions greater than or equal to 1.2 and before 3.5.0, passing HTML from untrusted sources - even after sanitizing it - to one of jQuery's DOM manipulation methods (i.e. .html(), .append(), and others) may execute untrusted code. This problem is patched in jQuery 3.5.0.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2020-11023 In jQuery versions greater than or equal to 1.0.3 and before 3.5.0, passing HTML containing <option> elements from untrusted sources - even after sanitizing it - to one of jQuery's DOM manipulation methods (i.e. .html(), .append(), and others) may execute untrusted code. This problem is patched in jQuery 3.5.0.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2020-15257 containerd is an industry-standard container runtime and is available as a daemon for Linux and Windows. In containerd before versions 1.3.9 and 1.4.3, the containerd-shim API is improperly exposed to host network containers. Access controls for the shim’s API socket verified that the connecting process had an effective UID of 0, but did not otherwise restrict access to the abstract Unix domain socket. This would allow malicious containers running in the same network namespace as the shim, with an effective UID of 0 but otherwise reduced privileges, to cause new processes to be run with elevated privileges. This vulnerability has been fixed in containerd 1.3.9 and 1.4.3. Users should update to these versions as soon as they are released. It should be noted that containers started with an old version of containerd-shim should be stopped and restarted, as running containers will continue to be vulnerable even after an upgrade. If you are not providing the ability for untrusted users to start containers in the same network namespace as the shim (typically the "host" network namespace, for example with docker run --net=host or hostNetwork: true in a Kubernetes pod) and run with an effective UID of 0, you are not vulnerable to this issue. If you are running containers with a vulnerable configuration, you can deny access to all abstract sockets with AppArmor by adding a line similar to deny unix addr=@**, to your policy. It is best practice to run containers with a reduced set of privileges, with a non-zero UID, and with isolated namespaces. The containerd maintainers strongly advise against sharing namespaces with the host. Reducing the set of isolation mechanisms used for a container necessarily increases that container's privilege, regardless of what container runtime is used for running that container.

cdsw-s2i-builder-buildah

CVE-2020-15522 Bouncy Castle BC Java before 1.66, BC C# .NET before 1.8.7, BC-FJA before 1.0.1.2, 1.0.2.1, and BC-FNA before 1.0.1.1 have a timing issue within the EC math library that can expose information about the private key when an attacker is able to observe timing information for the generation of multiple deterministic ECDSA signatures.

thunderhead-servicediscoverysimple

CVE-2020-25645 A flaw was found in the Linux kernel in versions before 5.9-rc7. Traffic between two Geneve endpoints may be unencrypted when IPsec is configured to encrypt traffic for the specific UDP port used by the GENEVE tunnel allowing anyone between the two endpoints to read the traffic unencrypted. The main threat from this vulnerability is to data confidentiality.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-4442 In the Linux kernel, the following vulnerability has been resolved: tcp: add sanity tests to TCP_QUEUE_SEQ Qingyu Li reported a syzkaller bug where the repro changes RCV SEQ _after_ restoring data in the receive queue. mprotect(0x4aa000, 12288, PROT_READ) = 0 mmap(0x1ffff000, 4096, PROT_NONE, MAP_PRIVATE|MAP_FIXED|MAP_ANONYMOUS, -1, 0) = 0x1ffff000 mmap(0x20000000, 16777216, PROT_READ|PROT_WRITE|PROT_EXEC, MAP_PRIVATE|MAP_FIXED|MAP_ANONYMOUS, -1, 0) = 0x20000000 mmap(0x21000000, 4096, PROT_NONE, MAP_PRIVATE|MAP_FIXED|MAP_ANONYMOUS, -1, 0) = 0x21000000 socket(AF_INET6, SOCK_STREAM, IPPROTO_IP) = 3 setsockopt(3, SOL_TCP, TCP_REPAIR, [1], 4) = 0 connect(3, {sa_family=AF_INET6, sin6_port=htons(0), sin6_flowinfo=htonl(0), inet_pton(AF_INET6, "::1", &sin6_addr), sin6_scope_id=0}, 28) = 0 setsockopt(3, SOL_TCP, TCP_REPAIR_QUEUE, [1], 4) = 0 sendmsg(3, {msg_name=NULL, msg_namelen=0, msg_iov=[{iov_base="0x0000000000000003\0\0", iov_len=20}], msg_iovlen=1, msg_controllen=0, msg_flags=0}, 0) = 20 setsockopt(3, SOL_TCP, TCP_REPAIR, [0], 4) = 0 setsockopt(3, SOL_TCP, TCP_QUEUE_SEQ, [128], 4) = 0 recvfrom(3, NULL, 20, 0, NULL, NULL) = -1 ECONNRESET (Connection reset by peer) syslog shows: [ 111.205099] TCP recvmsg seq # bug 2: copied 80, seq 0, rcvnxt 80, fl 0 [ 111.207894] WARNING: CPU: 1 PID: 356 at net/ipv4/tcp.c:2343 tcp_recvmsg_locked+0x90e/0x29a0 This should not be allowed. TCP_QUEUE_SEQ should only be used when queues are empty. This patch fixes this case, and the tx path as well.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-21240 httplib2 is a comprehensive HTTP client library for Python. In httplib2 before version 0.19.0, a malicious server which responds with long series of "\xa0" characters in the "www-authenticate" header may cause Denial of Service (CPU burn while parsing header) of the httplib2 client accessing said server. This is fixed in version 0.19.0 which contains a new implementation of auth headers parsing using the pyparsing library.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2021-21334 In containerd (an industry-standard container runtime) before versions 1.3.10 and 1.4.4, containers launched through containerd's CRI implementation (through Kubernetes, crictl, or any other pod/container client that uses the containerd CRI service) that share the same image may receive incorrect environment variables, including values that are defined for other containers. If the affected containers have different security contexts, this may allow sensitive information to be unintentionally shared. If you are not using containerd's CRI implementation (through one of the mechanisms described above), you are not vulnerable to this issue. If you are not launching multiple containers or Kubernetes pods from the same image which have different environment variables, you are not vulnerable to this issue. If you are not launching multiple containers or Kubernetes pods from the same image in rapid succession, you have reduced likelihood of being vulnerable to this issue This vulnerability has been fixed in containerd 1.3.10 and containerd 1.4.4. Users should update to these versions.

cdsw-s2i-builder-buildah

CVE-2021-30465 runc before 1.0.0-rc95 allows a Container Filesystem Breakout via Directory Traversal. To exploit the vulnerability, an attacker must be able to create multiple containers with a fairly specific mount configuration. The problem occurs via a symlink-exchange attack that relies on a race condition.

cdsw-s2i-builder-buildah

CVE-2021-33450 An issue was discovered in NASM version 2.16rc0. There are memory leaks in nasm_calloc() in nasmlib/alloc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2021-33452 An issue was discovered in NASM version 2.16rc0. There are memory leaks in nasm_malloc() in nasmlib/alloc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2021-40490 A race condition was discovered in ext4_write_inline_data_end in fs/ext4/inline.c in the ext4 subsystem in the Linux kernel through 5.13.13.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-44269 An out of bounds read was found in Wavpack 5.4.0 in processing *.WAV files. This issue triggered in function WavpackPackSamples of file src/pack_utils.c, tainted variable cnt is too large, that makes pointer sptr read beyond heap bound.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2021-45256 A Null Pointer Dereference vulnerability existfs in nasm 2.16rc0 via asm/preproc.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2021-45257 An infinite loop vulnerability exists in nasm 2.16rc0 via the gpaste_tokens function.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2021-46925 In the Linux kernel, the following vulnerability has been resolved: net/smc: fix kernel panic caused by race of smc_sock A crash occurs when smc_cdc_tx_handler() tries to access smc_sock but smc_release() has already freed it. [ 4570.695099] BUG: unable to handle page fault for address: 000000002eae9e88 [ 4570.696048] #PF: supervisor write access in kernel mode [ 4570.696728] #PF: error_code(0x0002) - not-present page [ 4570.697401] PGD 0 P4D 0 [ 4570.697716] Oops: 0002 [#1] PREEMPT SMP NOPTI [ 4570.698228] CPU: 0 PID: 0 Comm: swapper/0 Not tainted 5.16.0-rc4+ #111 [ 4570.699013] Hardware name: Alibaba Cloud Alibaba Cloud ECS, BIOS 8c24b4c 04/0 [ 4570.699933] RIP: 0010:_raw_spin_lock+0x1a/0x30 <...> [ 4570.711446] Call Trace: [ 4570.711746] <IRQ> [ 4570.711992] smc_cdc_tx_handler+0x41/0xc0 [ 4570.712470] smc_wr_tx_tasklet_fn+0x213/0x560 [ 4570.712981] ? smc_cdc_tx_dismisser+0x10/0x10 [ 4570.713489] tasklet_action_common.isra.17+0x66/0x140 [ 4570.714083] __do_softirq+0x123/0x2f4 [ 4570.714521] irq_exit_rcu+0xc4/0xf0 [ 4570.714934] common_interrupt+0xba/0xe0 Though smc_cdc_tx_handler() checked the existence of smc connection, smc_release() may have already dismissed and released the smc socket before smc_cdc_tx_handler() further visits it. smc_cdc_tx_handler() |smc_release() if (!conn) | | |smc_cdc_tx_dismiss_slots() | smc_cdc_tx_dismisser() | |sock_put(&smc->sk) <- last sock_put, | smc_sock freed bh_lock_sock(&smc->sk) (panic) | To make sure we won't receive any CDC messages after we free the smc_sock, add a refcount on the smc_connection for inflight CDC message(posted to the QP but haven't received related CQE), and don't release the smc_connection until all the inflight CDC messages haven been done, for both success or failed ones. Using refcount on CDC messages brings another problem: when the link is going to be destroyed, smcr_link_clear() will reset the QP, which then remove all the pending CQEs related to the QP in the CQ. To make sure all the CQEs will always come back so the refcount on the smc_connection can always reach 0, smc_ib_modify_qp_reset() was replaced by smc_ib_modify_qp_error(). And remove the timeout in smc_wr_tx_wait_no_pending_sends() since we need to wait for all pending WQEs done, or we may encounter use-after- free when handling CQEs. For IB device removal routine, we need to wait for all the QPs on that device been destroyed before we can destroy CQs on the device, or the refcount on smc_connection won't reach 0 and smc_sock cannot be released.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47011 In the Linux kernel, the following vulnerability has been resolved: mm: memcontrol: slab: fix obtain a reference to a freeing memcg Patch series "Use obj_cgroup APIs to charge kmem pages", v5. Since Roman's series "The new cgroup slab memory controller" applied. All slab objects are charged with the new APIs of obj_cgroup. The new APIs introduce a struct obj_cgroup to charge slab objects. It prevents long-living objects from pinning the original memory cgroup in the memory. But there are still some corner objects (e.g. allocations larger than order-1 page on SLUB) which are not charged with the new APIs. Those objects (include the pages which are allocated from buddy allocator directly) are charged as kmem pages which still hold a reference to the memory cgroup. E.g. We know that the kernel stack is charged as kmem pages because the size of the kernel stack can be greater than 2 pages (e.g. 16KB on x86_64 or arm64). If we create a thread (suppose the thread stack is charged to memory cgroup A) and then move it from memory cgroup A to memory cgroup B. Because the kernel stack of the thread hold a reference to the memory cgroup A. The thread can pin the memory cgroup A in the memory even if we remove the cgroup A. If we want to see this scenario by using the following script. We can see that the system has added 500 dying cgroups (This is not a real world issue, just a script to show that the large kmallocs are charged as kmem pages which can pin the memory cgroup in the memory). #!/bin/bash cat /proc/cgroups | grep memory cd /sys/fs/cgroup/memory echo 1 > memory.move_charge_at_immigrate for i in range{1..500} do mkdir kmem_test echo $$ > kmem_test/cgroup.procs sleep 3600 & echo $$ > cgroup.procs echo `cat kmem_test/cgroup.procs` > cgroup.procs rmdir kmem_test done cat /proc/cgroups | grep memory This patchset aims to make those kmem pages to drop the reference to memory cgroup by using the APIs of obj_cgroup. Finally, we can see that the number of the dying cgroups will not increase if we run the above test script. This patch (of 7): The rcu_read_lock/unlock only can guarantee that the memcg will not be freed, but it cannot guarantee the success of css_get (which is in the refill_stock when cached memcg changed) to memcg. rcu_read_lock() memcg = obj_cgroup_memcg(old) __memcg_kmem_uncharge(memcg) refill_stock(memcg) if (stock->cached != memcg) // css_get can change the ref counter from 0 back to 1. css_get(&memcg->css) rcu_read_unlock() This fix is very like the commit: eefbfa7fd678 ("mm: memcg/slab: fix use after free in obj_cgroup_charge") Fix this by holding a reference to the memcg which is passed to the __memcg_kmem_uncharge() before calling __memcg_kmem_uncharge().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47103 In the Linux kernel, the following vulnerability has been resolved: inet: fully convert sk->sk_rx_dst to RCU rules syzbot reported various issues around early demux, one being included in this changelog [1] sk->sk_rx_dst is using RCU protection without clearly documenting it. And following sequences in tcp_v4_do_rcv()/tcp_v6_do_rcv() are not following standard RCU rules. [a] dst_release(dst); [b] sk->sk_rx_dst = NULL; They look wrong because a delete operation of RCU protected pointer is supposed to clear the pointer before the call_rcu()/synchronize_rcu() guarding actual memory freeing. In some cases indeed, dst could be freed before [b] is done. We could cheat by clearing sk_rx_dst before calling dst_release(), but this seems the right time to stick to standard RCU annotations and debugging facilities. [1] BUG: KASAN: use-after-free in dst_check include/net/dst.h:470 [inline] BUG: KASAN: use-after-free in tcp_v4_early_demux+0x95b/0x960 net/ipv4/tcp_ipv4.c:1792 Read of size 2 at addr ffff88807f1cb73a by task syz-executor.5/9204 CPU: 0 PID: 9204 Comm: syz-executor.5 Not tainted 5.16.0-rc5-syzkaller #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 01/01/2011 Call Trace: <TASK> __dump_stack lib/dump_stack.c:88 [inline] dump_stack_lvl+0xcd/0x134 lib/dump_stack.c:106 print_address_description.constprop.0.cold+0x8d/0x320 mm/kasan/report.c:247 __kasan_report mm/kasan/report.c:433 [inline] kasan_report.cold+0x83/0xdf mm/kasan/report.c:450 dst_check include/net/dst.h:470 [inline] tcp_v4_early_demux+0x95b/0x960 net/ipv4/tcp_ipv4.c:1792 ip_rcv_finish_core.constprop.0+0x15de/0x1e80 net/ipv4/ip_input.c:340 ip_list_rcv_finish.constprop.0+0x1b2/0x6e0 net/ipv4/ip_input.c:583 ip_sublist_rcv net/ipv4/ip_input.c:609 [inline] ip_list_rcv+0x34e/0x490 net/ipv4/ip_input.c:644 __netif_receive_skb_list_ptype net/core/dev.c:5508 [inline] __netif_receive_skb_list_core+0x549/0x8e0 net/core/dev.c:5556 __netif_receive_skb_list net/core/dev.c:5608 [inline] netif_receive_skb_list_internal+0x75e/0xd80 net/core/dev.c:5699 gro_normal_list net/core/dev.c:5853 [inline] gro_normal_list net/core/dev.c:5849 [inline] napi_complete_done+0x1f1/0x880 net/core/dev.c:6590 virtqueue_napi_complete drivers/net/virtio_net.c:339 [inline] virtnet_poll+0xca2/0x11b0 drivers/net/virtio_net.c:1557 __napi_poll+0xaf/0x440 net/core/dev.c:7023 napi_poll net/core/dev.c:7090 [inline] net_rx_action+0x801/0xb40 net/core/dev.c:7177 __do_softirq+0x29b/0x9c2 kernel/softirq.c:558 invoke_softirq kernel/softirq.c:432 [inline] __irq_exit_rcu+0x123/0x180 kernel/softirq.c:637 irq_exit_rcu+0x5/0x20 kernel/softirq.c:649 common_interrupt+0x52/0xc0 arch/x86/kernel/irq.c:240 asm_common_interrupt+0x1e/0x40 arch/x86/include/asm/idtentry.h:629 RIP: 0033:0x7f5e972bfd57 Code: 39 d1 73 14 0f 1f 80 00 00 00 00 48 8b 50 f8 48 83 e8 08 48 39 ca 77 f3 48 39 c3 73 3e 48 89 13 48 8b 50 f8 48 89 38 49 8b 0e <48> 8b 3e 48 83 c3 08 48 83 c6 08 eb bc 48 39 d1 72 9e 48 39 d0 73 RSP: 002b:00007fff8a413210 EFLAGS: 00000283 RAX: 00007f5e97108990 RBX: 00007f5e97108338 RCX: ffffffff81d3aa45 RDX: ffffffff81d3aa45 RSI: 00007f5e97108340 RDI: ffffffff81d3aa45 RBP: 00007f5e97107eb8 R08: 00007f5e97108d88 R09: 0000000093c2e8d9 R10: 0000000000000000 R11: 0000000000000000 R12: 00007f5e97107eb0 R13: 00007f5e97108338 R14: 00007f5e97107ea8 R15: 0000000000000019 </TASK> Allocated by task 13: kasan_save_stack+0x1e/0x50 mm/kasan/common.c:38 kasan_set_track mm/kasan/common.c:46 [inline] set_alloc_info mm/kasan/common.c:434 [inline] __kasan_slab_alloc+0x90/0xc0 mm/kasan/common.c:467 kasan_slab_alloc include/linux/kasan.h:259 [inline] slab_post_alloc_hook mm/slab.h:519 [inline] slab_alloc_node mm/slub.c:3234 [inline] slab_alloc mm/slub.c:3242 [inline] kmem_cache_alloc+0x202/0x3a0 mm/slub.c:3247 dst_alloc+0x146/0x1f0 net/core/dst.c:92 rt_dst_alloc+0x73/0x430 net/ipv4/route.c:1613 ip_route_input_slow+0x1817/0x3a20 net/ipv4/route.c:234 ---truncated---

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47178 In the Linux kernel, the following vulnerability has been resolved: scsi: target: core: Avoid smp_processor_id() in preemptible code The BUG message "BUG: using smp_processor_id() in preemptible [00000000] code" was observed for TCMU devices with kernel config DEBUG_PREEMPT. The message was observed when blktests block/005 was run on TCMU devices with fileio backend or user:zbc backend [1]. The commit 1130b499b4a7 ("scsi: target: tcm_loop: Use LIO wq cmd submission helper") triggered the symptom. The commit modified work queue to handle commands and changed 'current->nr_cpu_allowed' at smp_processor_id() call. The message was also observed at system shutdown when TCMU devices were not cleaned up [2]. The function smp_processor_id() was called in SCSI host work queue for abort handling, and triggered the BUG message. This symptom was observed regardless of the commit 1130b499b4a7 ("scsi: target: tcm_loop: Use LIO wq cmd submission helper"). To avoid the preemptible code check at smp_processor_id(), get CPU ID with raw_smp_processor_id() instead. The CPU ID is used for performance improvement then thread move to other CPU will not affect the code. [1] [ 56.468103] run blktests block/005 at 2021-05-12 14:16:38 [ 57.369473] check_preemption_disabled: 85 callbacks suppressed [ 57.369480] BUG: using smp_processor_id() in preemptible [00000000] code: fio/1511 [ 57.369506] BUG: using smp_processor_id() in preemptible [00000000] code: fio/1510 [ 57.369512] BUG: using smp_processor_id() in preemptible [00000000] code: fio/1506 [ 57.369552] caller is __target_init_cmd+0x157/0x170 [target_core_mod] [ 57.369606] CPU: 4 PID: 1506 Comm: fio Not tainted 5.13.0-rc1+ #34 [ 57.369613] Hardware name: System manufacturer System Product Name/PRIME Z270-A, BIOS 1302 03/15/2018 [ 57.369617] Call Trace: [ 57.369621] BUG: using smp_processor_id() in preemptible [00000000] code: fio/1507 [ 57.369628] dump_stack+0x6d/0x89 [ 57.369642] check_preemption_disabled+0xc8/0xd0 [ 57.369628] caller is __target_init_cmd+0x157/0x170 [target_core_mod] [ 57.369655] __target_init_cmd+0x157/0x170 [target_core_mod] [ 57.369695] target_init_cmd+0x76/0x90 [target_core_mod] [ 57.369732] tcm_loop_queuecommand+0x109/0x210 [tcm_loop] [ 57.369744] scsi_queue_rq+0x38e/0xc40 [ 57.369761] __blk_mq_try_issue_directly+0x109/0x1c0 [ 57.369779] blk_mq_try_issue_directly+0x43/0x90 [ 57.369790] blk_mq_submit_bio+0x4e5/0x5d0 [ 57.369812] submit_bio_noacct+0x46e/0x4e0 [ 57.369830] __blkdev_direct_IO_simple+0x1a3/0x2d0 [ 57.369859] ? set_init_blocksize.isra.0+0x60/0x60 [ 57.369880] generic_file_read_iter+0x89/0x160 [ 57.369898] blkdev_read_iter+0x44/0x60 [ 57.369906] new_sync_read+0x102/0x170 [ 57.369929] vfs_read+0xd4/0x160 [ 57.369941] __x64_sys_pread64+0x6e/0xa0 [ 57.369946] ? lockdep_hardirqs_on+0x79/0x100 [ 57.369958] do_syscall_64+0x3a/0x70 [ 57.369965] entry_SYSCALL_64_after_hwframe+0x44/0xae [ 57.369973] RIP: 0033:0x7f7ed4c1399f [ 57.369979] Code: 08 89 3c 24 48 89 4c 24 18 e8 7d f3 ff ff 4c 8b 54 24 18 48 8b 54 24 10 41 89 c0 48 8b 74 24 08 8b 3c 24 b8 11 00 00 00 0f 05 <48> 3d 00 f0 ff ff 77 31 44 89 c7 48 89 04 24 e8 cd f3 ff ff 48 8b [ 57.369983] RSP: 002b:00007ffd7918c580 EFLAGS: 00000293 ORIG_RAX: 0000000000000011 [ 57.369990] RAX: ffffffffffffffda RBX: 00000000015b4540 RCX: 00007f7ed4c1399f [ 57.369993] RDX: 0000000000001000 RSI: 00000000015de000 RDI: 0000000000000009 [ 57.369996] RBP: 00000000015b4540 R08: 0000000000000000 R09: 0000000000000001 [ 57.369999] R10: 0000000000e5c000 R11: 0000000000000293 R12: 00007f7eb5269a70 [ 57.370002] R13: 0000000000000000 R14: 0000000000001000 R15: 00000000015b4568 [ 57.370031] CPU: 7 PID: 1507 Comm: fio Not tainted 5.13.0-rc1+ #34 [ 57.370036] Hardware name: System manufacturer System Product Name/PRIME Z270-A, BIOS 1302 03/15/2018 [ 57.370039] Call Trace: [ 57.370045] dump_stack+0x6d/0x89 [ 57.370056] ch ---truncated---

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47226 In the Linux kernel, the following vulnerability has been resolved: x86/fpu: Invalidate FPU state after a failed XRSTOR from a user buffer Both Intel and AMD consider it to be architecturally valid for XRSTOR to fail with #PF but nonetheless change the register state. The actual conditions under which this might occur are unclear [1], but it seems plausible that this might be triggered if one sibling thread unmaps a page and invalidates the shared TLB while another sibling thread is executing XRSTOR on the page in question. __fpu__restore_sig() can execute XRSTOR while the hardware registers are preserved on behalf of a different victim task (using the fpu_fpregs_owner_ctx mechanism), and, in theory, XRSTOR could fail but modify the registers. If this happens, then there is a window in which __fpu__restore_sig() could schedule out and the victim task could schedule back in without reloading its own FPU registers. This would result in part of the FPU state that __fpu__restore_sig() was attempting to load leaking into the victim task's user-visible state. Invalidate preserved FPU registers on XRSTOR failure to prevent this situation from corrupting any state. [1] Frequent readers of the errata lists might imagine "complex microarchitectural conditions".

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47308 In the Linux kernel, the following vulnerability has been resolved: scsi: libfc: Fix array index out of bound exception Fix array index out of bound exception in fc_rport_prli_resp().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47313 In the Linux kernel, the following vulnerability has been resolved: cpufreq: CPPC: Fix potential memleak in cppc_cpufreq_cpu_init It's a classic example of memleak, we allocate something, we fail and never free the resources. Make sure we free all resources on policy ->init() failures.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47483 In the Linux kernel, the following vulnerability has been resolved: regmap: Fix possible double-free in regcache_rbtree_exit() In regcache_rbtree_insert_to_block(), when 'present' realloc failed, the 'blk' which is supposed to assign to 'rbnode->block' will be freed, so 'rbnode->block' points a freed memory, in the error handling path of regcache_rbtree_init(), 'rbnode->block' will be freed again in regcache_rbtree_exit(), KASAN will report double-free as follows: BUG: KASAN: double-free or invalid-free in kfree+0xce/0x390 Call Trace: slab_free_freelist_hook+0x10d/0x240 kfree+0xce/0x390 regcache_rbtree_exit+0x15d/0x1a0 regcache_rbtree_init+0x224/0x2c0 regcache_init+0x88d/0x1310 __regmap_init+0x3151/0x4a80 __devm_regmap_init+0x7d/0x100 madera_spi_probe+0x10f/0x333 [madera_spi] spi_probe+0x183/0x210 really_probe+0x285/0xc30 To fix this, moving up the assignment of rbnode->block to immediately after the reallocation has succeeded so that the data structure stays valid even if the second reallocation fails.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47517 In the Linux kernel, the following vulnerability has been resolved: ethtool: do not perform operations on net devices being unregistered There is a short period between a net device starts to be unregistered and when it is actually gone. In that time frame ethtool operations could still be performed, which might end up in unwanted or undefined behaviours[1]. Do not allow ethtool operations after a net device starts its unregistration. This patch targets the netlink part as the ioctl one isn't affected: the reference to the net device is taken and the operation is executed within an rtnl lock section and the net device won't be found after unregister. [1] For example adding Tx queues after unregister ends up in NULL pointer exceptions and UaFs, such as: BUG: KASAN: use-after-free in kobject_get+0x14/0x90 Read of size 1 at addr ffff88801961248c by task ethtool/755 CPU: 0 PID: 755 Comm: ethtool Not tainted 5.15.0-rc6+ #778 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.14.0-4.fc34 04/014 Call Trace: dump_stack_lvl+0x57/0x72 print_address_description.constprop.0+0x1f/0x140 kasan_report.cold+0x7f/0x11b kobject_get+0x14/0x90 kobject_add_internal+0x3d1/0x450 kobject_init_and_add+0xba/0xf0 netdev_queue_update_kobjects+0xcf/0x200 netif_set_real_num_tx_queues+0xb4/0x310 veth_set_channels+0x1c3/0x550 ethnl_set_channels+0x524/0x610

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47639 In the Linux kernel, the following vulnerability has been resolved: KVM: x86/mmu: Zap _all_ roots when unmapping gfn range in TDP MMU Zap both valid and invalid roots when zapping/unmapping a gfn range, as KVM must ensure it holds no references to the freed page after returning from the unmap operation. Most notably, the TDP MMU doesn't zap invalid roots in mmu_notifier callbacks. This leads to use-after-free and other issues if the mmu_notifier runs to completion while an invalid root zapper yields as KVM fails to honor the requirement that there must be _no_ references to the page after the mmu_notifier returns. The bug is most easily reproduced by hacking KVM to cause a collision between set_nx_huge_pages() and kvm_mmu_notifier_release(), but the bug exists between kvm_mmu_notifier_invalidate_range_start() and memslot updates as well. Invalidating a root ensures pages aren't accessible by the guest, and KVM won't read or write page data itself, but KVM will trigger e.g. kvm_set_pfn_dirty() when zapping SPTEs, and thus completing a zap of an invalid root _after_ the mmu_notifier returns is fatal. WARNING: CPU: 24 PID: 1496 at arch/x86/kvm/../../../virt/kvm/kvm_main.c:173 [kvm] RIP: 0010:kvm_is_zone_device_pfn+0x96/0xa0 [kvm] Call Trace: <TASK> kvm_set_pfn_dirty+0xa8/0xe0 [kvm] __handle_changed_spte+0x2ab/0x5e0 [kvm] __handle_changed_spte+0x2ab/0x5e0 [kvm] __handle_changed_spte+0x2ab/0x5e0 [kvm] zap_gfn_range+0x1f3/0x310 [kvm] kvm_tdp_mmu_zap_invalidated_roots+0x50/0x90 [kvm] kvm_mmu_zap_all_fast+0x177/0x1a0 [kvm] set_nx_huge_pages+0xb4/0x190 [kvm] param_attr_store+0x70/0x100 module_attr_store+0x19/0x30 kernfs_fop_write_iter+0x119/0x1b0 new_sync_write+0x11c/0x1b0 vfs_write+0x1cc/0x270 ksys_write+0x5f/0xe0 do_syscall_64+0x38/0xc0 entry_SYSCALL_64_after_hwframe+0x44/0xae </TASK>

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2021-47657 In the Linux kernel, the following vulnerability has been resolved: drm/virtio: Ensure that objs is not NULL in virtio_gpu_array_put_free() If virtio_gpu_object_shmem_init() fails (e.g. due to fault injection, as it happened in the bug report by syzbot), virtio_gpu_array_put_free() could be called with objs equal to NULL. Ensure that objs is not NULL in virtio_gpu_array_put_free(), or otherwise return from the function.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-3114 An issue was discovered in the Linux kernel through 5.16-rc6. imx_register_uart_clocks in drivers/clk/imx/clk.c lacks check of the return value of kcalloc() and will cause the null pointer dereference.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-24999 qs before 6.10.3, as used in Express before 4.17.3 and other products, allows attackers to cause a Node process hang for an Express application because an __ proto__ key can be used. In many typical Express use cases, an unauthenticated remote attacker can place the attack payload in the query string of the URL that is used to visit the application, such as a[__proto__]=b&a[__proto__]&a[length]=100000000. The fix was backported to qs 6.9.7, 6.8.3, 6.7.3, 6.6.1, 6.5.3, 6.4.1, 6.3.3, and 6.2.4 (and therefore Express 4.17.3, which has "deps: qs@6.9.7" in its release description, is not vulnerable).

cloudera-ai-rag-studio

CVE-2022-39253 Git is an open source, scalable, distributed revision control system. Versions prior to 2.30.6, 2.31.5, 2.32.4, 2.33.5, 2.34.5, 2.35.5, 2.36.3, and 2.37.4 are subject to exposure of sensitive information to a malicious actor. When performing a local clone (where the source and target of the clone are on the same volume), Git copies the contents of the source's `$GIT_DIR/objects` directory into the destination by either creating hardlinks to the source contents, or copying them (if hardlinks are disabled via `--no-hardlinks`). A malicious actor could convince a victim to clone a repository with a symbolic link pointing at sensitive information on the victim's machine. This can be done either by having the victim clone a malicious repository on the same machine, or having them clone a malicious repository embedded as a bare repository via a submodule from any source, provided they clone with the `--recurse-submodules` option. Git does not create symbolic links in the `$GIT_DIR/objects` directory. The problem has been patched in the versions published on 2022-10-18, and backported to v2.30.x. Potential workarounds: Avoid cloning untrusted repositories using the `--local` optimization when on a shared machine, either by passing the `--no-local` option to `git clone` or cloning from a URL that uses the `file://` scheme. Alternatively, avoid cloning repositories from untrusted sources with `--recurse-submodules` or run `git config --global protocol.file.allow user`.

cdsw-s2i-builder-buildah

CVE-2022-41420 nasm v2.16 was discovered to contain a stack overflow in the Ndisasm component

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2022-46456 NASM v2.16 was discovered to contain a global buffer overflow in the component dbgdbg_typevalue at /output/outdbg.c.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2022-48696 In the Linux kernel, the following vulnerability has been resolved: regmap: spi: Reserve space for register address/padding Currently the max_raw_read and max_raw_write limits in regmap_spi struct do not take into account the additional size of the transmitted register address and padding. This may result in exceeding the maximum permitted SPI message size, which could cause undefined behaviour, e.g. data corruption. Fix regmap_get_spi_bus() to properly adjust the above mentioned limits by reserving space for the register address/padding as set in the regmap configuration.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-48904 In the Linux kernel, the following vulnerability has been resolved: iommu/amd: Fix I/O page table memory leak The current logic updates the I/O page table mode for the domain before calling the logic to free memory used for the page table. This results in IOMMU page table memory leak, and can be observed when launching VM w/ pass-through devices. Fix by freeing the memory used for page table before updating the mode.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-48911 In the Linux kernel, the following vulnerability has been resolved: netfilter: nf_queue: fix possible use-after-free Eric Dumazet says: The sock_hold() side seems suspect, because there is no guarantee that sk_refcnt is not already 0. On failure, we cannot queue the packet and need to indicate an error. The packet will be dropped by the caller. v2: split skb prefetch hunk into separate change

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-48915 In the Linux kernel, the following vulnerability has been resolved: thermal: core: Fix TZ_GET_TRIP NULL pointer dereference Do not call get_trip_hyst() from thermal_genl_cmd_tz_get_trip() if the thermal zone does not define one.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-48943 In the Linux kernel, the following vulnerability has been resolved: KVM: x86/mmu: make apf token non-zero to fix bug In current async pagefault logic, when a page is ready, KVM relies on kvm_arch_can_dequeue_async_page_present() to determine whether to deliver a READY event to the Guest. This function test token value of struct kvm_vcpu_pv_apf_data, which must be reset to zero by Guest kernel when a READY event is finished by Guest. If value is zero meaning that a READY event is done, so the KVM can deliver another. But the kvm_arch_setup_async_pf() may produce a valid token with zero value, which is confused with previous mention and may lead the loss of this READY event. This bug may cause task blocked forever in Guest: INFO: task stress:7532 blocked for more than 1254 seconds. Not tainted 5.10.0 #16 "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. task:stress state:D stack: 0 pid: 7532 ppid: 1409 flags:0x00000080 Call Trace: __schedule+0x1e7/0x650 schedule+0x46/0xb0 kvm_async_pf_task_wait_schedule+0xad/0xe0 ? exit_to_user_mode_prepare+0x60/0x70 __kvm_handle_async_pf+0x4f/0xb0 ? asm_exc_page_fault+0x8/0x30 exc_page_fault+0x6f/0x110 ? asm_exc_page_fault+0x8/0x30 asm_exc_page_fault+0x1e/0x30 RIP: 0033:0x402d00 RSP: 002b:00007ffd31912500 EFLAGS: 00010206 RAX: 0000000000071000 RBX: ffffffffffffffff RCX: 00000000021a32b0 RDX: 000000000007d011 RSI: 000000000007d000 RDI: 00000000021262b0 RBP: 00000000021262b0 R08: 0000000000000003 R09: 0000000000000086 R10: 00000000000000eb R11: 00007fefbdf2baa0 R12: 0000000000000000 R13: 0000000000000002 R14: 000000000007d000 R15: 0000000000001000

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-48988 In the Linux kernel, the following vulnerability has been resolved: memcg: fix possible use-after-free in memcg_write_event_control() memcg_write_event_control() accesses the dentry->d_name of the specified control fd to route the write call. As a cgroup interface file can't be renamed, it's safe to access d_name as long as the specified file is a regular cgroup file. Also, as these cgroup interface files can't be removed before the directory, it's safe to access the parent too. Prior to 347c4a874710 ("memcg: remove cgroup_event->cft"), there was a call to __file_cft() which verified that the specified file is a regular cgroupfs file before further accesses. The cftype pointer returned from __file_cft() was no longer necessary and the commit inadvertently dropped the file type check with it allowing any file to slip through. With the invarients broken, the d_name and parent accesses can now race against renames and removals of arbitrary files and cause use-after-free's. Fix the bug by resurrecting the file type check in __file_cft(). Now that cgroupfs is implemented through kernfs, checking the file operations needs to go through a layer of indirection. Instead, let's check the superblock and dentry type.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49060 In the Linux kernel, the following vulnerability has been resolved: net/smc: Fix NULL pointer dereference in smc_pnet_find_ib() dev_name() was called with dev.parent as argument but without to NULL-check it before. Solve this by checking the pointer before the call to dev_name().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49127 In the Linux kernel, the following vulnerability has been resolved: ref_tracker: implement use-after-free detection Whenever ref_tracker_dir_init() is called, mark the struct ref_tracker_dir as dead. Test the dead status from ref_tracker_alloc() and ref_tracker_free() This should detect buggy dev_put()/dev_hold() happening too late in netdevice dismantle process.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49129 In the Linux kernel, the following vulnerability has been resolved: mt76: mt7921: fix crash when startup fails. If the nic fails to start, it is possible that the reset_work has already been scheduled. Ensure the work item is canceled so we do not have use-after-free crash in case cleanup is called before the work item is executed. This fixes crash on my x86_64 apu2 when mt7921k radio fails to work. Radio still fails, but OS does not crash.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49130 In the Linux kernel, the following vulnerability has been resolved: ath11k: mhi: use mhi_sync_power_up() If amss.bin was missing ath11k would crash during 'rmmod ath11k_pci'. The reason for that was that we were using mhi_async_power_up() which does not check any errors. But mhi_sync_power_up() on the other hand does check for errors so let's use that to fix the crash. I was not able to find a reason why an async version was used. ath11k_mhi_start() (which enables state ATH11K_MHI_POWER_ON) is called from ath11k_hif_power_up(), which can sleep. So sync version should be safe to use here. [ 145.569731] general protection fault, probably for non-canonical address 0xdffffc0000000000: 0000 [#1] PREEMPT SMP DEBUG_PAGEALLOC KASAN PTI [ 145.569789] KASAN: null-ptr-deref in range [0x0000000000000000-0x0000000000000007] [ 145.569843] CPU: 2 PID: 1628 Comm: rmmod Kdump: loaded Tainted: G W 5.16.0-wt-ath+ #567 [ 145.569898] Hardware name: Intel(R) Client Systems NUC8i7HVK/NUC8i7HVB, BIOS HNKBLi70.86A.0067.2021.0528.1339 05/28/2021 [ 145.569956] RIP: 0010:ath11k_hal_srng_access_begin+0xb5/0x2b0 [ath11k] [ 145.570028] Code: df 48 89 fa 48 c1 ea 03 80 3c 02 00 0f 85 ec 01 00 00 48 8b ab a8 00 00 00 48 b8 00 00 00 00 00 fc ff df 48 89 ea 48 c1 ea 03 <0f> b6 14 02 48 89 e8 83 e0 07 83 c0 03 45 85 ed 75 48 38 d0 7c 08 [ 145.570089] RSP: 0018:ffffc900025d7ac0 EFLAGS: 00010246 [ 145.570144] RAX: dffffc0000000000 RBX: ffff88814fca2dd8 RCX: 1ffffffff50cb455 [ 145.570196] RDX: 0000000000000000 RSI: ffff88814fca2dd8 RDI: ffff88814fca2e80 [ 145.570252] RBP: 0000000000000000 R08: 0000000000000000 R09: ffffffffa8659497 [ 145.570329] R10: fffffbfff50cb292 R11: 0000000000000001 R12: ffff88814fca0000 [ 145.570410] R13: 0000000000000000 R14: ffff88814fca2798 R15: ffff88814fca2dd8 [ 145.570465] FS: 00007fa399988540(0000) GS:ffff888233e00000(0000) knlGS:0000000000000000 [ 145.570519] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 145.570571] CR2: 00007fa399b51421 CR3: 0000000137898002 CR4: 00000000003706e0 [ 145.570623] Call Trace: [ 145.570675] <TASK> [ 145.570727] ? ath11k_ce_tx_process_cb+0x34b/0x860 [ath11k] [ 145.570797] ath11k_ce_tx_process_cb+0x356/0x860 [ath11k] [ 145.570864] ? tasklet_init+0x150/0x150 [ 145.570919] ? ath11k_ce_alloc_pipes+0x280/0x280 [ath11k] [ 145.570986] ? tasklet_clear_sched+0x42/0xe0 [ 145.571042] ? tasklet_kill+0xe9/0x1b0 [ 145.571095] ? tasklet_clear_sched+0xe0/0xe0 [ 145.571148] ? irq_has_action+0x120/0x120 [ 145.571202] ath11k_ce_cleanup_pipes+0x45a/0x580 [ath11k] [ 145.571270] ? ath11k_pci_stop+0x10e/0x170 [ath11k_pci] [ 145.571345] ath11k_core_stop+0x8a/0xc0 [ath11k] [ 145.571434] ath11k_core_deinit+0x9e/0x150 [ath11k] [ 145.571499] ath11k_pci_remove+0xd2/0x260 [ath11k_pci] [ 145.571553] pci_device_remove+0x9a/0x1c0 [ 145.571605] __device_release_driver+0x332/0x660 [ 145.571659] driver_detach+0x1e7/0x2c0 [ 145.571712] bus_remove_driver+0xe2/0x2d0 [ 145.571772] pci_unregister_driver+0x21/0x250 [ 145.571826] __do_sys_delete_module+0x30a/0x4b0 [ 145.571879] ? free_module+0xac0/0xac0 [ 145.571933] ? lockdep_hardirqs_on_prepare.part.0+0x18c/0x370 [ 145.571986] ? syscall_enter_from_user_mode+0x1d/0x50 [ 145.572039] ? lockdep_hardirqs_on+0x79/0x100 [ 145.572097] do_syscall_64+0x3b/0x90 [ 145.572153] entry_SYSCALL_64_after_hwframe+0x44/0xae Tested-on: WCN6855 hw2.0 PCI WLAN.HSP.1.1-03003-QCAHSPSWPL_V1_V2_SILICONZ_LITE-2

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49136 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: hci_sync: Fix queuing commands when HCI_UNREGISTER is set hci_cmd_sync_queue shall return an error if HCI_UNREGISTER flag has been set as that means hci_unregister_dev has been called so it will likely cause a uaf after the timeout as the hdev will be freed.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49167 In the Linux kernel, the following vulnerability has been resolved: btrfs: do not double complete bio on errors during compressed reads I hit some weird panics while fixing up the error handling from btrfs_lookup_bio_sums(). Turns out the compression path will complete the bio we use if we set up any of the compression bios and then return an error, and then btrfs_submit_data_bio() will also call bio_endio() on the bio. Fix this by making btrfs_submit_compressed_read() responsible for calling bio_endio() on the bio if there are any errors. Currently it was only doing it if we created the compression bios, otherwise it was depending on btrfs_submit_data_bio() to do the right thing. This creates the above problem, so fix up btrfs_submit_compressed_read() to always call bio_endio() in case of an error, and then simply return from btrfs_submit_data_bio() if we had to call btrfs_submit_compressed_read().

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49234 In the Linux kernel, the following vulnerability has been resolved: net: dsa: Avoid cross-chip syncing of VLAN filtering Changes to VLAN filtering are not applicable to cross-chip notifications. On a system like this: .-----. .-----. .-----. | sw1 +---+ sw2 +---+ sw3 | '-1-2-' '-1-2-' '-1-2-' Before this change, upon sw1p1 leaving a bridge, a call to dsa_port_vlan_filtering would also be made to sw2p1 and sw3p1. In this scenario: .---------. .-----. .-----. | sw1 +---+ sw2 +---+ sw3 | '-1-2-3-4-' '-1-2-' '-1-2-' When sw1p4 would leave a bridge, dsa_port_vlan_filtering would be called for sw2 and sw3 with a non-existing port - leading to array out-of-bounds accesses and crashes on mv88e6xxx.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49235 In the Linux kernel, the following vulnerability has been resolved: ath9k_htc: fix uninit value bugs Syzbot reported 2 KMSAN bugs in ath9k. All of them are caused by missing field initialization. In htc_connect_service() svc_meta_len and pad are not initialized. Based on code it looks like in current skb there is no service data, so simply initialize svc_meta_len to 0. htc_issue_send() does not initialize htc_frame_hdr::control array. Based on firmware code, it will initialize it by itself, so simply zero whole array to make KMSAN happy Fail logs: BUG: KMSAN: kernel-usb-infoleak in usb_submit_urb+0x6c1/0x2aa0 drivers/usb/core/urb.c:430 usb_submit_urb+0x6c1/0x2aa0 drivers/usb/core/urb.c:430 hif_usb_send_regout drivers/net/wireless/ath/ath9k/hif_usb.c:127 [inline] hif_usb_send+0x5f0/0x16f0 drivers/net/wireless/ath/ath9k/hif_usb.c:479 htc_issue_send drivers/net/wireless/ath/ath9k/htc_hst.c:34 [inline] htc_connect_service+0x143e/0x1960 drivers/net/wireless/ath/ath9k/htc_hst.c:275 ... Uninit was created at: slab_post_alloc_hook mm/slab.h:524 [inline] slab_alloc_node mm/slub.c:3251 [inline] __kmalloc_node_track_caller+0xe0c/0x1510 mm/slub.c:4974 kmalloc_reserve net/core/skbuff.c:354 [inline] __alloc_skb+0x545/0xf90 net/core/skbuff.c:426 alloc_skb include/linux/skbuff.h:1126 [inline] htc_connect_service+0x1029/0x1960 drivers/net/wireless/ath/ath9k/htc_hst.c:258 ... Bytes 4-7 of 18 are uninitialized Memory access of size 18 starts at ffff888027377e00 BUG: KMSAN: kernel-usb-infoleak in usb_submit_urb+0x6c1/0x2aa0 drivers/usb/core/urb.c:430 usb_submit_urb+0x6c1/0x2aa0 drivers/usb/core/urb.c:430 hif_usb_send_regout drivers/net/wireless/ath/ath9k/hif_usb.c:127 [inline] hif_usb_send+0x5f0/0x16f0 drivers/net/wireless/ath/ath9k/hif_usb.c:479 htc_issue_send drivers/net/wireless/ath/ath9k/htc_hst.c:34 [inline] htc_connect_service+0x143e/0x1960 drivers/net/wireless/ath/ath9k/htc_hst.c:275 ... Uninit was created at: slab_post_alloc_hook mm/slab.h:524 [inline] slab_alloc_node mm/slub.c:3251 [inline] __kmalloc_node_track_caller+0xe0c/0x1510 mm/slub.c:4974 kmalloc_reserve net/core/skbuff.c:354 [inline] __alloc_skb+0x545/0xf90 net/core/skbuff.c:426 alloc_skb include/linux/skbuff.h:1126 [inline] htc_connect_service+0x1029/0x1960 drivers/net/wireless/ath/ath9k/htc_hst.c:258 ... Bytes 16-17 of 18 are uninitialized Memory access of size 18 starts at ffff888027377e00

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49288 In the Linux kernel, the following vulnerability has been resolved: ALSA: pcm: Fix races among concurrent prealloc proc writes We have no protection against concurrent PCM buffer preallocation changes via proc files, and it may potentially lead to UAF or some weird problem. This patch applies the PCM open_mutex to the proc write operation for avoiding the racy proc writes and the PCM stream open (and further operations).

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49294 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Check if modulo is 0 before dividing. [How & Why] If a value of 0 is read, then this will cause a divide-by-0 panic.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49316 In the Linux kernel, the following vulnerability has been resolved: NFSv4: Don't hold the layoutget locks across multiple RPC calls When doing layoutget as part of the open() compound, we have to be careful to release the layout locks before we can call any further RPC calls, such as setattr(). The reason is that those calls could trigger a recall, which could deadlock.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49323 In the Linux kernel, the following vulnerability has been resolved: iommu/arm-smmu: fix possible null-ptr-deref in arm_smmu_device_probe() It will cause null-ptr-deref when using 'res', if platform_get_resource() returns NULL, so move using 'res' after devm_ioremap_resource() that will check it to avoid null-ptr-deref. And use devm_platform_get_and_ioremap_resource() to simplify code.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49349 In the Linux kernel, the following vulnerability has been resolved: ext4: fix use-after-free in ext4_rename_dir_prepare We got issue as follows: EXT4-fs (loop0): mounted filesystem without journal. Opts: ,errors=continue ext4_get_first_dir_block: bh->b_data=0xffff88810bee6000 len=34478 ext4_get_first_dir_block: *parent_de=0xffff88810beee6ae bh->b_data=0xffff88810bee6000 ext4_rename_dir_prepare: [1] parent_de=0xffff88810beee6ae ================================================================== BUG: KASAN: use-after-free in ext4_rename_dir_prepare+0x152/0x220 Read of size 4 at addr ffff88810beee6ae by task rep/1895 CPU: 13 PID: 1895 Comm: rep Not tainted 5.10.0+ #241 Call Trace: dump_stack+0xbe/0xf9 print_address_description.constprop.0+0x1e/0x220 kasan_report.cold+0x37/0x7f ext4_rename_dir_prepare+0x152/0x220 ext4_rename+0xf44/0x1ad0 ext4_rename2+0x11c/0x170 vfs_rename+0xa84/0x1440 do_renameat2+0x683/0x8f0 __x64_sys_renameat+0x53/0x60 do_syscall_64+0x33/0x40 entry_SYSCALL_64_after_hwframe+0x44/0xa9 RIP: 0033:0x7f45a6fc41c9 RSP: 002b:00007ffc5a470218 EFLAGS: 00000246 ORIG_RAX: 0000000000000108 RAX: ffffffffffffffda RBX: 0000000000000000 RCX: 00007f45a6fc41c9 RDX: 0000000000000005 RSI: 0000000020000180 RDI: 0000000000000005 RBP: 00007ffc5a470240 R08: 00007ffc5a470160 R09: 0000000020000080 R10: 00000000200001c0 R11: 0000000000000246 R12: 0000000000400bb0 R13: 00007ffc5a470320 R14: 0000000000000000 R15: 0000000000000000 The buggy address belongs to the page: page:00000000440015ce refcount:0 mapcount:0 mapping:0000000000000000 index:0x1 pfn:0x10beee flags: 0x200000000000000() raw: 0200000000000000 ffffea00043ff4c8 ffffea0004325608 0000000000000000 raw: 0000000000000001 0000000000000000 00000000ffffffff 0000000000000000 page dumped because: kasan: bad access detected Memory state around the buggy address: ffff88810beee580: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ffff88810beee600: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff >ffff88810beee680: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ^ ffff88810beee700: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ffff88810beee780: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ================================================================== Disabling lock debugging due to kernel taint ext4_rename_dir_prepare: [2] parent_de->inode=3537895424 ext4_rename_dir_prepare: [3] dir=0xffff888124170140 ext4_rename_dir_prepare: [4] ino=2 ext4_rename_dir_prepare: ent->dir->i_ino=2 parent=-757071872 Reason is first directory entry which 'rec_len' is 34478, then will get illegal parent entry. Now, we do not check directory entry after read directory block in 'ext4_get_first_dir_block'. To solve this issue, check directory entry in 'ext4_get_first_dir_block'. [ Trigger an ext4_error() instead of just warning if the directory is missing a '.' or '..' entry. Also make sure we return an error code if the file system is corrupted. -TYT ]

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49359 In the Linux kernel, the following vulnerability has been resolved: drm/panfrost: Job should reference MMU not file_priv For a while now it's been allowed for a MMU context to outlive it's corresponding panfrost_priv, however the job structure still references panfrost_priv to get hold of the MMU context. If panfrost_priv has been freed this is a use-after-free which I've been able to trigger resulting in a splat. To fix this, drop the reference to panfrost_priv in the job structure and add a direct reference to the MMU structure which is what's actually needed.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49371 In the Linux kernel, the following vulnerability has been resolved: driver core: fix deadlock in __device_attach In __device_attach function, The lock holding logic is as follows: ... __device_attach device_lock(dev) // get lock dev async_schedule_dev(__device_attach_async_helper, dev); // func async_schedule_node async_schedule_node_domain(func) entry = kzalloc(sizeof(struct async_entry), GFP_ATOMIC); /* when fail or work limit, sync to execute func, but __device_attach_async_helper will get lock dev as well, which will lead to A-A deadlock. */ if (!entry || atomic_read(&entry_count) > MAX_WORK) { func; else queue_work_node(node, system_unbound_wq, &entry->work) device_unlock(dev) As shown above, when it is allowed to do async probes, because of out of memory or work limit, async work is not allowed, to do sync execute instead. it will lead to A-A deadlock because of __device_attach_async_helper getting lock dev. To fix the deadlock, move the async_schedule_dev outside device_lock, as we can see, in async_schedule_node_domain, the parameter of queue_work_node is system_unbound_wq, so it can accept concurrent operations. which will also not change the code logic, and will not lead to deadlock.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49374 In the Linux kernel, the following vulnerability has been resolved: tipc: check attribute length for bearer name syzbot reported uninit-value: ===================================================== BUG: KMSAN: uninit-value in string_nocheck lib/vsprintf.c:644 [inline] BUG: KMSAN: uninit-value in string+0x4f9/0x6f0 lib/vsprintf.c:725 string_nocheck lib/vsprintf.c:644 [inline] string+0x4f9/0x6f0 lib/vsprintf.c:725 vsnprintf+0x2222/0x3650 lib/vsprintf.c:2806 vprintk_store+0x537/0x2150 kernel/printk/printk.c:2158 vprintk_emit+0x28b/0xab0 kernel/printk/printk.c:2256 vprintk_default+0x86/0xa0 kernel/printk/printk.c:2283 vprintk+0x15f/0x180 kernel/printk/printk_safe.c:50 _printk+0x18d/0x1cf kernel/printk/printk.c:2293 tipc_enable_bearer net/tipc/bearer.c:371 [inline] __tipc_nl_bearer_enable+0x2022/0x22a0 net/tipc/bearer.c:1033 tipc_nl_bearer_enable+0x6c/0xb0 net/tipc/bearer.c:1042 genl_family_rcv_msg_doit net/netlink/genetlink.c:731 [inline] - Do sanity check the attribute length for TIPC_NLA_BEARER_NAME. - Do not use 'illegal name' in printing message.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49393 In the Linux kernel, the following vulnerability has been resolved: misc: fastrpc: fix list iterator in fastrpc_req_mem_unmap_impl This is another instance of incorrect use of list iterator and checking it for NULL. The list iterator value 'map' will *always* be set and non-NULL by list_for_each_entry(), so it is incorrect to assume that the iterator value will be NULL if the list is empty (in this case, the check 'if (!map) {' will always be false and never exit as expected). To fix the bug, use a new variable 'iter' as the list iterator, while use the original variable 'map' as a dedicated pointer to point to the found element. Without this patch, Kernel crashes with below trace: Unable to handle kernel access to user memory outside uaccess routines at virtual address 0000ffff7fb03750 ... Call trace: fastrpc_map_create+0x70/0x290 [fastrpc] fastrpc_req_mem_map+0xf0/0x2dc [fastrpc] fastrpc_device_ioctl+0x138/0xc60 [fastrpc] __arm64_sys_ioctl+0xa8/0xec invoke_syscall+0x48/0x114 el0_svc_common.constprop.0+0xd4/0xfc do_el0_svc+0x28/0x90 el0_svc+0x3c/0x130 el0t_64_sync_handler+0xa4/0x130 el0t_64_sync+0x18c/0x190 Code: 14000016 f94000a5 eb05029f 54000260 (b94018a6) ---[ end trace 0000000000000000 ]---

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49404 In the Linux kernel, the following vulnerability has been resolved: RDMA/hfi1: Fix potential integer multiplication overflow errors When multiplying of different types, an overflow is possible even when storing the result in a larger type. This is because the conversion is done after the multiplication. So arithmetic overflow and thus in incorrect value is possible. Correct an instance of this in the inter packet delay calculation. Fix by ensuring one of the operands is u64 which will promote the other to u64 as well ensuring no overflow.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49416 In the Linux kernel, the following vulnerability has been resolved: wifi: mac80211: fix use-after-free in chanctx code In ieee80211_vif_use_reserved_context(), when we have an old context and the new context's replace_state is set to IEEE80211_CHANCTX_REPLACE_NONE, we free the old context in ieee80211_vif_use_reserved_reassign(). Therefore, we cannot check the old_ctx anymore, so we should set it to NULL after this point. However, since the new_ctx replace state is clearly not IEEE80211_CHANCTX_REPLACES_OTHER, we're not going to do anything else in this function and can just return to avoid accessing the freed old_ctx.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49492 In the Linux kernel, the following vulnerability has been resolved: nvme-pci: fix a NULL pointer dereference in nvme_alloc_admin_tags In nvme_alloc_admin_tags, the admin_q can be set to an error (typically -ENOMEM) if the blk_mq_init_queue call fails to set up the queue, which is checked immediately after the call. However, when we return the error message up the stack, to nvme_reset_work the error takes us to nvme_remove_dead_ctrl() nvme_dev_disable() nvme_suspend_queue(&dev->queues[0]). Here, we only check that the admin_q is non-NULL, rather than not an error or NULL, and begin quiescing a queue that never existed, leading to bad / NULL pointer dereference.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49536 In the Linux kernel, the following vulnerability has been resolved: scsi: lpfc: Fix SCSI I/O completion and abort handler deadlock During stress I/O tests with 500+ vports, hard LOCKUP call traces are observed. CPU A: native_queued_spin_lock_slowpath+0x192 _raw_spin_lock_irqsave+0x32 lpfc_handle_fcp_err+0x4c6 lpfc_fcp_io_cmd_wqe_cmpl+0x964 lpfc_sli4_fp_handle_cqe+0x266 __lpfc_sli4_process_cq+0x105 __lpfc_sli4_hba_process_cq+0x3c lpfc_cq_poll_hdler+0x16 irq_poll_softirq+0x76 __softirqentry_text_start+0xe4 irq_exit+0xf7 do_IRQ+0x7f CPU B: native_queued_spin_lock_slowpath+0x5b _raw_spin_lock+0x1c lpfc_abort_handler+0x13e scmd_eh_abort_handler+0x85 process_one_work+0x1a7 worker_thread+0x30 kthread+0x112 ret_from_fork+0x1f Diagram of lockup: CPUA CPUB ---- ---- lpfc_cmd->buf_lock phba->hbalock lpfc_cmd->buf_lock phba->hbalock Fix by reordering the taking of the lpfc_cmd->buf_lock and phba->hbalock in lpfc_abort_handler routine so that it tries to take the lpfc_cmd->buf_lock first before phba->hbalock.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49538 In the Linux kernel, the following vulnerability has been resolved: ALSA: jack: Access input_dev under mutex It is possible when using ASoC that input_dev is unregistered while calling snd_jack_report, which causes NULL pointer dereference. In order to prevent this serialize access to input_dev using mutex lock.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49577 In the Linux kernel, the following vulnerability has been resolved: udp: Fix a data-race around sysctl_udp_l3mdev_accept. While reading sysctl_udp_l3mdev_accept, it can be changed concurrently. Thus, we need to add READ_ONCE() to its reader.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49583 In the Linux kernel, the following vulnerability has been resolved: iavf: Fix handling of dummy receive descriptors Fix memory leak caused by not handling dummy receive descriptor properly. iavf_get_rx_buffer now sets the rx_buffer return value for dummy receive descriptors. Without this patch, when the hardware writes a dummy descriptor, iavf would not free the page allocated for the previous receive buffer. This is an unlikely event but can still happen. [Jesse: massaged commit message]

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49615 In the Linux kernel, the following vulnerability has been resolved: ASoC: rt711-sdca: fix kernel NULL pointer dereference when IO error The initial settings will be written before the codec probe function. But, the rt711->component doesn't be assigned yet. If IO error happened during initial settings operations, it will cause the kernel panic. This patch changed component->dev to slave->dev to fix this issue.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49626 In the Linux kernel, the following vulnerability has been resolved: sfc: fix use after free when disabling sriov Use after free is detected by kfence when disabling sriov. What was read after being freed was vf->pci_dev: it was freed from pci_disable_sriov and later read in efx_ef10_sriov_free_vf_vports, called from efx_ef10_sriov_free_vf_vswitching. Set the pointer to NULL at release time to not trying to read it later. Reproducer and dmesg log (note that kfence doesn't detect it every time): $ echo 1 > /sys/class/net/enp65s0f0np0/device/sriov_numvfs $ echo 0 > /sys/class/net/enp65s0f0np0/device/sriov_numvfs BUG: KFENCE: use-after-free read in efx_ef10_sriov_free_vf_vswitching+0x82/0x170 [sfc] Use-after-free read at 0x00000000ff3c1ba5 (in kfence-#224): efx_ef10_sriov_free_vf_vswitching+0x82/0x170 [sfc] efx_ef10_pci_sriov_disable+0x38/0x70 [sfc] efx_pci_sriov_configure+0x24/0x40 [sfc] sriov_numvfs_store+0xfe/0x140 kernfs_fop_write_iter+0x11c/0x1b0 new_sync_write+0x11f/0x1b0 vfs_write+0x1eb/0x280 ksys_write+0x5f/0xe0 do_syscall_64+0x5c/0x80 entry_SYSCALL_64_after_hwframe+0x44/0xae kfence-#224: 0x00000000edb8ef95-0x00000000671f5ce1, size=2792, cache=kmalloc-4k allocated by task 6771 on cpu 10 at 3137.860196s: pci_alloc_dev+0x21/0x60 pci_iov_add_virtfn+0x2a2/0x320 sriov_enable+0x212/0x3e0 efx_ef10_sriov_configure+0x67/0x80 [sfc] efx_pci_sriov_configure+0x24/0x40 [sfc] sriov_numvfs_store+0xba/0x140 kernfs_fop_write_iter+0x11c/0x1b0 new_sync_write+0x11f/0x1b0 vfs_write+0x1eb/0x280 ksys_write+0x5f/0xe0 do_syscall_64+0x5c/0x80 entry_SYSCALL_64_after_hwframe+0x44/0xae freed by task 6771 on cpu 12 at 3170.991309s: device_release+0x34/0x90 kobject_cleanup+0x3a/0x130 pci_iov_remove_virtfn+0xd9/0x120 sriov_disable+0x30/0xe0 efx_ef10_pci_sriov_disable+0x57/0x70 [sfc] efx_pci_sriov_configure+0x24/0x40 [sfc] sriov_numvfs_store+0xfe/0x140 kernfs_fop_write_iter+0x11c/0x1b0 new_sync_write+0x11f/0x1b0 vfs_write+0x1eb/0x280 ksys_write+0x5f/0xe0 do_syscall_64+0x5c/0x80 entry_SYSCALL_64_after_hwframe+0x44/0xae

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49636 In the Linux kernel, the following vulnerability has been resolved: vlan: fix memory leak in vlan_newlink() Blamed commit added back a bug I fixed in commit 9bbd917e0bec ("vlan: fix memory leak in vlan_dev_set_egress_priority") If a memory allocation fails in vlan_changelink() after other allocations succeeded, we need to call vlan_dev_free_egress_priority() to free all allocated memory because after a failed ->newlink() we do not call any methods like ndo_uninit() or dev->priv_destructor(). In following example, if the allocation for last element 2000:2001 fails, we need to free eight prior allocations: ip link add link dummy0 dummy0.100 type vlan id 100 \ egress-qos-map 1:2 2:3 3:4 4:5 5:6 6:7 7:8 8:9 2000:2001 syzbot report was: BUG: memory leak unreferenced object 0xffff888117bd1060 (size 32): comm "syz-executor408", pid 3759, jiffies 4294956555 (age 34.090s) hex dump (first 32 bytes): 09 00 00 00 00 a0 00 00 00 00 00 00 00 00 00 00 ................ 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 ................ backtrace: [<ffffffff83fc60ad>] kmalloc include/linux/slab.h:600 [inline] [<ffffffff83fc60ad>] vlan_dev_set_egress_priority+0xed/0x170 net/8021q/vlan_dev.c:193 [<ffffffff83fc6628>] vlan_changelink+0x178/0x1d0 net/8021q/vlan_netlink.c:128 [<ffffffff83fc67c8>] vlan_newlink+0x148/0x260 net/8021q/vlan_netlink.c:185 [<ffffffff838b1278>] rtnl_newlink_create net/core/rtnetlink.c:3363 [inline] [<ffffffff838b1278>] __rtnl_newlink+0xa58/0xdc0 net/core/rtnetlink.c:3580 [<ffffffff838b1629>] rtnl_newlink+0x49/0x70 net/core/rtnetlink.c:3593 [<ffffffff838ac66c>] rtnetlink_rcv_msg+0x21c/0x5c0 net/core/rtnetlink.c:6089 [<ffffffff839f9c37>] netlink_rcv_skb+0x87/0x1d0 net/netlink/af_netlink.c:2501 [<ffffffff839f8da7>] netlink_unicast_kernel net/netlink/af_netlink.c:1319 [inline] [<ffffffff839f8da7>] netlink_unicast+0x397/0x4c0 net/netlink/af_netlink.c:1345 [<ffffffff839f9266>] netlink_sendmsg+0x396/0x710 net/netlink/af_netlink.c:1921 [<ffffffff8384dbf6>] sock_sendmsg_nosec net/socket.c:714 [inline] [<ffffffff8384dbf6>] sock_sendmsg+0x56/0x80 net/socket.c:734 [<ffffffff8384e15c>] ____sys_sendmsg+0x36c/0x390 net/socket.c:2488 [<ffffffff838523cb>] ___sys_sendmsg+0x8b/0xd0 net/socket.c:2542 [<ffffffff838525b8>] __sys_sendmsg net/socket.c:2571 [inline] [<ffffffff838525b8>] __do_sys_sendmsg net/socket.c:2580 [inline] [<ffffffff838525b8>] __se_sys_sendmsg net/socket.c:2578 [inline] [<ffffffff838525b8>] __x64_sys_sendmsg+0x78/0xf0 net/socket.c:2578 [<ffffffff845ad8d5>] do_syscall_x64 arch/x86/entry/common.c:50 [inline] [<ffffffff845ad8d5>] do_syscall_64+0x35/0xb0 arch/x86/entry/common.c:80 [<ffffffff8460006a>] entry_SYSCALL_64_after_hwframe+0x46/0xb0

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49639 In the Linux kernel, the following vulnerability has been resolved: cipso: Fix data-races around sysctl. While reading cipso sysctl variables, they can be changed concurrently. So, we need to add READ_ONCE() to avoid data-races.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49644 In the Linux kernel, the following vulnerability has been resolved: drm/i915: fix a possible refcount leak in intel_dp_add_mst_connector() If drm_connector_init fails, intel_connector_free will be called to take care of proper free. So it is necessary to drop the refcount of port before intel_connector_free. (cherry picked from commit cea9ed611e85d36a05db52b6457bf584b7d969e2)

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49647 In the Linux kernel, the following vulnerability has been resolved: cgroup: Use separate src/dst nodes when preloading css_sets for migration Each cset (css_set) is pinned by its tasks. When we're moving tasks around across csets for a migration, we need to hold the source and destination csets to ensure that they don't go away while we're moving tasks about. This is done by linking cset->mg_preload_node on either the mgctx->preloaded_src_csets or mgctx->preloaded_dst_csets list. Using the same cset->mg_preload_node for both the src and dst lists was deemed okay as a cset can't be both the source and destination at the same time. Unfortunately, this overloading becomes problematic when multiple tasks are involved in a migration and some of them are identity noop migrations while others are actually moving across cgroups. For example, this can happen with the following sequence on cgroup1: #1> mkdir -p /sys/fs/cgroup/misc/a/b #2> echo $$ > /sys/fs/cgroup/misc/a/cgroup.procs #3> RUN_A_COMMAND_WHICH_CREATES_MULTIPLE_THREADS & #4> PID=$! #5> echo $PID > /sys/fs/cgroup/misc/a/b/tasks #6> echo $PID > /sys/fs/cgroup/misc/a/cgroup.procs the process including the group leader back into a. In this final migration, non-leader threads would be doing identity migration while the group leader is doing an actual one. After #3, let's say the whole process was in cset A, and that after #4, the leader moves to cset B. Then, during #6, the following happens: 1. cgroup_migrate_add_src() is called on B for the leader. 2. cgroup_migrate_add_src() is called on A for the other threads. 3. cgroup_migrate_prepare_dst() is called. It scans the src list. 4. It notices that B wants to migrate to A, so it tries to A to the dst list but realizes that its ->mg_preload_node is already busy. 5. and then it notices A wants to migrate to A as it's an identity migration, it culls it by list_del_init()'ing its ->mg_preload_node and putting references accordingly. 6. The rest of migration takes place with B on the src list but nothing on the dst list. This means that A isn't held while migration is in progress. If all tasks leave A before the migration finishes and the incoming task pins it, the cset will be destroyed leading to use-after-free. This is caused by overloading cset->mg_preload_node for both src and dst preload lists. We wanted to exclude the cset from the src list but ended up inadvertently excluding it from the dst list too. This patch fixes the issue by separating out cset->mg_preload_node into ->mg_src_preload_node and ->mg_dst_preload_node, so that the src and dst preloadings don't interfere with each other.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49653 In the Linux kernel, the following vulnerability has been resolved: i2c: piix4: Fix a memory leak in the EFCH MMIO support The recently added support for EFCH MMIO regions introduced a memory leak in that code path. The leak is caused by the fact that release_resource() merely removes the resource from the tree but does not free its memory. We need to call release_mem_region() instead, which does free the memory. As a nice side effect, this brings back some symmetry between the legacy and MMIO paths.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49664 In the Linux kernel, the following vulnerability has been resolved: tipc: move bc link creation back to tipc_node_create Shuang Li reported a NULL pointer dereference crash: [] BUG: kernel NULL pointer dereference, address: 0000000000000068 [] RIP: 0010:tipc_link_is_up+0x5/0x10 [tipc] [] Call Trace: [] <IRQ> [] tipc_bcast_rcv+0xa2/0x190 [tipc] [] tipc_node_bc_rcv+0x8b/0x200 [tipc] [] tipc_rcv+0x3af/0x5b0 [tipc] [] tipc_udp_recv+0xc7/0x1e0 [tipc] It was caused by the 'l' passed into tipc_bcast_rcv() is NULL. When it creates a node in tipc_node_check_dest(), after inserting the new node into hashtable in tipc_node_create(), it creates the bc link. However, there is a gap between this insert and bc link creation, a bc packet may come in and get the node from the hashtable then try to dereference its bc link, which is NULL. This patch is to fix it by moving the bc link creation before inserting into the hashtable. Note that for a preliminary node becoming "real", the bc link creation should also be called before it's rehashed, as we don't create it for preliminary nodes.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49671 In the Linux kernel, the following vulnerability has been resolved: RDMA/cm: Fix memory leak in ib_cm_insert_listen cm_alloc_id_priv() allocates resource for the cm_id_priv. When cm_init_listen() fails it doesn't free it, leading to memory leak. Add the missing error unwind.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49700 In the Linux kernel, the following vulnerability has been resolved: mm/slub: add missing TID updates on slab deactivation The fastpath in slab_alloc_node() assumes that c->slab is stable as long as the TID stays the same. However, two places in __slab_alloc() currently don't update the TID when deactivating the CPU slab. If multiple operations race the right way, this could lead to an object getting lost; or, in an even more unlikely situation, it could even lead to an object being freed onto the wrong slab's freelist, messing up the `inuse` counter and eventually causing a page to be freed to the page allocator while it still contains slab objects. (I haven't actually tested these cases though, this is just based on looking at the code. Writing testcases for this stuff seems like it'd be a pain...) The race leading to state inconsistency is (all operations on the same CPU and kmem_cache): - task A: begin do_slab_free(): - read TID - read pcpu freelist (==NULL) - check `slab == c->slab` (true) - [PREEMPT A->B] - task B: begin slab_alloc_node(): - fastpath fails (`c->freelist` is NULL) - enter __slab_alloc() - slub_get_cpu_ptr() (disables preemption) - enter ___slab_alloc() - take local_lock_irqsave() - read c->freelist as NULL - get_freelist() returns NULL - write `c->slab = NULL` - drop local_unlock_irqrestore() - goto new_slab - slub_percpu_partial() is NULL - get_partial() returns NULL - slub_put_cpu_ptr() (enables preemption) - [PREEMPT B->A] - task A: finish do_slab_free(): - this_cpu_cmpxchg_double() succeeds() - [CORRUPT STATE: c->slab==NULL, c->freelist!=NULL] From there, the object on c->freelist will get lost if task B is allowed to continue from here: It will proceed to the retry_load_slab label, set c->slab, then jump to load_freelist, which clobbers c->freelist. But if we instead continue as follows, we get worse corruption: - task A: run __slab_free() on object from other struct slab: - CPU_PARTIAL_FREE case (slab was on no list, is now on pcpu partial) - task A: run slab_alloc_node() with NUMA node constraint: - fastpath fails (c->slab is NULL) - call __slab_alloc() - slub_get_cpu_ptr() (disables preemption) - enter ___slab_alloc() - c->slab is NULL: goto new_slab - slub_percpu_partial() is non-NULL - set c->slab to slub_percpu_partial(c) - [CORRUPT STATE: c->slab points to slab-1, c->freelist has objects from slab-2] - goto redo - node_match() fails - goto deactivate_slab - existing c->freelist is passed into deactivate_slab() - inuse count of slab-1 is decremented to account for object from slab-2 At this point, the inuse count of slab-1 is 1 lower than it should be. This means that if we free all allocated objects in slab-1 except for one, SLUB will think that slab-1 is completely unused, and may free its page, leading to use-after-free.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49853 In the Linux kernel, the following vulnerability has been resolved: net: macvlan: fix memory leaks of macvlan_common_newlink kmemleak reports memory leaks in macvlan_common_newlink, as follows: ip link add link eth0 name .. type macvlan mode source macaddr add <MAC-ADDR> kmemleak reports: unreferenced object 0xffff8880109bb140 (size 64): comm "ip", pid 284, jiffies 4294986150 (age 430.108s) hex dump (first 32 bytes): 00 00 00 00 00 00 00 00 b8 aa 5a 12 80 88 ff ff ..........Z..... 80 1b fa 0d 80 88 ff ff 1e ff ac af c7 c1 6b 6b ..............kk backtrace: [<ffffffff813e06a7>] kmem_cache_alloc_trace+0x1c7/0x300 [<ffffffff81b66025>] macvlan_hash_add_source+0x45/0xc0 [<ffffffff81b66a67>] macvlan_changelink_sources+0xd7/0x170 [<ffffffff81b6775c>] macvlan_common_newlink+0x38c/0x5a0 [<ffffffff81b6797e>] macvlan_newlink+0xe/0x20 [<ffffffff81d97f8f>] __rtnl_newlink+0x7af/0xa50 [<ffffffff81d98278>] rtnl_newlink+0x48/0x70 ... In the scenario where the macvlan mode is configured as 'source', macvlan_changelink_sources() will be execured to reconfigure list of remote source mac addresses, at the same time, if register_netdevice() return an error, the resource generated by macvlan_changelink_sources() is not cleaned up. Using this patch, in the case of an error, it will execute macvlan_flush_sources() to ensure that the resource is cleaned up.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49858 In the Linux kernel, the following vulnerability has been resolved: octeontx2-pf: Fix SQE threshold checking Current way of checking available SQE count which is based on HW updated SQB count could result in driver submitting an SQE even before CQE for the previously transmitted SQE at the same index is processed in NAPI resulting losing SKB pointers, hence a leak. Fix this by checking a consumer index which is updated once CQE is processed.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-49885 In the Linux kernel, the following vulnerability has been resolved: ACPI: APEI: Fix integer overflow in ghes_estatus_pool_init() Change num_ghes from int to unsigned int, preventing an overflow and causing subsequent vmalloc() to fail. The overflow happens in ghes_estatus_pool_init() when calculating len during execution of the statement below as both multiplication operands here are signed int: len += (num_ghes * GHES_ESOURCE_PREALLOC_MAX_SIZE); The following call trace is observed because of this bug: [ 9.317108] swapper/0: vmalloc error: size 18446744071562596352, exceeds total pages, mode:0xcc0(GFP_KERNEL), nodemask=(null),cpuset=/,mems_allowed=0-1 [ 9.317131] Call Trace: [ 9.317134] <TASK> [ 9.317137] dump_stack_lvl+0x49/0x5f [ 9.317145] dump_stack+0x10/0x12 [ 9.317146] warn_alloc.cold+0x7b/0xdf [ 9.317150] ? __device_attach+0x16a/0x1b0 [ 9.317155] __vmalloc_node_range+0x702/0x740 [ 9.317160] ? device_add+0x17f/0x920 [ 9.317164] ? dev_set_name+0x53/0x70 [ 9.317166] ? platform_device_add+0xf9/0x240 [ 9.317168] __vmalloc_node+0x49/0x50 [ 9.317170] ? ghes_estatus_pool_init+0x43/0xa0 [ 9.317176] vmalloc+0x21/0x30 [ 9.317177] ghes_estatus_pool_init+0x43/0xa0 [ 9.317179] acpi_hest_init+0x129/0x19c [ 9.317185] acpi_init+0x434/0x4a4 [ 9.317188] ? acpi_sleep_proc_init+0x2a/0x2a [ 9.317190] do_one_initcall+0x48/0x200 [ 9.317195] kernel_init_freeable+0x221/0x284 [ 9.317200] ? rest_init+0xe0/0xe0 [ 9.317204] kernel_init+0x1a/0x130 [ 9.317205] ret_from_fork+0x22/0x30 [ 9.317208] </TASK> [ rjw: Subject and changelog edits ]

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49908 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: L2CAP: Fix memory leak in vhci_write Syzkaller reports a memory leak as follows: ==================================== BUG: memory leak unreferenced object 0xffff88810d81ac00 (size 240): [...] hex dump (first 32 bytes): 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 ................ 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 ................ backtrace: [<ffffffff838733d9>] __alloc_skb+0x1f9/0x270 net/core/skbuff.c:418 [<ffffffff833f742f>] alloc_skb include/linux/skbuff.h:1257 [inline] [<ffffffff833f742f>] bt_skb_alloc include/net/bluetooth/bluetooth.h:469 [inline] [<ffffffff833f742f>] vhci_get_user drivers/bluetooth/hci_vhci.c:391 [inline] [<ffffffff833f742f>] vhci_write+0x5f/0x230 drivers/bluetooth/hci_vhci.c:511 [<ffffffff815e398d>] call_write_iter include/linux/fs.h:2192 [inline] [<ffffffff815e398d>] new_sync_write fs/read_write.c:491 [inline] [<ffffffff815e398d>] vfs_write+0x42d/0x540 fs/read_write.c:578 [<ffffffff815e3cdd>] ksys_write+0x9d/0x160 fs/read_write.c:631 [<ffffffff845e0645>] do_syscall_x64 arch/x86/entry/common.c:50 [inline] [<ffffffff845e0645>] do_syscall_64+0x35/0xb0 arch/x86/entry/common.c:80 [<ffffffff84600087>] entry_SYSCALL_64_after_hwframe+0x63/0xcd ==================================== HCI core will uses hci_rx_work() to process frame, which is queued to the hdev->rx_q tail in hci_recv_frame() by HCI driver. Yet the problem is that, HCI core may not free the skb after handling ACL data packets. To be more specific, when start fragment does not contain the L2CAP length, HCI core just copies skb into conn->rx_skb and finishes frame process in l2cap_recv_acldata(), without freeing the skb, which triggers the above memory leak. This patch solves it by releasing the relative skb, after processing the above case in l2cap_recv_acldata().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49949 In the Linux kernel, the following vulnerability has been resolved: firmware_loader: Fix memory leak in firmware upload In the case of firmware-upload, an instance of struct fw_upload is allocated in firmware_upload_register(). This data needs to be freed in fw_dev_release(). Create a new fw_upload_free() function in sysfs_upload.c to handle the firmware-upload specific memory frees and incorporate the missing kfree call for the fw_upload structure.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-49974 In the Linux kernel, the following vulnerability has been resolved: HID: nintendo: fix rumble worker null pointer deref We can dereference a null pointer trying to queue work to a destroyed workqueue. If the device is disconnected, nintendo_hid_remove is called, in which the rumble_queue is destroyed. Avoid using that queue to defer rumble work once the controller state is set to JOYCON_CTLR_STATE_REMOVED. This eliminates the null pointer dereference.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-50170 In the Linux kernel, the following vulnerability has been resolved: kunit: executor: Fix a memory leak on failure in kunit_filter_tests It's possible that memory allocation for 'filtered' will fail, but for the copy of the suite to succeed. In this case, the copy could be leaked. Properly free 'copy' in the error case for the allocation of 'filtered' failing. Note that there may also have been a similar issue in kunit_filter_subsuites, before it was removed in "kunit: flatten kunit_suite*** to kunit_suite** in .kunit_test_suites". This was reported by clang-analyzer via the kernel test robot, here: https://lore.kernel.org/all/c8073b8e-7b9e-0830-4177-87c12f16349c@intel.com/ And by smatch via Dan Carpenter and the kernel test robot: https://lore.kernel.org/all/202207101328.ASjx88yj-lkp@intel.com/

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50354 In the Linux kernel, the following vulnerability has been resolved: drm/amdkfd: Fix kfd_process_device_init_vm error handling Should only destroy the ib_mem and let process cleanup worker to free the outstanding BOs. Reset the pointer in pdd->qpd structure, to avoid NULL pointer access in process destroy worker. BUG: kernel NULL pointer dereference, address: 0000000000000010 Call Trace: amdgpu_amdkfd_gpuvm_unmap_gtt_bo_from_kernel+0x46/0xb0 [amdgpu] kfd_process_device_destroy_cwsr_dgpu+0x40/0x70 [amdgpu] kfd_process_destroy_pdds+0x71/0x190 [amdgpu] kfd_process_wq_release+0x2a2/0x3b0 [amdgpu] process_one_work+0x2a1/0x600 worker_thread+0x39/0x3d0

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-50393 In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: SDMA update use unlocked iterator SDMA update page table may be called from unlocked context, this generate below warning. Use unlocked iterator to handle this case. WARNING: CPU: 0 PID: 1475 at drivers/dma-buf/dma-resv.c:483 dma_resv_iter_next Call Trace: dma_resv_iter_first+0x43/0xa0 amdgpu_vm_sdma_update+0x69/0x2d0 [amdgpu] amdgpu_vm_ptes_update+0x29c/0x870 [amdgpu] amdgpu_vm_update_range+0x2f6/0x6c0 [amdgpu] svm_range_unmap_from_gpus+0x115/0x300 [amdgpu] svm_range_cpu_invalidate_pagetables+0x510/0x5e0 [amdgpu] __mmu_notifier_invalidate_range_start+0x1d3/0x230 unmap_vmas+0x140/0x150 unmap_region+0xa8/0x110

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2022-50562 In the Linux kernel, the following vulnerability has been resolved: tpm: acpi: Call acpi_put_table() to fix memory leak The start and length of the event log area are obtained from TPM2 or TCPA table, so we call acpi_get_table() to get the ACPI information, but the acpi_get_table() should be coupled with acpi_put_table() to release the ACPI memory, add the acpi_put_table() properly to fix the memory leak. While we are at it, remove the redundant empty line at the end of the tpm_read_log_acpi().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50615 In the Linux kernel, the following vulnerability has been resolved: perf/x86/intel/uncore: Fix reference count leak in snr_uncore_mmio_map() pci_get_device() will increase the reference count for the returned pci_dev, so snr_uncore_get_mc_dev() will return a pci_dev with its reference count increased. We need to call pci_dev_put() to decrease the reference count. Let's add the missing pci_dev_put().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50617 In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu/powerplay/psm: Fix memory leak in power state init Commit 902bc65de0b3 ("drm/amdgpu/powerplay/psm: return an error in power state init") made the power state init function return early in case of failure to get an entry from the powerplay table, but it missed to clean up the allocated memory for the current power state before returning.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50619 In the Linux kernel, the following vulnerability has been resolved: drm/amdkfd: Fix memory leak in kfd_mem_dmamap_userptr() If the number of pages from the userptr BO differs from the SG BO then the allocated memory for the SG table doesn't get freed before returning -EINVAL, which may lead to a memory leak in some error paths. Fix this by checking the number of pages before allocating memory for the SG table.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50626 In the Linux kernel, the following vulnerability has been resolved: media: dvb-usb: fix memory leak in dvb_usb_adapter_init() Syzbot reports a memory leak in "dvb_usb_adapter_init()". The leak is due to not accounting for and freeing current iteration's adapter->priv in case of an error. Currently if an error occurs, it will exit before incrementing "num_adapters_initalized", which is used as a reference counter to free all adap->priv in "dvb_usb_adapter_exit()". There are multiple error paths that can exit from before incrementing the counter. Including the error handling paths for "dvb_usb_adapter_stream_init()", "dvb_usb_adapter_dvb_init()" and "dvb_usb_adapter_frontend_init()" within "dvb_usb_adapter_init()". This means that in case of an error in any of these functions the current iteration is not accounted for and the current iteration's adap->priv is not freed. Fix this by freeing the current iteration's adap->priv in the "stream_init_err:" label in the error path. The rest of the (accounted for) adap->priv objects are freed in dvb_usb_adapter_exit() as expected using the num_adapters_initalized variable. Syzbot report: BUG: memory leak unreferenced object 0xffff8881172f1a00 (size 512): comm "kworker/0:2", pid 139, jiffies 4294994873 (age 10.960s) hex dump (first 32 bytes): 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 ................ 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 ................ backtrace: [<ffffffff844af012>] dvb_usb_adapter_init drivers/media/usb/dvb-usb/dvb-usb-init.c:75 [inline] [<ffffffff844af012>] dvb_usb_init drivers/media/usb/dvb-usb/dvb-usb-init.c:184 [inline] [<ffffffff844af012>] dvb_usb_device_init.cold+0x4e5/0x79e drivers/media/usb/dvb-usb/dvb-usb-init.c:308 [<ffffffff830db21d>] dib0700_probe+0x8d/0x1b0 drivers/media/usb/dvb-usb/dib0700_core.c:883 [<ffffffff82d3fdc7>] usb_probe_interface+0x177/0x370 drivers/usb/core/driver.c:396 [<ffffffff8274ab37>] call_driver_probe drivers/base/dd.c:542 [inline] [<ffffffff8274ab37>] really_probe.part.0+0xe7/0x310 drivers/base/dd.c:621 [<ffffffff8274ae6c>] really_probe drivers/base/dd.c:583 [inline] [<ffffffff8274ae6c>] __driver_probe_device+0x10c/0x1e0 drivers/base/dd.c:752 [<ffffffff8274af6a>] driver_probe_device+0x2a/0x120 drivers/base/dd.c:782 [<ffffffff8274b786>] __device_attach_driver+0xf6/0x140 drivers/base/dd.c:899 [<ffffffff82747c87>] bus_for_each_drv+0xb7/0x100 drivers/base/bus.c:427 [<ffffffff8274b352>] __device_attach+0x122/0x260 drivers/base/dd.c:970 [<ffffffff827498f6>] bus_probe_device+0xc6/0xe0 drivers/base/bus.c:487 [<ffffffff82745cdb>] device_add+0x5fb/0xdf0 drivers/base/core.c:3405 [<ffffffff82d3d202>] usb_set_configuration+0x8f2/0xb80 drivers/usb/core/message.c:2170 [<ffffffff82d4dbfc>] usb_generic_driver_probe+0x8c/0xc0 drivers/usb/core/generic.c:238 [<ffffffff82d3f49c>] usb_probe_device+0x5c/0x140 drivers/usb/core/driver.c:293 [<ffffffff8274ab37>] call_driver_probe drivers/base/dd.c:542 [inline] [<ffffffff8274ab37>] really_probe.part.0+0xe7/0x310 drivers/base/dd.c:621 [<ffffffff8274ae6c>] really_probe drivers/base/dd.c:583 [inline] [<ffffffff8274ae6c>] __driver_probe_device+0x10c/0x1e0 drivers/base/dd.c:752

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50630 In the Linux kernel, the following vulnerability has been resolved: mm: hugetlb: fix UAF in hugetlb_handle_userfault The vma_lock and hugetlb_fault_mutex are dropped before handling userfault and reacquire them again after handle_userfault(), but reacquire the vma_lock could lead to UAF[1,2] due to the following race, hugetlb_fault hugetlb_no_page /*unlock vma_lock */ hugetlb_handle_userfault handle_userfault /* unlock mm->mmap_lock*/ vm_mmap_pgoff do_mmap mmap_region munmap_vma_range /* clean old vma */ /* lock vma_lock again <--- UAF */ /* unlock vma_lock */ Since the vma_lock will unlock immediately after hugetlb_handle_userfault(), let's drop the unneeded lock and unlock in hugetlb_handle_userfault() to fix the issue. [1] https://lore.kernel.org/linux-mm/000000000000d5e00a05e834962e@google.com/ [2] https://lore.kernel.org/linux-mm/20220921014457.1668-1-liuzixian4@huawei.com/

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50667 In the Linux kernel, the following vulnerability has been resolved: drm/vmwgfx: Fix memory leak in vmw_mksstat_add_ioctl() If the copy of the description string from userspace fails, then the page for the instance descriptor doesn't get freed before returning -EFAULT, which leads to a memleak.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50717 In the Linux kernel, the following vulnerability has been resolved: nvmet-tcp: add bounds check on Transfer Tag ttag is used as an index to get cmd in nvmet_tcp_handle_h2c_data_pdu(), add a bounds check to avoid out-of-bounds access.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2022-50785 In the Linux kernel, the following vulnerability has been resolved: fsi: occ: Prevent use after free Use get_device and put_device in the open and close functions to make sure the device doesn't get freed while a file descriptor is open. Also, lock around the freeing of the device buffer and check the buffer before using it in the submit function.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-2976 Use of Java's default temporary directory for file creation in `FileBackedOutputStream` in Google Guava versions 1.0 to 31.1 on Unix systems and Android Ice Cream Sandwich allows other users and apps on the machine with access to the default Java temporary directory to be able to access the files created by the class. Even though the security vulnerability is fixed in version 32.0.0, we recommend using version 32.0.1 as version 32.0.0 breaks some functionality under Windows.

trino

CVE-2023-4010 Rejected reason: Rejected because the CVE description attributes a vulnerability to a non-existent Linux kernel function (usb_giveback_urb()) and the reported issue cannot be mapped to any valid codebase.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2023-4016 Under some circumstances, this weakness allows a user who has access to run the “ps” utility on a machine, the ability to write almost unlimited amounts of unfiltered data into the process heap.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-4039 **DISPUTED**A failure in the -fstack-protector feature in GCC-based toolchains that target AArch64 allows an attacker to exploit an existing buffer overflow in dynamically-sized local variables in your application without this being detected. This stack-protector failure only applies to C99-style dynamically-sized local variables or those created using alloca(). The stack-protector operates as intended for statically-sized local variables. The default behavior when the stack-protector detects an overflow is to terminate your application, resulting in controlled loss of availability. An attacker who can exploit a buffer overflow without triggering the stack-protector might be able to change program flow control to cause an uncontrolled loss of availability or to go further and affect confidentiality or integrity. NOTE: The GCC project argues that this is a missed hardening bug and not a vulnerability by itself.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-4641 A flaw was found in shadow-utils. When asking for a new password, shadow-utils asks the password twice. If the password fails on the second attempt, shadow-utils fails in cleaning the buffer used to store the first entry. This may allow an attacker with enough access to retrieve the password from the memory.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-4806 A flaw has been identified in glibc. In an extremely rare situation, the getaddrinfo function may access memory that has been freed, resulting in an application crash. This issue is only exploitable when a NSS module implements only the _nss_*_gethostbyname2_r and _nss_*_getcanonname_r hooks without implementing the _nss_*_gethostbyname3_r hook. The resolved name should return a large number of IPv6 and IPv4, and the call to the getaddrinfo function should have the AF_INET6 address family with AI_CANONNAME, AI_ALL and AI_V4MAPPED as flags.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-5981 A vulnerability was found that the response times to malformed ciphertexts in RSA-PSK ClientKeyExchange differ from response times of ciphertexts with correct PKCS#1 v1.5 padding.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-6992 Cloudflare version of zlib library was found to be vulnerable to memory corruption issues affecting the deflation algorithm implementation (deflate.c). The issues resulted from improper input validation and heap-based buffer overflow. A local attacker could exploit the problem during compression using a crafted malicious file potentially leading to denial of service of the software. Patches: The issue has been patched in commit 8352d10 https://github.com/cloudflare/zlib/commit/8352d108c05db1bdc5ac3bdf834dad641694c13c . The upstream repository is not affected.

dex_haveged

CVE-2023-20585 Insufficient checks of the RMP on host buffer access in IOMMU may allow an attacker with privileges and a compromised hypervisor to trigger an out of bounds condition without RMP checks, resulting in a potential loss of confidential guest integrity.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2023-22946 In Apache Spark versions prior to 3.4.0, applications using spark-submit can specify a 'proxy-user' to run as, limiting privileges. The application can execute code with the privileges of the submitting user, however, by providing malicious configuration-related classes on the classpath. This affects architectures relying on proxy-user, for example those using Apache Livy to manage submitted applications. Update to Apache Spark 3.4.0 or later, and ensure that spark.submit.proxyUser.allowCustomClasspathInClusterMode is set to its default of "false", and is not overridden by submitted applications.

dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-3.3.2-7.1.9.1078
dex-spark-history-server-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2023-23931 cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. In affected versions `Cipher.update_into` would accept Python objects which implement the buffer protocol, but provide only immutable buffers. This would allow immutable objects (such as `bytes`) to be mutated, thus violating fundamental rules of Python and resulting in corrupted output. This now correctly raises an exception. This issue has been present since `update_into` was originally introduced in cryptography 1.8.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
hue

CVE-2023-26604 systemd before 247 does not adequately block local privilege escalation for some Sudo configurations, e.g., plausible sudoers files in which the "systemctl status" command may be executed. Specifically, systemd does not set LESSSECURE to 1, and thus other programs may be launched from the less program. This presents a substantial security risk when running systemctl from Sudo, because less executes as root when the terminal size is too small to show the complete systemctl output.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-29483 eventlet before 0.35.2, as used in dnspython before 2.6.0, allows remote attackers to interfere with DNS name resolution by quickly sending an invalid packet from the expected IP address and source port, aka a "TuDoor" attack. In other words, dnspython does not have the preferred behavior in which the DNS name resolution algorithm would proceed, within the full time window, in order to wait for a valid packet. NOTE: dnspython 2.6.0 is unusable for a different reason that was addressed in 2.6.1.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
hue

CVE-2023-31722 There exists a heap buffer overflow in nasm 2.16.02rc1 (GitHub commit: b952891).

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2023-33201 Bouncy Castle For Java before 1.74 is affected by an LDAP injection vulnerability. The vulnerability only affects applications that use an LDAP CertStore from Bouncy Castle to validate X.509 certificates. During the certificate validation process, Bouncy Castle inserts the certificate's Subject Name into an LDAP search filter without any escaping, which leads to an LDAP injection vulnerability.

thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2023-33202 Bouncy Castle for Java before 1.73 contains a potential Denial of Service (DoS) issue within the Bouncy Castle org.bouncycastle.openssl.PEMParser class. This class parses OpenSSL PEM encoded streams containing X.509 certificates, PKCS8 encoded keys, and PKCS7 objects. Parsing a file that has crafted ASN.1 data through the PEMParser causes an OutOfMemoryError, which can enable a denial of service attack. (For users of the FIPS Java API: BC-FJA 1.0.2.3 and earlier are affected; BC-FJA 1.0.2.4 is fixed.)

thunderhead-servicediscoverysimple

CVE-2023-34969 D-Bus before 1.15.6 sometimes allows unprivileged users to crash dbus-daemon. If a privileged user with control over the dbus-daemon is using the org.freedesktop.DBus.Monitoring interface to monitor message bus traffic, then an unprivileged user with the ability to connect to the same dbus-daemon can cause a dbus-daemon crash under some circumstances via an unreplyable message. When done on the well-known system bus, this is a denial-of-service vulnerability. The fixed versions are 1.12.28, 1.14.8, and 1.15.6.

cml-addon-hadoop-cli-7.3.1.200-90
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-35789 An issue was discovered in the C AMQP client library (aka rabbitmq-c) through 0.13.0 for RabbitMQ. Credentials can only be entered on the command line (e.g., for amqp-publish or amqp-consume) and are thus visible to local attackers by listing a process and its arguments.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2023-38665 Null pointer dereference in ieee_write_file in nasm 2.16rc0 allows attackers to cause a denial of service (crash).

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2023-38667 Stack-based buffer over-read in function disasm in nasm 2.16 allows attackers to cause a denial of service.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2023-38668 Stack-based buffer over-read in disasm in nasm 2.16 allows attackers to cause a denial of service (crash).

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2023-39327 A flaw was found in OpenJPEG. Maliciously constructed pictures can cause the program to enter a large loop and continuously print warning messages on the terminal.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2023-44487 The HTTP/2 protocol allows a denial of service (server resource consumption) because request cancellation can reset many streams quickly, as exploited in the wild in August through October 2023.

cdp-private
parcel

CVE-2023-47038 A vulnerability was found in perl 5.30.0 through 5.38.0. This issue occurs when a crafted regular expression is compiled by perl, which can allow an attacker controlled byte buffer overflow in a heap allocated buffer.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2023-48022 Anyscale Ray 2.6.3 and 2.8.0 allows a remote attacker to execute arbitrary code via the job submission API. NOTE: the vendor's position is that this report is irrelevant because Ray, as stated in its documentation, is not intended for use outside of a strictly controlled network environment

nim-deepseek-r1-v1.7.3

CVE-2023-48713 Knative Serving builds on Kubernetes to support deploying and serving of applications and functions as serverless containers. An attacker who controls a pod to a degree where they can control the responses from the /metrics endpoint can cause Denial-of-Service of the autoscaler from an unbound memory allocation bug. This is a DoS vulnerability, where a non-privileged Knative user can cause a DoS for the cluster. This issue has been patched in version 0.39.0.

api
authorizer

CVE-2023-49081 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. Improper validation made it possible for an attacker to modify the HTTP request (e.g. to insert a new header) or create a new HTTP request if the attacker controls the HTTP version. The vulnerability only occurs if the attacker can control the HTTP version of the request. This issue has been patched in version 3.9.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2023-49082 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. Improper validation makes it possible for an attacker to modify the HTTP request (e.g. insert a new header) or even create a new HTTP request if the attacker controls the HTTP method. The vulnerability occurs only if the attacker can control the HTTP method (GET, POST etc.) of the request. If the attacker can control the HTTP version of the request it will be able to modify the request (request smuggling). This issue has been patched in version 3.9.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2023-49083 cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. Calling `load_pem_pkcs7_certificates` or `load_der_pkcs7_certificates` could lead to a NULL-pointer dereference and segfault. Exploitation of this vulnerability poses a serious risk of Denial of Service (DoS) for any application attempting to deserialize a PKCS7 blob/certificate. The consequences extend to potential disruptions in system availability and stability. This vulnerability has been patched in version 41.0.6.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
hue

CVE-2023-49290 lestrrat-go/jwx is a Go module implementing various JWx (JWA/JWE/JWK/JWS/JWT, otherwise known as JOSE) technologies. A p2c parameter set too high in JWE's algorithm PBES2-* could lead to a denial of service. The JWE key management algorithms based on PBKDF2 require a JOSE Header Parameter called p2c (PBES2 Count). This parameter dictates the number of PBKDF2 iterations needed to derive a CEK wrapping key. Its primary purpose is to intentionally slow down the key derivation function, making password brute-force and dictionary attacks more resource- intensive. Therefore, if an attacker sets the p2c parameter in JWE to a very large number, it can cause a lot of computational consumption, resulting in a denial of service. This vulnerability has been addressed in commit `64f2a229b` which has been included in release version 1.2.27 and 2.0.18. Users are advised to upgrade. There are no known workarounds for this vulnerability.

install-cni
pilot

CVE-2023-52519 In the Linux kernel, the following vulnerability has been resolved: HID: intel-ish-hid: ipc: Disable and reenable ACPI GPE bit The EHL (Elkhart Lake) based platforms provide a OOB (Out of band) service, which allows to wakup device when the system is in S5 (Soft-Off state). This OOB service can be enabled/disabled from BIOS settings. When enabled, the ISH device gets PME wake capability. To enable PME wakeup, driver also needs to enable ACPI GPE bit. On resume, BIOS will clear the wakeup bit. So driver need to re-enable it in resume function to keep the next wakeup capability. But this BIOS clearing of wakeup bit doesn't decrement internal OS GPE reference count, so this reenabling on every resume will cause reference count to overflow. So first disable and reenable ACPI GPE bit using acpi_disable_gpe().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-52924 In the Linux kernel, the following vulnerability has been resolved: netfilter: nf_tables: don't skip expired elements during walk There is an asymmetry between commit/abort and preparation phase if the following conditions are met: 1. set is a verdict map ("1.2.3.4 : jump foo") 2. timeouts are enabled In this case, following sequence is problematic: 1. element E in set S refers to chain C 2. userspace requests removal of set S 3. kernel does a set walk to decrement chain->use count for all elements from preparation phase 4. kernel does another set walk to remove elements from the commit phase (or another walk to do a chain->use increment for all elements from abort phase) If E has already expired in 1), it will be ignored during list walk, so its use count won't have been changed. Then, when set is culled, ->destroy callback will zap the element via nf_tables_set_elem_destroy(), but this function is only safe for elements that have been deactivated earlier from the preparation phase: lack of earlier deactivate removes the element but leaks the chain use count, which results in a WARN splat when the chain gets removed later, plus a leak of the nft_chain structure. Update pipapo_get() not to skip expired elements, otherwise flush command reports bogus ENOENT errors.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-52931 In the Linux kernel, the following vulnerability has been resolved: drm/i915: Avoid potential vm use-after-free Adding the vm to the vm_xa table makes it visible to userspace, which could try to race with us to close the vm. So we need to take our extra reference before putting it in the table. (cherry picked from commit 99343c46d4e2b34c285d3d5f68ff04274c2f9fb4)

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-52937 In the Linux kernel, the following vulnerability has been resolved: HV: hv_balloon: fix memory leak with using debugfs_lookup() When calling debugfs_lookup() the result must have dput() called on it, otherwise the memory will leak over time. To make things simpler, just call debugfs_lookup_and_remove() instead which handles all of the logic at once.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-52938 In the Linux kernel, the following vulnerability has been resolved: usb: typec: ucsi: Don't attempt to resume the ports before they exist This will fix null pointer dereference that was caused by the driver attempting to resume ports that were not yet registered.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-52973 In the Linux kernel, the following vulnerability has been resolved: vc_screen: move load of struct vc_data pointer in vcs_read() to avoid UAF After a call to console_unlock() in vcs_read() the vc_data struct can be freed by vc_deallocate(). Because of that, the struct vc_data pointer load must be done at the top of while loop in vcs_read() to avoid a UAF when vcs_size() is called. Syzkaller reported a UAF in vcs_size(). BUG: KASAN: use-after-free in vcs_size (drivers/tty/vt/vc_screen.c:215) Read of size 4 at addr ffff8881137479a8 by task 4a005ed81e27e65/1537 CPU: 0 PID: 1537 Comm: 4a005ed81e27e65 Not tainted 6.2.0-rc5 #1 Hardware name: Red Hat KVM, BIOS 1.15.0-2.module Call Trace: <TASK> __asan_report_load4_noabort (mm/kasan/report_generic.c:350) vcs_size (drivers/tty/vt/vc_screen.c:215) vcs_read (drivers/tty/vt/vc_screen.c:415) vfs_read (fs/read_write.c:468 fs/read_write.c:450) ... </TASK> Allocated by task 1191: ... kmalloc_trace (mm/slab_common.c:1069) vc_allocate (./include/linux/slab.h:580 ./include/linux/slab.h:720 drivers/tty/vt/vt.c:1128 drivers/tty/vt/vt.c:1108) con_install (drivers/tty/vt/vt.c:3383) tty_init_dev (drivers/tty/tty_io.c:1301 drivers/tty/tty_io.c:1413 drivers/tty/tty_io.c:1390) tty_open (drivers/tty/tty_io.c:2080 drivers/tty/tty_io.c:2126) chrdev_open (fs/char_dev.c:415) do_dentry_open (fs/open.c:883) vfs_open (fs/open.c:1014) ... Freed by task 1548: ... kfree (mm/slab_common.c:1021) vc_port_destruct (drivers/tty/vt/vt.c:1094) tty_port_destructor (drivers/tty/tty_port.c:296) tty_port_put (drivers/tty/tty_port.c:312) vt_disallocate_all (drivers/tty/vt/vt_ioctl.c:662 (discriminator 2)) vt_ioctl (drivers/tty/vt/vt_ioctl.c:903) tty_ioctl (drivers/tty/tty_io.c:2776) ... The buggy address belongs to the object at ffff888113747800 which belongs to the cache kmalloc-1k of size 1024 The buggy address is located 424 bytes inside of 1024-byte region [ffff888113747800, ffff888113747c00) The buggy address belongs to the physical page: page:00000000b3fe6c7c refcount:1 mapcount:0 mapping:0000000000000000 index:0x0 pfn:0x113740 head:00000000b3fe6c7c order:3 compound_mapcount:0 subpages_mapcount:0 compound_pincount:0 anon flags: 0x17ffffc0010200(slab|head|node=0|zone=2|lastcpupid=0x1fffff) raw: 0017ffffc0010200 ffff888100042dc0 0000000000000000 dead000000000001 raw: 0000000000000000 0000000000100010 00000001ffffffff 0000000000000000 page dumped because: kasan: bad access detected Memory state around the buggy address: ffff888113747880: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ffff888113747900: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb > ffff888113747980: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ^ ffff888113747a00: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ffff888113747a80: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ================================================================== Disabling lock debugging due to kernel taint

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53013 In the Linux kernel, the following vulnerability has been resolved: ptdma: pt_core_execute_cmd() should use spinlock The interrupt handler (pt_core_irq_handler()) of the ptdma driver can be called from interrupt context. The code flow in this function can lead down to pt_core_execute_cmd() which will attempt to grab a mutex, which is not appropriate in interrupt context and ultimately leads to a kernel panic. The fix here changes this mutex to a spinlock, which has been verified to resolve the issue.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53015 In the Linux kernel, the following vulnerability has been resolved: HID: betop: check shape of output reports betopff_init() only checks the total sum of the report counts for each report field to be at least 4, but hid_betopff_play() expects 4 report fields. A device advertising an output report with one field and 4 report counts would pass the check but crash the kernel with a NULL pointer dereference in hid_betopff_play().

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53026 In the Linux kernel, the following vulnerability has been resolved: RDMA/core: Fix ib block iterator counter overflow When registering a new DMA MR after selecting the best aligned page size for it, we iterate over the given sglist to split each entry to smaller, aligned to the selected page size, DMA blocks. In given circumstances where the sg entry and page size fit certain sizes and the sg entry is not aligned to the selected page size, the total size of the aligned pages we need to cover the sg entry is >= 4GB. Under this circumstances, while iterating page aligned blocks, the counter responsible for counting how much we advanced from the start of the sg entry is overflowed because its type is u32 and we pass 4GB in size. This can lead to an infinite loop inside the iterator function because the overflow prevents the counter to be larger than the size of the sg entry. Fix the presented problem by changing the advancement condition to eliminate overflow. Backtrace: [ 192.374329] efa_reg_user_mr_dmabuf [ 192.376783] efa_register_mr [ 192.382579] pgsz_bitmap 0xfffff000 rounddown 0x80000000 [ 192.386423] pg_sz [0x80000000] umem_length[0xc0000000] [ 192.392657] start 0x0 length 0xc0000000 params.page_shift 31 params.page_num 3 [ 192.399559] hp_cnt[3], pages_in_hp[524288] [ 192.403690] umem->sgt_append.sgt.nents[1] [ 192.407905] number entries: [1], pg_bit: [31] [ 192.411397] biter->__sg_nents [1] biter->__sg [0000000008b0c5d8] [ 192.415601] biter->__sg_advance [665837568] sg_dma_len[3221225472] [ 192.419823] biter->__sg_nents [1] biter->__sg [0000000008b0c5d8] [ 192.423976] biter->__sg_advance [2813321216] sg_dma_len[3221225472] [ 192.428243] biter->__sg_nents [1] biter->__sg [0000000008b0c5d8] [ 192.432397] biter->__sg_advance [665837568] sg_dma_len[3221225472]

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53387 In the Linux kernel, the following vulnerability has been resolved: scsi: ufs: core: Fix device management cmd timeout flow In the UFS error handling flow, the host will send a device management cmd (NOP OUT) to the device for link recovery. If this cmd times out and clearing the doorbell fails, ufshcd_wait_for_dev_cmd() will do nothing and return. hba->dev_cmd.complete struct is not set to NULL. When this happens, if cmd has been completed by device, then we will call complete() in __ufshcd_transfer_req_compl(). Because the complete struct is allocated on the stack, the following crash will occur: ipanic_die+0x24/0x38 [mrdump] die+0x344/0x748 arm64_notify_die+0x44/0x104 do_debug_exception+0x104/0x1e0 el1_dbg+0x38/0x54 el1_sync_handler+0x40/0x88 el1_sync+0x8c/0x140 queued_spin_lock_slowpath+0x2e4/0x3c0 __ufshcd_transfer_req_compl+0x3b0/0x1164 ufshcd_trc_handler+0x15c/0x308 ufshcd_host_reset_and_restore+0x54/0x260 ufshcd_reset_and_restore+0x28c/0x57c ufshcd_err_handler+0xeb8/0x1b6c process_one_work+0x288/0x964 worker_thread+0x4bc/0xc7c kthread+0x15c/0x264 ret_from_fork+0x10/0x30

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-53478 In the Linux kernel, the following vulnerability has been resolved: tracing/synthetic: Fix races on freeing last_cmd Currently, the "last_cmd" variable can be accessed by multiple processes asynchronously when multiple users manipulate synthetic_events node at the same time, it could lead to use-after-free or double-free. This patch add "lastcmd_mutex" to prevent "last_cmd" from being accessed asynchronously. ================================================================ It's easy to reproduce in the KASAN environment by running the two scripts below in different shells. script 1: while : do echo -n -e '\x88' > /sys/kernel/tracing/synthetic_events done script 2: while : do echo -n -e '\xb0' > /sys/kernel/tracing/synthetic_events done ================================================================ double-free scenario: process A process B ------------------- --------------- 1.kstrdup last_cmd 2.free last_cmd 3.free last_cmd(double-free) ================================================================ use-after-free scenario: process A process B ------------------- --------------- 1.kstrdup last_cmd 2.free last_cmd 3.tracing_log_err(use-after-free) ================================================================ Appendix 1. KASAN report double-free: BUG: KASAN: double-free in kfree+0xdc/0x1d4 Free of addr ***** by task sh/4879 Call trace: ... kfree+0xdc/0x1d4 create_or_delete_synth_event+0x60/0x1e8 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ... Allocated by task 4879: ... kstrdup+0x5c/0x98 create_or_delete_synth_event+0x6c/0x1e8 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ... Freed by task 5464: ... kfree+0xdc/0x1d4 create_or_delete_synth_event+0x60/0x1e8 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ... ================================================================ Appendix 2. KASAN report use-after-free: BUG: KASAN: use-after-free in strlen+0x5c/0x7c Read of size 1 at addr ***** by task sh/5483 sh: CPU: 7 PID: 5483 Comm: sh ... __asan_report_load1_noabort+0x34/0x44 strlen+0x5c/0x7c tracing_log_err+0x60/0x444 create_or_delete_synth_event+0xc4/0x204 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ... Allocated by task 5483: ... kstrdup+0x5c/0x98 create_or_delete_synth_event+0x80/0x204 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ... Freed by task 5480: ... kfree+0xdc/0x1d4 create_or_delete_synth_event+0x74/0x204 trace_parse_run_command+0x2bc/0x4b8 synth_events_write+0x20/0x30 vfs_write+0x200/0x830 ...

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-53609 In the Linux kernel, the following vulnerability has been resolved: scsi: Revert "scsi: core: Do not increase scsi_device's iorequest_cnt if dispatch failed" The "atomic_inc(&cmd->device->iorequest_cnt)" in scsi_queue_rq() would cause kernel panic because cmd->device may be freed after returning from scsi_dispatch_cmd(). This reverts commit cfee29ffb45b1c9798011b19d454637d1b0fe87d.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-53639 In the Linux kernel, the following vulnerability has been resolved: wifi: ath6kl: reduce WARN to dev_dbg() in callback The warn is triggered on a known race condition, documented in the code above the test, that is correctly handled. Using WARN() hinders automated testing. Reducing severity.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53764 In the Linux kernel, the following vulnerability has been resolved: wifi: ath12k: Handle lock during peer_id find ath12k_peer_find_by_id() requires that the caller hold the ab->base_lock. Currently the WBM error path does not hold the lock and calling that function, leads to the following lockdep_assert()in QCN9274: [105162.160893] ------------[ cut here ]------------ [105162.160916] WARNING: CPU: 3 PID: 0 at drivers/net/wireless/ath/ath12k/peer.c:71 ath12k_peer_find_by_id+0x52/0x60 [ath12k] [105162.160933] Modules linked in: ath12k(O) qrtr_mhi qrtr mac80211 cfg80211 mhi qmi_helpers libarc4 nvme nvme_core [last unloaded: ath12k(O)] [105162.160967] CPU: 3 PID: 0 Comm: swapper/3 Tainted: G W O 6.1.0-rc2+ #3 [105162.160972] Hardware name: Intel(R) Client Systems NUC8i7HVK/NUC8i7HVB, BIOS HNKBLi70.86A.0056.2019.0506.1527 05/06/2019 [105162.160977] RIP: 0010:ath12k_peer_find_by_id+0x52/0x60 [ath12k] [105162.160990] Code: 07 eb 0f 39 68 24 74 0a 48 8b 00 48 39 f8 75 f3 31 c0 5b 5d c3 48 8d bf b0 f2 00 00 be ff ff ff ff e8 22 20 c4 e2 85 c0 75 bf <0f> 0b eb bb 66 2e 0f 1f 84 00 00 00 00 00 41 54 4c 8d a7 98 f2 00 [105162.160996] RSP: 0018:ffffa223001acc60 EFLAGS: 00010246 [105162.161003] RAX: 0000000000000000 RBX: ffff9f0573940000 RCX: 0000000000000000 [105162.161008] RDX: 0000000000000001 RSI: ffffffffa3951c8e RDI: ffffffffa39a96d7 [105162.161013] RBP: 000000000000000a R08: 0000000000000000 R09: 0000000000000000 [105162.161017] R10: ffffa223001acb40 R11: ffffffffa3d57c60 R12: ffff9f057394f2e0 [105162.161022] R13: ffff9f0573940000 R14: ffff9f04ecd659c0 R15: ffff9f04d5a9b040 [105162.161026] FS: 0000000000000000(0000) GS:ffff9f0575600000(0000) knlGS:0000000000000000 [105162.161031] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [105162.161036] CR2: 00001d5c8277a008 CR3: 00000001e6224006 CR4: 00000000003706e0 [105162.161041] Call Trace: [105162.161046] <IRQ> [105162.161051] ath12k_dp_rx_process_wbm_err+0x6da/0xaf0 [ath12k] [105162.161072] ? ath12k_dp_rx_process_err+0x80e/0x15a0 [ath12k] [105162.161084] ? __lock_acquire+0x4ca/0x1a60 [105162.161104] ath12k_dp_service_srng+0x263/0x310 [ath12k] [105162.161120] ath12k_pci_ext_grp_napi_poll+0x1c/0x70 [ath12k] [105162.161133] __napi_poll+0x22/0x260 [105162.161141] net_rx_action+0x2f8/0x380 [105162.161153] __do_softirq+0xd0/0x4c9 [105162.161162] irq_exit_rcu+0x88/0xe0 [105162.161169] common_interrupt+0xa5/0xc0 [105162.161174] </IRQ> [105162.161179] <TASK> [105162.161184] asm_common_interrupt+0x22/0x40 Handle spin lock/unlock in WBM error path to hold the necessary lock expected by ath12k_peer_find_by_id(). Tested-on: QCN9274 hw2.0 PCI WLAN.WBE.1.0-03171-QCAHKSWPL_SILICONZ-1

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-53811 In the Linux kernel, the following vulnerability has been resolved: RDMA/irdma: Cap MSIX used to online CPUs + 1 The irdma driver can use a maximum number of msix vectors equal to num_online_cpus() + 1 and the kernel warning stack below is shown if that number is exceeded. The kernel throws a warning as the driver tries to update the affinity hint with a CPU mask greater than the max CPU IDs. Fix this by capping the MSIX vectors to num_online_cpus() + 1. WARNING: CPU: 7 PID: 23655 at include/linux/cpumask.h:106 irdma_cfg_ceq_vector+0x34c/0x3f0 [irdma] RIP: 0010:irdma_cfg_ceq_vector+0x34c/0x3f0 [irdma] Call Trace: irdma_rt_init_hw+0xa62/0x1290 [irdma] ? irdma_alloc_local_mac_entry+0x1a0/0x1a0 [irdma] ? __is_kernel_percpu_address+0x63/0x310 ? rcu_read_lock_held_common+0xe/0xb0 ? irdma_lan_unregister_qset+0x280/0x280 [irdma] ? irdma_request_reset+0x80/0x80 [irdma] ? ice_get_qos_params+0x84/0x390 [ice] irdma_probe+0xa40/0xfc0 [irdma] ? rcu_read_lock_bh_held+0xd0/0xd0 ? irdma_remove+0x140/0x140 [irdma] ? rcu_read_lock_sched_held+0x62/0xe0 ? down_write+0x187/0x3d0 ? auxiliary_match_id+0xf0/0x1a0 ? irdma_remove+0x140/0x140 [irdma] auxiliary_bus_probe+0xa6/0x100 __driver_probe_device+0x4a4/0xd50 ? __device_attach_driver+0x2c0/0x2c0 driver_probe_device+0x4a/0x110 __driver_attach+0x1aa/0x350 bus_for_each_dev+0x11d/0x1b0 ? subsys_dev_iter_init+0xe0/0xe0 bus_add_driver+0x3b1/0x610 driver_register+0x18e/0x410 ? 0xffffffffc0b88000 irdma_init_module+0x50/0xaa [irdma] do_one_initcall+0x103/0x5f0 ? perf_trace_initcall_level+0x420/0x420 ? do_init_module+0x4e/0x700 ? __kasan_kmalloc+0x7d/0xa0 ? kmem_cache_alloc_trace+0x188/0x2b0 ? kasan_unpoison+0x21/0x50 do_init_module+0x1d1/0x700 load_module+0x3867/0x5260 ? layout_and_allocate+0x3990/0x3990 ? rcu_read_lock_held_common+0xe/0xb0 ? rcu_read_lock_sched_held+0x62/0xe0 ? rcu_read_lock_bh_held+0xd0/0xd0 ? __vmalloc_node_range+0x46b/0x890 ? lock_release+0x5c8/0xba0 ? alloc_vm_area+0x120/0x120 ? selinux_kernel_module_from_file+0x2a5/0x300 ? __inode_security_revalidate+0xf0/0xf0 ? __do_sys_init_module+0x1db/0x260 __do_sys_init_module+0x1db/0x260 ? load_module+0x5260/0x5260 ? do_syscall_64+0x22/0x450 do_syscall_64+0xa5/0x450 entry_SYSCALL_64_after_hwframe+0x66/0xdb

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53832 In the Linux kernel, the following vulnerability has been resolved: md/raid10: fix null-ptr-deref in raid10_sync_request init_resync() inits mempool and sets conf->have_replacemnt at the beginning of sync, close_sync() frees the mempool when sync is completed. After [1] recovery might be skipped and init_resync() is called but close_sync() is not. null-ptr-deref occurs with r10bio->dev[i].repl_bio. The following is one way to reproduce the issue. 1) create a array, wait for resync to complete, mddev->recovery_cp is set to MaxSector. 2) recovery is woken and it is skipped. conf->have_replacement is set to 0 in init_resync(). close_sync() not called. 3) some io errors and rdev A is set to WantReplacement. 4) a new device is added and set to A's replacement. 5) recovery is woken, A have replacement, but conf->have_replacemnt is 0. r10bio->dev[i].repl_bio will not be alloced and null-ptr-deref occurs. Fix it by not calling init_resync() if recovery skipped. [1] commit 7e83ccbecd60 ("md/raid10: Allow skipping recovery when clean arrays are assembled")

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53844 In the Linux kernel, the following vulnerability has been resolved: drm/ttm: Don't leak a resource on swapout move error If moving the bo to system for swapout failed, we were leaking a resource. Fix.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53847 In the Linux kernel, the following vulnerability has been resolved: usb-storage: alauda: Fix uninit-value in alauda_check_media() Syzbot got KMSAN to complain about access to an uninitialized value in the alauda subdriver of usb-storage: BUG: KMSAN: uninit-value in alauda_transport+0x462/0x57f0 drivers/usb/storage/alauda.c:1137 CPU: 0 PID: 12279 Comm: usb-storage Not tainted 5.3.0-rc7+ #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 01/01/2011 Call Trace: __dump_stack lib/dump_stack.c:77 [inline] dump_stack+0x191/0x1f0 lib/dump_stack.c:113 kmsan_report+0x13a/0x2b0 mm/kmsan/kmsan_report.c:108 __msan_warning+0x73/0xe0 mm/kmsan/kmsan_instr.c:250 alauda_check_media+0x344/0x3310 drivers/usb/storage/alauda.c:460 The problem is that alauda_check_media() doesn't verify that its USB transfer succeeded before trying to use the received data. What should happen if the transfer fails isn't entirely clear, but a reasonably conservative approach is to pretend that no media is present. A similar problem exists in a usb_stor_dbg() call in alauda_get_media_status(). In this case, when an error occurs the call is redundant, because usb_stor_ctrl_transfer() already will print a debugging message. Finally, unrelated to the uninitialized memory access, is the fact that alauda_check_media() performs DMA to a buffer on the stack. Fortunately usb-storage provides a general purpose DMA-able buffer for uses like this. We'll use it instead.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53848 In the Linux kernel, the following vulnerability has been resolved: md/raid5-cache: fix a deadlock in r5l_exit_log() Commit b13015af94cf ("md/raid5-cache: Clear conf->log after finishing work") introduce a new problem: // caller hold reconfig_mutex r5l_exit_log flush_work(&log->disable_writeback_work) r5c_disable_writeback_async wait_event /* * conf->log is not NULL, and mddev_trylock() * will fail, wait_event() can never pass. */ conf->log = NULL Fix this problem by setting 'config->log' to NULL before wake_up() as it used to be, so that wait_event() from r5c_disable_writeback_async() can exist. In the meantime, move forward md_unregister_thread() so that null-ptr-deref this commit fixed can still be fixed.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-53999 In the Linux kernel, the following vulnerability has been resolved: net/mlx5e: TC, Fix internal port memory leak The flow rule can be splited, and the extra post_act rules are added to post_act table. It's possible to trigger memleak when the rule forwards packets from internal port and over tunnel, in the case that, for example, CT 'new' state offload is allowed. As int_port object is assigned to the flow attribute of post_act rule, and its refcnt is incremented by mlx5e_tc_int_port_get(), but mlx5e_tc_int_port_put() is not called, the refcnt is never decremented, then int_port is never freed. The kmemleak reports the following error: unreferenced object 0xffff888128204b80 (size 64): comm "handler20", pid 50121, jiffies 4296973009 (age 642.932s) hex dump (first 32 bytes): 01 00 00 00 19 00 00 00 03 f0 00 00 04 00 00 00 ................ 98 77 67 41 81 88 ff ff 98 77 67 41 81 88 ff ff .wgA.....wgA.... backtrace: [<00000000e992680d>] kmalloc_trace+0x27/0x120 [<000000009e945a98>] mlx5e_tc_int_port_get+0x3f3/0xe20 [mlx5_core] [<0000000035a537f0>] mlx5e_tc_add_fdb_flow+0x473/0xcf0 [mlx5_core] [<0000000070c2cec6>] __mlx5e_add_fdb_flow+0x7cf/0xe90 [mlx5_core] [<000000005cc84048>] mlx5e_configure_flower+0xd40/0x4c40 [mlx5_core] [<000000004f8a2031>] mlx5e_rep_indr_offload.isra.0+0x10e/0x1c0 [mlx5_core] [<000000007df797dc>] mlx5e_rep_indr_setup_tc_cb+0x90/0x130 [mlx5_core] [<0000000016c15cc3>] tc_setup_cb_add+0x1cf/0x410 [<00000000a63305b4>] fl_hw_replace_filter+0x38f/0x670 [cls_flower] [<000000008bc9e77c>] fl_change+0x1fd5/0x4430 [cls_flower] [<00000000e7f766e4>] tc_new_tfilter+0x867/0x2010 [<00000000e101c0ef>] rtnetlink_rcv_msg+0x6fc/0x9f0 [<00000000e1111d44>] netlink_rcv_skb+0x12c/0x360 [<0000000082dd6c8b>] netlink_unicast+0x438/0x710 [<00000000fc568f70>] netlink_sendmsg+0x794/0xc50 [<0000000016e92590>] sock_sendmsg+0xc5/0x190 So fix this by moving int_port cleanup code to the flow attribute free helper, which is used by all the attribute free cases.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-54040 In the Linux kernel, the following vulnerability has been resolved: ice: fix wrong fallback logic for FDIR When adding a FDIR filter, if ice_vc_fdir_set_irq_ctx returns failure, the inserted fdir entry will not be removed and if ice_vc_fdir_write_fltr returns failure, the fdir context info for irq handler will not be cleared which may lead to inconsistent or memory leak issue. This patch refines failure cases to resolve this issue.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-54125 In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: Return error for inconsistent extended attributes ntfs_read_ea is called when we want to read extended attributes. There are some sanity checks for the validity of the EAs. However, it fails to return a proper error code for the inconsistent attributes, which might lead to unpredicted memory accesses after return. [ 138.916927] BUG: KASAN: use-after-free in ntfs_set_ea+0x453/0xbf0 [ 138.923876] Write of size 4 at addr ffff88800205cfac by task poc/199 [ 138.931132] [ 138.933016] CPU: 0 PID: 199 Comm: poc Not tainted 6.2.0-rc1+ #4 [ 138.938070] Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS rel-1.16.0-0-gd239552ce722-prebuilt.qemu.org 04/01/2014 [ 138.947327] Call Trace: [ 138.949557] <TASK> [ 138.951539] dump_stack_lvl+0x4d/0x67 [ 138.956834] print_report+0x16f/0x4a6 [ 138.960798] ? ntfs_set_ea+0x453/0xbf0 [ 138.964437] ? kasan_complete_mode_report_info+0x7d/0x200 [ 138.969793] ? ntfs_set_ea+0x453/0xbf0 [ 138.973523] kasan_report+0xb8/0x140 [ 138.976740] ? ntfs_set_ea+0x453/0xbf0 [ 138.980578] __asan_store4+0x76/0xa0 [ 138.984669] ntfs_set_ea+0x453/0xbf0 [ 138.988115] ? __pfx_ntfs_set_ea+0x10/0x10 [ 138.993390] ? kernel_text_address+0xd3/0xe0 [ 138.998270] ? __kernel_text_address+0x16/0x50 [ 139.002121] ? unwind_get_return_address+0x3e/0x60 [ 139.005659] ? __pfx_stack_trace_consume_entry+0x10/0x10 [ 139.010177] ? arch_stack_walk+0xa2/0x100 [ 139.013657] ? filter_irq_stacks+0x27/0x80 [ 139.017018] ntfs_setxattr+0x405/0x440 [ 139.022151] ? __pfx_ntfs_setxattr+0x10/0x10 [ 139.026569] ? kvmalloc_node+0x2d/0x120 [ 139.030329] ? kasan_save_stack+0x41/0x60 [ 139.033883] ? kasan_save_stack+0x2a/0x60 [ 139.037338] ? kasan_set_track+0x29/0x40 [ 139.040163] ? kasan_save_alloc_info+0x1f/0x30 [ 139.043588] ? __kasan_kmalloc+0x8b/0xa0 [ 139.047255] ? __kmalloc_node+0x68/0x150 [ 139.051264] ? kvmalloc_node+0x2d/0x120 [ 139.055301] ? vmemdup_user+0x2b/0xa0 [ 139.058584] __vfs_setxattr+0x121/0x170 [ 139.062617] ? __pfx___vfs_setxattr+0x10/0x10 [ 139.066282] __vfs_setxattr_noperm+0x97/0x300 [ 139.070061] __vfs_setxattr_locked+0x145/0x170 [ 139.073580] vfs_setxattr+0x137/0x2a0 [ 139.076641] ? __pfx_vfs_setxattr+0x10/0x10 [ 139.080223] ? __kasan_check_write+0x18/0x20 [ 139.084234] do_setxattr+0xce/0x150 [ 139.087768] setxattr+0x126/0x140 [ 139.091250] ? __pfx_setxattr+0x10/0x10 [ 139.094948] ? __virt_addr_valid+0xcb/0x140 [ 139.097838] ? __call_rcu_common.constprop.0+0x1c7/0x330 [ 139.102688] ? debug_smp_processor_id+0x1b/0x30 [ 139.105985] ? kasan_quarantine_put+0x5b/0x190 [ 139.109980] ? putname+0x84/0xa0 [ 139.113886] ? __kasan_slab_free+0x11e/0x1b0 [ 139.117961] ? putname+0x84/0xa0 [ 139.121316] ? preempt_count_sub+0x1c/0xd0 [ 139.124427] ? __mnt_want_write+0xae/0x100 [ 139.127836] ? mnt_want_write+0x8f/0x150 [ 139.130954] path_setxattr+0x164/0x180 [ 139.133998] ? __pfx_path_setxattr+0x10/0x10 [ 139.137853] ? __pfx_ksys_pwrite64+0x10/0x10 [ 139.141299] ? debug_smp_processor_id+0x1b/0x30 [ 139.145714] ? fpregs_assert_state_consistent+0x6b/0x80 [ 139.150796] __x64_sys_setxattr+0x71/0x90 [ 139.155407] do_syscall_64+0x3f/0x90 [ 139.159035] entry_SYSCALL_64_after_hwframe+0x72/0xdc [ 139.163843] RIP: 0033:0x7f108cae4469 [ 139.166481] Code: 00 f3 c3 66 2e 0f 1f 84 00 00 00 00 00 0f 1f 40 00 48 89 f8 48 89 f7 48 89 d6 48 89 ca 4d 89 c2 4d 89 c8 4c 8b 4c 24 088 [ 139.183764] RSP: 002b:00007fff87588388 EFLAGS: 00000286 ORIG_RAX: 00000000000000bc [ 139.190657] RAX: ffffffffffffffda RBX: 0000000000000000 RCX: 00007f108cae4469 [ 139.196586] RDX: 00007fff875883b0 RSI: 00007fff875883d1 RDI: 00007fff875883b6 [ 139.201716] RBP: 00007fff8758c530 R08: 0000000000000001 R09: 00007fff8758c618 [ 139.207940] R10: 0000000000000006 R11: 0000000000000286 R12: 00000000004004c0 [ 139.214007] R13: 00007fff8758c610 R14: 0000000000000000 R15 ---truncated---

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2023-54238 In the Linux kernel, the following vulnerability has been resolved: mlx5: fix skb leak while fifo resync and push During ptp resync operation SKBs were poped from the fifo but were never freed neither by napi_consume nor by dev_kfree_skb_any. Add call to napi_consume_skb to properly free SKBs. Another leak was happening because mlx5e_skb_fifo_has_room() had an error in the check. Comparing free running counters works well unless C promotes the types to something wider than the counter. In this case counters are u16 but the result of the substraction is promouted to int and it causes wrong result (negative value) of the check when producer have already overlapped but consumer haven't yet. Explicit cast to u16 fixes the issue.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2023-54285 In the Linux kernel, the following vulnerability has been resolved: iomap: Fix possible overflow condition in iomap_write_delalloc_scan folio_next_index() returns an unsigned long value which left shifted by PAGE_SHIFT could possibly cause an overflow on 32-bit system. Instead use folio_pos(folio) + folio_size(folio), which does this correctly.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-0553 A vulnerability was found in GnuTLS. The response times to malformed ciphertexts in RSA-PSK ClientKeyExchange differ from the response times of ciphertexts with correct PKCS#1 v1.5 padding. This issue may allow a remote attacker to perform a timing side-channel attack in the RSA-PSK key exchange, potentially leading to the leakage of sensitive data. CVE-2024-0553 is designated as an incomplete resolution for CVE-2023-5981.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-2961 The iconv() function in the GNU C Library versions 2.39 and older may overflow the output buffer passed to it by up to 4 bytes when converting strings to the ISO-2022-CN-EXT character set, which may be used to crash an application or overwrite a neighbouring variable.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-3596 RADIUS Protocol under RFC 2865 is susceptible to forgery attacks by a local attacker who can modify any valid Response (Access-Accept, Access-Reject, or Access-Challenge) to any other response using a chosen-prefix collision attack against MD5 Response Authenticator signature.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2024-3651 A vulnerability was identified in the kjd/idna library, specifically within the `idna.encode()` function, affecting version 3.6. The issue arises from the function's handling of crafted input strings, which can lead to quadratic complexity and consequently, a denial of service condition. This vulnerability is triggered by a crafted input that causes the `idna.encode()` function to process the input with considerable computational load, significantly increasing the processing time in a quadratic manner relative to the input size.

nim-mit-boltz2-v1.3.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2024-5569 A Denial of Service (DoS) vulnerability exists in the jaraco/zipp library, affecting all versions prior to 3.19.1. The vulnerability is triggered when processing a specially crafted zip file that leads to an infinite loop. This issue also impacts the zipfile module of CPython, as features from the third-party zipp library are later merged into CPython, and the affected code is identical in both projects. The infinite loop can be initiated through the use of functions affecting the `Path` module in both zipp and zipfile, such as `joinpath`, the overloaded division operator, and `iterdir`. Although the infinite loop is not resource exhaustive, it prevents the application from responding. The vulnerability was addressed in version 3.19.1 of jaraco/zipp.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-12718 Allows modifying some file metadata (e.g. last modified) with filter="data" or file permissions (chmod) with filter="tar" of files outside the extraction directory. You are affected by this vulnerability if using the tarfile module to extract untrusted tar archives using TarFile.extractall() or TarFile.extract() using the filter= parameter with a value of "data" or "tar". See the tarfile extraction filters documentation https://docs.python.org/3/library/tarfile.html#tarfile-extraction-filter  for more information. Only Python versions 3.12 or later are affected by these vulnerabilities, earlier versions don't include the extraction filter feature. Note that for Python 3.14 or later the default value of filter= changed from "no filtering" to `"data", so if you are relying on this new default behavior then your usage is also affected. Note that none of these vulnerabilities significantly affect the installation of source distributions which are tar archives as source distributions already allow arbitrary code execution during the build process. However when evaluating source distributions it's important to avoid installing source distributions with suspicious links.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-21503 Versions of the package black before 24.3.0 are vulnerable to Regular Expression Denial of Service (ReDoS) via the lines_with_leading_tabs_expanded function in the strings.py file. An attacker could exploit this vulnerability by crafting a malicious input that causes a denial of service. Exploiting this vulnerability is possible when running Black on untrusted input, or if you habitually put thousands of leading tab characters in your docstrings.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2024-22365 linux-pam (aka Linux PAM) before 1.6.0 allows attackers to cause a denial of service (blocked login process) via mkfifo because the openat call (for protect_dir) lacks O_DIRECTORY.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-22513 djangorestframework-simplejwt version 5.3.1 and before is vulnerable to information disclosure. A user can access web application resources even after their account has been disabled due to missing user validation checks via the for_user method.

hue

CVE-2024-23334 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. When using aiohttp as a web server and configuring static routes, it is necessary to specify the root path for static files. Additionally, the option 'follow_symlinks' can be used to determine whether to follow symbolic links outside the static root directory. When 'follow_symlinks' is set to True, there is no validation to check if reading a file is within the root directory. This can lead to directory traversal vulnerabilities, resulting in unauthorized access to arbitrary files on the system, even when symlinks are not present. Disabling follow_symlinks and using a reverse proxy are encouraged mitigations. Version 3.9.2 fixes this issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-23337 jq is a command-line JSON processor. In versions up to and including 1.7.1, an integer overflow arises when assigning value using an index of 2147483647, the signed integer limit. This causes a denial of service. Commit de21386681c0df0104a99d9d09db23a9b2a78b1e contains a patch for the issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2024-23829 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. Security-sensitive parts of the Python HTTP parser retained minor differences in allowable character sets, that must trigger error handling to robustly match frame boundaries of proxies in order to protect against injection of additional requests. Additionally, validation could trigger exceptions that were not handled consistently with processing of other malformed input. Being more lenient than internet standards require could, depending on deployment environment, assist in request smuggling. The unhandled exception could cause excessive resource consumption on the application server and/or its logging facilities. This vulnerability exists due to an incomplete fix for CVE-2023-47627. Version 3.9.2 fixes this vulnerability.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-25638 dnsjava is an implementation of DNS in Java. Records in DNS replies are not checked for their relevance to the query, allowing an attacker to respond with RRs from different zones. This vulnerability is fixed in 3.6.0.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-26130 cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. Starting in version 38.0.0 and prior to version 42.0.4, if `pkcs12.serialize_key_and_certificates` is called with both a certificate whose public key did not match the provided private key and an `encryption_algorithm` with `hmac_hash` set (via `PrivateFormat.PKCS12.encryption_builder().hmac_hash(...)`, then a NULL pointer dereference would occur, crashing the Python process. This has been resolved in version 42.0.4, the first version in which a `ValueError` is properly raised.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
hue

CVE-2024-26458 Kerberos 5 (aka krb5) 1.21.2 contains a memory leak in /krb5/src/lib/rpc/pmap_rmt.c.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2024-26461 Kerberos 5 (aka krb5) 1.21.2 contains a memory leak vulnerability in /krb5/src/lib/gssapi/krb5/k5sealv3.c.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2024-26462 Kerberos 5 (aka krb5) 1.21.2 contains a memory leak vulnerability in /krb5/src/kdc/ndr.c.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2024-26782 In the Linux kernel, the following vulnerability has been resolved: mptcp: fix double-free on socket dismantle when MPTCP server accepts an incoming connection, it clones its listener socket. However, the pointer to 'inet_opt' for the new socket has the same value as the original one: as a consequence, on program exit it's possible to observe the following splat: BUG: KASAN: double-free in inet_sock_destruct+0x54f/0x8b0 Free of addr ffff888485950880 by task swapper/25/0 CPU: 25 PID: 0 Comm: swapper/25 Kdump: loaded Not tainted 6.8.0-rc1+ #609 Hardware name: Supermicro SYS-6027R-72RF/X9DRH-7TF/7F/iTF/iF, BIOS 3.0 07/26/2013 Call Trace: <IRQ> dump_stack_lvl+0x32/0x50 print_report+0xca/0x620 kasan_report_invalid_free+0x64/0x90 __kasan_slab_free+0x1aa/0x1f0 kfree+0xed/0x2e0 inet_sock_destruct+0x54f/0x8b0 __sk_destruct+0x48/0x5b0 rcu_do_batch+0x34e/0xd90 rcu_core+0x559/0xac0 __do_softirq+0x183/0x5a4 irq_exit_rcu+0x12d/0x170 sysvec_apic_timer_interrupt+0x6b/0x80 </IRQ> <TASK> asm_sysvec_apic_timer_interrupt+0x16/0x20 RIP: 0010:cpuidle_enter_state+0x175/0x300 Code: 30 00 0f 84 1f 01 00 00 83 e8 01 83 f8 ff 75 e5 48 83 c4 18 44 89 e8 5b 5d 41 5c 41 5d 41 5e 41 5f c3 cc cc cc cc fb 45 85 ed <0f> 89 60 ff ff ff 48 c1 e5 06 48 c7 43 18 00 00 00 00 48 83 44 2b RSP: 0018:ffff888481cf7d90 EFLAGS: 00000202 RAX: 0000000000000000 RBX: ffff88887facddc8 RCX: 0000000000000000 RDX: 1ffff1110ff588b1 RSI: 0000000000000019 RDI: ffff88887fac4588 RBP: 0000000000000004 R08: 0000000000000002 R09: 0000000000043080 R10: 0009b02ea273363f R11: ffff88887fabf42b R12: ffffffff932592e0 R13: 0000000000000004 R14: 0000000000000000 R15: 00000022c880ec80 cpuidle_enter+0x4a/0xa0 do_idle+0x310/0x410 cpu_startup_entry+0x51/0x60 start_secondary+0x211/0x270 secondary_startup_64_no_verify+0x184/0x18b </TASK> Allocated by task 6853: kasan_save_stack+0x1c/0x40 kasan_save_track+0x10/0x30 __kasan_kmalloc+0xa6/0xb0 __kmalloc+0x1eb/0x450 cipso_v4_sock_setattr+0x96/0x360 netlbl_sock_setattr+0x132/0x1f0 selinux_netlbl_socket_post_create+0x6c/0x110 selinux_socket_post_create+0x37b/0x7f0 security_socket_post_create+0x63/0xb0 __sock_create+0x305/0x450 __sys_socket_create.part.23+0xbd/0x130 __sys_socket+0x37/0xb0 __x64_sys_socket+0x6f/0xb0 do_syscall_64+0x83/0x160 entry_SYSCALL_64_after_hwframe+0x6e/0x76 Freed by task 6858: kasan_save_stack+0x1c/0x40 kasan_save_track+0x10/0x30 kasan_save_free_info+0x3b/0x60 __kasan_slab_free+0x12c/0x1f0 kfree+0xed/0x2e0 inet_sock_destruct+0x54f/0x8b0 __sk_destruct+0x48/0x5b0 subflow_ulp_release+0x1f0/0x250 tcp_cleanup_ulp+0x6e/0x110 tcp_v4_destroy_sock+0x5a/0x3a0 inet_csk_destroy_sock+0x135/0x390 tcp_fin+0x416/0x5c0 tcp_data_queue+0x1bc8/0x4310 tcp_rcv_state_process+0x15a3/0x47b0 tcp_v4_do_rcv+0x2c1/0x990 tcp_v4_rcv+0x41fb/0x5ed0 ip_protocol_deliver_rcu+0x6d/0x9f0 ip_local_deliver_finish+0x278/0x360 ip_local_deliver+0x182/0x2c0 ip_rcv+0xb5/0x1c0 __netif_receive_skb_one_core+0x16e/0x1b0 process_backlog+0x1e3/0x650 __napi_poll+0xa6/0x500 net_rx_action+0x740/0xbb0 __do_softirq+0x183/0x5a4 The buggy address belongs to the object at ffff888485950880 which belongs to the cache kmalloc-64 of size 64 The buggy address is located 0 bytes inside of 64-byte region [ffff888485950880, ffff8884859508c0) The buggy address belongs to the physical page: page:0000000056d1e95e refcount:1 mapcount:0 mapping:0000000000000000 index:0xffff888485950700 pfn:0x485950 flags: 0x57ffffc0000800(slab|node=1|zone=2|lastcpupid=0x1fffff) page_type: 0xffffffff() raw: 0057ffffc0000800 ffff88810004c640 ffffea00121b8ac0 dead000000000006 raw: ffff888485950700 0000000000200019 00000001ffffffff 0000000000000000 page dumped because: kasan: bad access detected Memory state around the buggy address: ffff888485950780: fa fb fb ---truncated---

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-26931 In the Linux kernel, the following vulnerability has been resolved: scsi: qla2xxx: Fix command flush on cable pull System crash due to command failed to flush back to SCSI layer. BUG: unable to handle kernel NULL pointer dereference at 0000000000000000 PGD 0 P4D 0 Oops: 0000 [#1] SMP NOPTI CPU: 27 PID: 793455 Comm: kworker/u130:6 Kdump: loaded Tainted: G OE --------- - - 4.18.0-372.9.1.el8.x86_64 #1 Hardware name: HPE ProLiant DL360 Gen10/ProLiant DL360 Gen10, BIOS U32 09/03/2021 Workqueue: nvme-wq nvme_fc_connect_ctrl_work [nvme_fc] RIP: 0010:__wake_up_common+0x4c/0x190 Code: 24 10 4d 85 c9 74 0a 41 f6 01 04 0f 85 9d 00 00 00 48 8b 43 08 48 83 c3 08 4c 8d 48 e8 49 8d 41 18 48 39 c3 0f 84 f0 00 00 00 <49> 8b 41 18 89 54 24 08 31 ed 4c 8d 70 e8 45 8b 29 41 f6 c5 04 75 RSP: 0018:ffff95f3e0cb7cd0 EFLAGS: 00010086 RAX: 0000000000000000 RBX: ffff8b08d3b26328 RCX: 0000000000000000 RDX: 0000000000000001 RSI: 0000000000000003 RDI: ffff8b08d3b26320 RBP: 0000000000000001 R08: 0000000000000000 R09: ffffffffffffffe8 R10: 0000000000000000 R11: ffff95f3e0cb7a60 R12: ffff95f3e0cb7d20 R13: 0000000000000003 R14: 0000000000000000 R15: 0000000000000000 FS: 0000000000000000(0000) GS:ffff8b2fdf6c0000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000000000000000 CR3: 0000002f1e410002 CR4: 00000000007706e0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 PKRU: 55555554 Call Trace: __wake_up_common_lock+0x7c/0xc0 qla_nvme_ls_req+0x355/0x4c0 [qla2xxx] qla2xxx [0000:12:00.1]-f084:3: qlt_free_session_done: se_sess 0000000000000000 / sess ffff8ae1407ca000 from port 21:32:00:02:ac:07:ee:b8 loop_id 0x02 s_id 01:02:00 logout 1 keep 0 els_logo 0 ? __nvme_fc_send_ls_req+0x260/0x380 [nvme_fc] qla2xxx [0000:12:00.1]-207d:3: FCPort 21:32:00:02:ac:07:ee:b8 state transitioned from ONLINE to LOST - portid=010200. ? nvme_fc_send_ls_req.constprop.42+0x1a/0x45 [nvme_fc] qla2xxx [0000:12:00.1]-2109:3: qla2x00_schedule_rport_del 21320002ac07eeb8. rport ffff8ae598122000 roles 1 ? nvme_fc_connect_ctrl_work.cold.63+0x1e3/0xa7d [nvme_fc] qla2xxx [0000:12:00.1]-f084:3: qlt_free_session_done: se_sess 0000000000000000 / sess ffff8ae14801e000 from port 21:32:01:02:ad:f7:ee:b8 loop_id 0x04 s_id 01:02:01 logout 1 keep 0 els_logo 0 ? __switch_to+0x10c/0x450 ? process_one_work+0x1a7/0x360 qla2xxx [0000:12:00.1]-207d:3: FCPort 21:32:01:02:ad:f7:ee:b8 state transitioned from ONLINE to LOST - portid=010201. ? worker_thread+0x1ce/0x390 ? create_worker+0x1a0/0x1a0 qla2xxx [0000:12:00.1]-2109:3: qla2x00_schedule_rport_del 21320102adf7eeb8. rport ffff8ae3b2312800 roles 70 ? kthread+0x10a/0x120 qla2xxx [0000:12:00.1]-2112:3: qla_nvme_unregister_remote_port: unregister remoteport on ffff8ae14801e000 21320102adf7eeb8 ? set_kthread_struct+0x40/0x40 qla2xxx [0000:12:00.1]-2110:3: remoteport_delete of ffff8ae14801e000 21320102adf7eeb8 completed. ? ret_from_fork+0x1f/0x40 qla2xxx [0000:12:00.1]-f086:3: qlt_free_session_done: waiting for sess ffff8ae14801e000 logout The system was under memory stress where driver was not able to allocate an SRB to carry out error recovery of cable pull. The failure to flush causes upper layer to start modifying scsi_cmnd. When the system frees up some memory, the subsequent cable pull trigger another command flush. At this point the driver access a null pointer when attempting to DMA unmap the SGL. Add a check to make sure commands are flush back on session tear down to prevent the null pointer access.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-27306 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. A XSS vulnerability exists on index pages for static file handling. This vulnerability is fixed in 3.9.4. We have always recommended using a reverse proxy server (e.g. nginx) for serving static files. Users following the recommendation are unaffected. Other users can disable `show_index` if unable to upgrade.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-28085 wall in util-linux through 2.40, often installed with setgid tty permissions, allows escape sequences to be sent to other users' terminals through argv. (Specifically, escape sequences received from stdin are blocked, but escape sequences received from argv are not blocked.) There may be plausible scenarios where this leads to account takeover.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-28834 A flaw was found in GnuTLS. The Minerva attack is a cryptographic vulnerability that exploits deterministic behavior in systems like GnuTLS, leading to side-channel leaks. In specific scenarios, such as when using the GNUTLS_PRIVKEY_FLAG_REPRODUCIBLE flag, it can result in a noticeable step in nonce size from 513 to 512 bits, exposing a potential timing side-channel.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2024-29857 An issue was discovered in ECCurve.java and ECCurve.cs in Bouncy Castle Java (BC Java) before 1.78, BC Java LTS before 2.73.6, BC-FJA before 1.0.2.5, and BC C# .Net before 2.3.1. Importing an EC certificate with crafted F2m parameters can lead to excessive CPU consumption during the evaluation of the curve parameters.

thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2024-30171 An issue was discovered in Bouncy Castle Java TLS API and JSSE Provider before 1.78. Timing-based leakage may occur in RSA based handshakes because of exception processing.

thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2024-30172 An issue was discovered in Bouncy Castle Java Cryptography APIs before 1.78. An Ed25519 verification code infinite loop can occur via a crafted signature and public key.

thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2024-31033 JJWT (aka Java JWT) through 0.12.5 ignores certain characters and thus a user might falsely conclude that they have a strong key. The impacted code is the setSigningKey() method within the DefaultJwtParser class and the signWith() method within the DefaultJwtBuilder class. NOTE: the vendor disputes this because the "ignores" behavior cannot occur (in any version) unless there is a user error in how JJWT is used, and because the version that was actually tested must have been more than six years out of date.

dmx-app

CVE-2024-34447 An issue was discovered in the Bouncy Castle Crypto Package For Java before BC TLS Java 1.0.19 (ships with BC Java 1.78, BC Java (LTS) 2.73.6) and before BC FIPS TLS Java 1.0.19. When endpoint identification is enabled in the BCJSSE and an SSL socket is created without an explicit hostname (as happens with HttpsURLConnection), hostname verification could be performed against a DNS-resolved IP address in some situations, opening up a possibility of DNS poisoning.

thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2024-36476 In the Linux kernel, the following vulnerability has been resolved: RDMA/rtrs: Ensure 'ib_sge list' is accessible Move the declaration of the 'ib_sge list' variable outside the 'always_invalidate' block to ensure it remains accessible for use throughout the function. Previously, 'ib_sge list' was declared within the 'always_invalidate' block, limiting its accessibility, then caused a 'BUG: kernel NULL pointer dereference'[1]. ? __die_body.cold+0x19/0x27 ? page_fault_oops+0x15a/0x2d0 ? search_module_extables+0x19/0x60 ? search_bpf_extables+0x5f/0x80 ? exc_page_fault+0x7e/0x180 ? asm_exc_page_fault+0x26/0x30 ? memcpy_orig+0xd5/0x140 rxe_mr_copy+0x1c3/0x200 [rdma_rxe] ? rxe_pool_get_index+0x4b/0x80 [rdma_rxe] copy_data+0xa5/0x230 [rdma_rxe] rxe_requester+0xd9b/0xf70 [rdma_rxe] ? finish_task_switch.isra.0+0x99/0x2e0 rxe_sender+0x13/0x40 [rdma_rxe] do_task+0x68/0x1e0 [rdma_rxe] process_one_work+0x177/0x330 worker_thread+0x252/0x390 ? __pfx_worker_thread+0x10/0x10 This change ensures the variable is available for subsequent operations that require it. [1] https://lore.kernel.org/linux-rdma/6a1f3e8f-deb0-49f9-bc69-a9b03ecfcda7@fujitsu.com/

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-37891 urllib3 is a user-friendly HTTP client library for Python. When using urllib3's proxy support with `ProxyManager`, the `Proxy-Authorization` header is only sent to the configured proxy, as expected. However, when sending HTTP requests *without* using urllib3's proxy support, it's possible to accidentally configure the `Proxy-Authorization` header even though it won't have any effect as the request is not using a forwarding proxy or a tunneling proxy. In those cases, urllib3 doesn't treat the `Proxy-Authorization` HTTP header as one carrying authentication material and thus doesn't strip the header on cross-origin redirects. Because this is a highly unlikely scenario, we believe the severity of this vulnerability is low for almost all users. Out of an abundance of caution urllib3 will automatically strip the `Proxy-Authorization` header during cross-origin redirects to avoid the small chance that users are doing this on accident. Users should use urllib3's proxy support or disable automatic redirects to achieve safe processing of the `Proxy-Authorization` header, but we still decided to strip the header by default in order to further protect users who aren't using the correct approach. We believe the number of usages affected by this advisory is low. It requires all of the following to be true to be exploited: 1. Setting the `Proxy-Authorization` header without using urllib3's built-in proxy support. 2. Not disabling HTTP redirects. 3. Either not using an HTTPS origin server or for the proxy or target origin to redirect to a malicious origin. Users are advised to update to either version 1.26.19 or version 2.2.2. Users unable to upgrade may use the `Proxy-Authorization` header with urllib3's `ProxyManager`, disable HTTP redirects using `redirects=False` when sending requests, or not user the `Proxy-Authorization` header as mitigations.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-38517 Tencent RapidJSON is vulnerable to privilege escalation due to an integer underflow in the `GenericReader::ParseNumber()` function of `include/rapidjson/reader.h` when parsing JSON text from a stream. An attacker needs to send the victim a crafted file which needs to be opened; this triggers the integer underflow vulnerability (when the file is parsed), leading to elevation of privilege.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2024-38827 The usage of String.toLowerCase() and String.toUpperCase() has some Locale dependent exceptions that could potentially result in authorization rules not working properly.

cdc-profilers
cdc_profilers
dex-airflow-7.3.1.709
dex-airflow-api-server-7.3.1.709
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-runtime-3.5.4-7.3.2.0
dex-livy-server-3.5.4-7.3.1.709
dex-livy-server-3.5.4-7.3.2.0
dex-runtime-airflow-python-builder-7.3.1.709
dex-spark-history-server-3.5.4-7.3.1.709
dex-spark-runtime-3.5.4-7.3.1.709

CVE-2024-38949 Heap Buffer Overflow vulnerability in Libde265 v1.0.15 allows attackers to crash the application via crafted payload to display444as420 function at sdl.cc

ml-runtime-pbj-workbench-r4.5-standard

CVE-2024-38950 Heap Buffer Overflow vulnerability in Libde265 v1.0.15 allows attackers to crash the application via crafted payload to __interceptor_memcpy function.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2024-39282 In the Linux kernel, the following vulnerability has been resolved: net: wwan: t7xx: Fix FSM command timeout issue When driver processes the internal state change command, it use an asynchronous thread to process the command operation. If the main thread detects that the task has timed out, the asynchronous thread will panic when executing the completion notification because the main thread completion object has been released. BUG: unable to handle page fault for address: fffffffffffffff8 PGD 1f283a067 P4D 1f283a067 PUD 1f283c067 PMD 0 Oops: 0000 [#1] PREEMPT SMP NOPTI RIP: 0010:complete_all+0x3e/0xa0 [...] Call Trace: <TASK> ? __die_body+0x68/0xb0 ? page_fault_oops+0x379/0x3e0 ? exc_page_fault+0x69/0xa0 ? asm_exc_page_fault+0x22/0x30 ? complete_all+0x3e/0xa0 fsm_main_thread+0xa3/0x9c0 [mtk_t7xx (HASH:1400 5)] ? __pfx_autoremove_wake_function+0x10/0x10 kthread+0xd8/0x110 ? __pfx_fsm_main_thread+0x10/0x10 [mtk_t7xx (HASH:1400 5)] ? __pfx_kthread+0x10/0x10 ret_from_fork+0x38/0x50 ? __pfx_kthread+0x10/0x10 ret_from_fork_asm+0x1b/0x30 </TASK> [...] CR2: fffffffffffffff8 ---[ end trace 0000000000000000 ]--- Use the reference counter to ensure safe release as Sergey suggests: https://lore.kernel.org/all/da90f64c-260a-4329-87bf-1f9ff20a5951@gmail.com/

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-39684 Tencent RapidJSON is vulnerable to privilege escalation due to an integer overflow in the `GenericReader::ParseNumber()` function of `include/rapidjson/reader.h` when parsing JSON text from a stream. An attacker needs to send the victim a crafted file which needs to be opened; this triggers the integer overflow vulnerability (when the file is parsed), leading to elevation of privilege.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2024-42159 In the Linux kernel, the following vulnerability has been resolved: scsi: mpi3mr: Sanitise num_phys Information is stored in mr_sas_port->phy_mask, values larger then size of this field shouldn't be allowed.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-42367 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. In versions on the 3.10 branch prior to version 3.10.2, static routes which contain files with compressed variants (`.gz` or `.br` extension) are vulnerable to path traversal outside the root directory if those variants are symbolic links. The server protects static routes from path traversal outside the root directory when `follow_symlinks=False` (default). It does this by resolving the requested URL to an absolute path and then checking that path relative to the root. However, these checks are not performed when looking for compressed variants in the `FileResponse` class, and symbolic links are then automatically followed when performing the `Path.stat()` and `Path.open()` to send the file. Version 3.10.2 contains a patch for the issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-49887 In the Linux kernel, the following vulnerability has been resolved: f2fs: fix to don't panic system for no free segment fault injection f2fs: fix to don't panic system for no free segment fault injection syzbot reports a f2fs bug as below: F2FS-fs (loop0): inject no free segment in get_new_segment of __allocate_new_segment+0x1ce/0x940 fs/f2fs/segment.c:3167 F2FS-fs (loop0): Stopped filesystem due to reason: 7 ------------[ cut here ]------------ kernel BUG at fs/f2fs/segment.c:2748! CPU: 0 UID: 0 PID: 5109 Comm: syz-executor304 Not tainted 6.11.0-rc6-syzkaller-00363-g89f5e14d05b4 #0 RIP: 0010:get_new_segment fs/f2fs/segment.c:2748 [inline] RIP: 0010:new_curseg+0x1f61/0x1f70 fs/f2fs/segment.c:2836 Call Trace: __allocate_new_segment+0x1ce/0x940 fs/f2fs/segment.c:3167 f2fs_allocate_new_section fs/f2fs/segment.c:3181 [inline] f2fs_allocate_pinning_section+0xfa/0x4e0 fs/f2fs/segment.c:3195 f2fs_expand_inode_data+0x5d6/0xbb0 fs/f2fs/file.c:1799 f2fs_fallocate+0x448/0x960 fs/f2fs/file.c:1903 vfs_fallocate+0x553/0x6c0 fs/open.c:334 do_vfs_ioctl+0x2592/0x2e50 fs/ioctl.c:886 __do_sys_ioctl fs/ioctl.c:905 [inline] __se_sys_ioctl+0x81/0x170 fs/ioctl.c:893 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0010:get_new_segment fs/f2fs/segment.c:2748 [inline] RIP: 0010:new_curseg+0x1f61/0x1f70 fs/f2fs/segment.c:2836 The root cause is when we inject no free segment fault into f2fs, we should not panic system, fix it.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-50157 In the Linux kernel, the following vulnerability has been resolved: RDMA/bnxt_re: Avoid CPU lockups due fifo occupancy check loop Driver waits indefinitely for the fifo occupancy to go below a threshold as soon as the pacing interrupt is received. This can cause soft lockup on one of the processors, if the rate of DB is very high. Add a loop count for FPGA and exit the __wait_for_fifo_occupancy_below_th if the loop is taking more time. Pacing will be continuing until the occupancy is below the threshold. This is ensured by the checks in bnxt_re_pacing_timer_exp and further scheduling the work for pacing based on the fifo occupancy.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-51744 golang-jwt is a Go implementation of JSON Web Tokens. Unclear documentation of the error behavior in `ParseWithClaims` can lead to situation where users are potentially not checking errors in the way they should be. Especially, if a token is both expired and invalid, the errors returned by `ParseWithClaims` return both error codes. If users only check for the `jwt.ErrTokenExpired ` using `error.Is`, they will ignore the embedded `jwt.ErrTokenSignatureInvalid` and thus potentially accept invalid tokens. A fix has been back-ported with the error handling logic from the `v5` branch to the `v4` branch. In this logic, the `ParseWithClaims` function will immediately return in "dangerous" situations (e.g., an invalid signature), limiting the combined errors only to situations where the signature is valid, but further validation failed (e.g., if the signature is valid, but is expired AND has the wrong audience). This fix is part of the 4.5.1 release. We are aware that this changes the behaviour of an established function and is not 100 % backwards compatible, so updating to 4.5.1 might break your code. In case you cannot update to 4.5.0, please make sure that you are properly checking for all errors ("dangerous" ones first), so that you are not running in the case detailed above.

traefik

CVE-2024-52003 Traefik (pronounced traffic) is an HTTP reverse proxy and load balancer. There is a vulnerability in Traefik that allows the client to provide the X-Forwarded-Prefix header from an untrusted source. This issue has been addressed in versions 2.11.14 and 3.2.1. Users are advised to upgrade. There are no known workarounds for this vulnerability.

traefik

CVE-2024-52303 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. In versions starting with 3.10.6 and prior to 3.10.11, a memory leak can occur when a request produces a MatchInfoError. This was caused by adding an entry to a cache on each request, due to the building of each MatchInfoError producing a unique cache entry. An attacker may be able to exhaust the memory resources of a server by sending a substantial number (100,000s to millions) of such requests. Those who use any middlewares with aiohttp.web should upgrade to version 3.10.11 to receive a patch.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-52304 aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. Prior to version 3.10.11, the Python parser parses newlines in chunk extensions incorrectly which can lead to request smuggling vulnerabilities under certain conditions. If a pure Python version of aiohttp is installed (i.e. without the usual C extensions) or `AIOHTTP_NO_EXTENSIONS` is enabled, then an attacker may be able to execute a request smuggling attack to bypass certain firewalls or proxy protections. Version 3.10.11 fixes the issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-53161 In the Linux kernel, the following vulnerability has been resolved: EDAC/bluefield: Fix potential integer overflow The 64-bit argument for the "get DIMM info" SMC call consists of mem_ctrl_idx left-shifted 16 bits and OR-ed with DIMM index. With mem_ctrl_idx defined as 32-bits wide the left-shift operation truncates the upper 16 bits of information during the calculation of the SMC argument. The mem_ctrl_idx stack variable must be defined as 64-bits wide to prevent any potential integer overflow, i.e. loss of data from upper 16 bits.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-53176 In the Linux kernel, the following vulnerability has been resolved: smb: During unmount, ensure all cached dir instances drop their dentry The unmount process (cifs_kill_sb() calling close_all_cached_dirs()) can race with various cached directory operations, which ultimately results in dentries not being dropped and these kernel BUGs: BUG: Dentry ffff88814f37e358{i=1000000000080,n=/} still in use (2) [unmount of cifs cifs] VFS: Busy inodes after unmount of cifs (cifs) ------------[ cut here ]------------ kernel BUG at fs/super.c:661! This happens when a cfid is in the process of being cleaned up when, and has been removed from the cfids->entries list, including: - Receiving a lease break from the server - Server reconnection triggers invalidate_all_cached_dirs(), which removes all the cfids from the list - The laundromat thread decides to expire an old cfid. To solve these problems, dropping the dentry is done in queued work done in a newly-added cfid_put_wq workqueue, and close_all_cached_dirs() flushes that workqueue after it drops all the dentries of which it's aware. This is a global workqueue (rather than scoped to a mount), but the queued work is minimal. The final cleanup work for cleaning up a cfid is performed via work queued in the serverclose_wq workqueue; this is done separate from dropping the dentries so that close_all_cached_dirs() doesn't block on any server operations. Both of these queued works expect to invoked with a cfid reference and a tcon reference to avoid those objects from being freed while the work is ongoing. While we're here, add proper locking to close_all_cached_dirs(), and locking around the freeing of cfid->dentry.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-53177 In the Linux kernel, the following vulnerability has been resolved: smb: prevent use-after-free due to open_cached_dir error paths If open_cached_dir() encounters an error parsing the lease from the server, the error handling may race with receiving a lease break, resulting in open_cached_dir() freeing the cfid while the queued work is pending. Update open_cached_dir() to drop refs rather than directly freeing the cfid. Have cached_dir_lease_break(), cfids_laundromat_worker(), and invalidate_all_cached_dirs() clear has_lease immediately while still holding cfids->cfid_list_lock, and then use this to also simplify the reference counting in cfids_laundromat_worker() and invalidate_all_cached_dirs(). Fixes this KASAN splat (which manually injects an error and lease break in open_cached_dir()): ================================================================== BUG: KASAN: slab-use-after-free in smb2_cached_lease_break+0x27/0xb0 Read of size 8 at addr ffff88811cc24c10 by task kworker/3:1/65 CPU: 3 UID: 0 PID: 65 Comm: kworker/3:1 Not tainted 6.12.0-rc6-g255cf264e6e5-dirty #87 Hardware name: VMware, Inc. VMware Virtual Platform/440BX Desktop Reference Platform, BIOS 6.00 11/12/2020 Workqueue: cifsiod smb2_cached_lease_break Call Trace: <TASK> dump_stack_lvl+0x77/0xb0 print_report+0xce/0x660 kasan_report+0xd3/0x110 smb2_cached_lease_break+0x27/0xb0 process_one_work+0x50a/0xc50 worker_thread+0x2ba/0x530 kthread+0x17c/0x1c0 ret_from_fork+0x34/0x60 ret_from_fork_asm+0x1a/0x30 </TASK> Allocated by task 2464: kasan_save_stack+0x33/0x60 kasan_save_track+0x14/0x30 __kasan_kmalloc+0xaa/0xb0 open_cached_dir+0xa7d/0x1fb0 smb2_query_path_info+0x43c/0x6e0 cifs_get_fattr+0x346/0xf10 cifs_get_inode_info+0x157/0x210 cifs_revalidate_dentry_attr+0x2d1/0x460 cifs_getattr+0x173/0x470 vfs_statx_path+0x10f/0x160 vfs_statx+0xe9/0x150 vfs_fstatat+0x5e/0xc0 __do_sys_newfstatat+0x91/0xf0 do_syscall_64+0x95/0x1a0 entry_SYSCALL_64_after_hwframe+0x76/0x7e Freed by task 2464: kasan_save_stack+0x33/0x60 kasan_save_track+0x14/0x30 kasan_save_free_info+0x3b/0x60 __kasan_slab_free+0x51/0x70 kfree+0x174/0x520 open_cached_dir+0x97f/0x1fb0 smb2_query_path_info+0x43c/0x6e0 cifs_get_fattr+0x346/0xf10 cifs_get_inode_info+0x157/0x210 cifs_revalidate_dentry_attr+0x2d1/0x460 cifs_getattr+0x173/0x470 vfs_statx_path+0x10f/0x160 vfs_statx+0xe9/0x150 vfs_fstatat+0x5e/0xc0 __do_sys_newfstatat+0x91/0xf0 do_syscall_64+0x95/0x1a0 entry_SYSCALL_64_after_hwframe+0x76/0x7e Last potentially related work creation: kasan_save_stack+0x33/0x60 __kasan_record_aux_stack+0xad/0xc0 insert_work+0x32/0x100 __queue_work+0x5c9/0x870 queue_work_on+0x82/0x90 open_cached_dir+0x1369/0x1fb0 smb2_query_path_info+0x43c/0x6e0 cifs_get_fattr+0x346/0xf10 cifs_get_inode_info+0x157/0x210 cifs_revalidate_dentry_attr+0x2d1/0x460 cifs_getattr+0x173/0x470 vfs_statx_path+0x10f/0x160 vfs_statx+0xe9/0x150 vfs_fstatat+0x5e/0xc0 __do_sys_newfstatat+0x91/0xf0 do_syscall_64+0x95/0x1a0 entry_SYSCALL_64_after_hwframe+0x76/0x7e The buggy address belongs to the object at ffff88811cc24c00 which belongs to the cache kmalloc-1k of size 1024 The buggy address is located 16 bytes inside of freed 1024-byte region [ffff88811cc24c00, ffff88811cc25000)

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-53187 In the Linux kernel, the following vulnerability has been resolved: io_uring: check for overflows in io_pin_pages WARNING: CPU: 0 PID: 5834 at io_uring/memmap.c:144 io_pin_pages+0x149/0x180 io_uring/memmap.c:144 CPU: 0 UID: 0 PID: 5834 Comm: syz-executor825 Not tainted 6.12.0-next-20241118-syzkaller #0 Call Trace: <TASK> __io_uaddr_map+0xfb/0x2d0 io_uring/memmap.c:183 io_rings_map io_uring/io_uring.c:2611 [inline] io_allocate_scq_urings+0x1c0/0x650 io_uring/io_uring.c:3470 io_uring_create+0x5b5/0xc00 io_uring/io_uring.c:3692 io_uring_setup io_uring/io_uring.c:3781 [inline] ... </TASK> io_pin_pages()'s uaddr parameter came directly from the user and can be garbage. Don't just add size to it as it can overflow.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-53220 In the Linux kernel, the following vulnerability has been resolved: f2fs: fix to account dirty data in __get_secs_required() It will trigger system panic w/ testcase in [1]: ------------[ cut here ]------------ kernel BUG at fs/f2fs/segment.c:2752! RIP: 0010:new_curseg+0xc81/0x2110 Call Trace: f2fs_allocate_data_block+0x1c91/0x4540 do_write_page+0x163/0xdf0 f2fs_outplace_write_data+0x1aa/0x340 f2fs_do_write_data_page+0x797/0x2280 f2fs_write_single_data_page+0x16cd/0x2190 f2fs_write_cache_pages+0x994/0x1c80 f2fs_write_data_pages+0x9cc/0xea0 do_writepages+0x194/0x7a0 filemap_fdatawrite_wbc+0x12b/0x1a0 __filemap_fdatawrite_range+0xbb/0xf0 file_write_and_wait_range+0xa1/0x110 f2fs_do_sync_file+0x26f/0x1c50 f2fs_sync_file+0x12b/0x1d0 vfs_fsync_range+0xfa/0x230 do_fsync+0x3d/0x80 __x64_sys_fsync+0x37/0x50 x64_sys_call+0x1e88/0x20d0 do_syscall_64+0x4b/0x110 entry_SYSCALL_64_after_hwframe+0x76/0x7e The root cause is if checkpoint_disabling and lfs_mode are both on, it will trigger OPU for all overwritten data, it may cost more free segment than expected, so f2fs must account those data correctly to calculate cosumed free segments later, and return ENOSPC earlier to avoid run out of free segment during block allocation. [1] https://lore.kernel.org/fstests/20241015025106.3203676-1-chao@kernel.org/

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-53259 quic-go is an implementation of the QUIC protocol in Go. An off-path attacker can inject an ICMP Packet Too Large packet. Since affected quic-go versions used IP_PMTUDISC_DO, the kernel would then return a "message too large" error on sendmsg, i.e. when quic-go attempts to send a packet that exceeds the MTU claimed in that ICMP packet. By setting this value to smaller than 1200 bytes (the minimum MTU for QUIC), the attacker can disrupt a QUIC connection. Crucially, this can be done after completion of the handshake, thereby circumventing any TCP fallback that might be implemented on the application layer (for example, many browsers fall back to HTTP over TCP if they're unable to establish a QUIC connection). The attacker needs to at least know the client's IP and port tuple to mount an attack. This vulnerability is fixed in 0.48.2.

traefik

CVE-2024-53427 decNumberCopy in decNumber.c in jq through 1.7.1 does not properly consider that NaN is interpreted as numeric, which has a resultant stack-based buffer overflow and out-of-bounds write, as demonstrated by use of --slurp with subtraction, such as a filter of .-. when the input has a certain form of digit string with NaN (e.g., "1 NaN123" immediately followed by many more digits).

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2024-53690 In the Linux kernel, the following vulnerability has been resolved: nilfs2: prevent use of deleted inode syzbot reported a WARNING in nilfs_rmdir. [1] Because the inode bitmap is corrupted, an inode with an inode number that should exist as a ".nilfs" file was reassigned by nilfs_mkdir for "file0", causing an inode duplication during execution. And this causes an underflow of i_nlink in rmdir operations. The inode is used twice by the same task to unmount and remove directories ".nilfs" and "file0", it trigger warning in nilfs_rmdir. Avoid to this issue, check i_nlink in nilfs_iget(), if it is 0, it means that this inode has been deleted, and iput is executed to reclaim it. [1] WARNING: CPU: 1 PID: 5824 at fs/inode.c:407 drop_nlink+0xc4/0x110 fs/inode.c:407 ... Call Trace: <TASK> nilfs_rmdir+0x1b0/0x250 fs/nilfs2/namei.c:342 vfs_rmdir+0x3a3/0x510 fs/namei.c:4394 do_rmdir+0x3b5/0x580 fs/namei.c:4453 __do_sys_rmdir fs/namei.c:4472 [inline] __se_sys_rmdir fs/namei.c:4470 [inline] __x64_sys_rmdir+0x47/0x50 fs/namei.c:4470 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-54193 In the Linux kernel, the following vulnerability has been resolved: accel/ivpu: Fix WARN in ivpu_ipc_send_receive_internal() Move pm_runtime_set_active() to ivpu_pm_init() so when ivpu_ipc_send_receive_internal() is executed before ivpu_pm_enable() it already has correct runtime state, even if last resume was not successful.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-54455 In the Linux kernel, the following vulnerability has been resolved: accel/ivpu: Fix general protection fault in ivpu_bo_list() Check if ctx is not NULL before accessing its fields.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-54458 In the Linux kernel, the following vulnerability has been resolved: scsi: ufs: bsg: Set bsg_queue to NULL after removal Currently, this does not cause any issues, but I believe it is necessary to set bsg_queue to NULL after removing it to prevent potential use-after-free (UAF) access.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2024-54460 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: iso: Fix circular lock in iso_listen_bis This fixes the circular locking dependency warning below, by releasing the socket lock before enterning iso_listen_bis, to avoid any potential deadlock with hdev lock. [ 75.307983] ====================================================== [ 75.307984] WARNING: possible circular locking dependency detected [ 75.307985] 6.12.0-rc6+ #22 Not tainted [ 75.307987] ------------------------------------------------------ [ 75.307987] kworker/u81:2/2623 is trying to acquire lock: [ 75.307988] ffff8fde1769da58 (sk_lock-AF_BLUETOOTH-BTPROTO_ISO) at: iso_connect_cfm+0x253/0x840 [bluetooth] [ 75.308021] but task is already holding lock: [ 75.308022] ffff8fdd61a10078 (&hdev->lock) at: hci_le_per_adv_report_evt+0x47/0x2f0 [bluetooth] [ 75.308053] which lock already depends on the new lock. [ 75.308054] the existing dependency chain (in reverse order) is: [ 75.308055] -> #1 (&hdev->lock){+.+.}-{3:3}: [ 75.308057] __mutex_lock+0xad/0xc50 [ 75.308061] mutex_lock_nested+0x1b/0x30 [ 75.308063] iso_sock_listen+0x143/0x5c0 [bluetooth] [ 75.308085] __sys_listen_socket+0x49/0x60 [ 75.308088] __x64_sys_listen+0x4c/0x90 [ 75.308090] x64_sys_call+0x2517/0x25f0 [ 75.308092] do_syscall_64+0x87/0x150 [ 75.308095] entry_SYSCALL_64_after_hwframe+0x76/0x7e [ 75.308098] -> #0 (sk_lock-AF_BLUETOOTH-BTPROTO_ISO){+.+.}-{0:0}: [ 75.308100] __lock_acquire+0x155e/0x25f0 [ 75.308103] lock_acquire+0xc9/0x300 [ 75.308105] lock_sock_nested+0x32/0x90 [ 75.308107] iso_connect_cfm+0x253/0x840 [bluetooth] [ 75.308128] hci_connect_cfm+0x6c/0x190 [bluetooth] [ 75.308155] hci_le_per_adv_report_evt+0x27b/0x2f0 [bluetooth] [ 75.308180] hci_le_meta_evt+0xe7/0x200 [bluetooth] [ 75.308206] hci_event_packet+0x21f/0x5c0 [bluetooth] [ 75.308230] hci_rx_work+0x3ae/0xb10 [bluetooth] [ 75.308254] process_one_work+0x212/0x740 [ 75.308256] worker_thread+0x1bd/0x3a0 [ 75.308258] kthread+0xe4/0x120 [ 75.308259] ret_from_fork+0x44/0x70 [ 75.308261] ret_from_fork_asm+0x1a/0x30 [ 75.308263] other info that might help us debug this: [ 75.308264] Possible unsafe locking scenario: [ 75.308264] CPU0 CPU1 [ 75.308265] ---- ---- [ 75.308265] lock(&hdev->lock); [ 75.308267] lock(sk_lock- AF_BLUETOOTH-BTPROTO_ISO); [ 75.308268] lock(&hdev->lock); [ 75.308269] lock(sk_lock-AF_BLUETOOTH-BTPROTO_ISO); [ 75.308270] *** DEADLOCK *** [ 75.308271] 4 locks held by kworker/u81:2/2623: [ 75.308272] #0: ffff8fdd66e52148 ((wq_completion)hci0#2){+.+.}-{0:0}, at: process_one_work+0x443/0x740 [ 75.308276] #1: ffffafb488b7fe48 ((work_completion)(&hdev->rx_work)), at: process_one_work+0x1ce/0x740 [ 75.308280] #2: ffff8fdd61a10078 (&hdev->lock){+.+.}-{3:3} at: hci_le_per_adv_report_evt+0x47/0x2f0 [bluetooth] [ 75.308304] #3: ffffffffb6ba4900 (rcu_read_lock){....}-{1:2}, at: hci_connect_cfm+0x29/0x190 [bluetooth]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-55639 In the Linux kernel, the following vulnerability has been resolved: net: renesas: rswitch: avoid use-after-put for a device tree node The device tree node saved in the rswitch_device structure is used at several driver locations. So passing this node to of_node_put() after the first use is wrong. Move of_node_put() for this node to exit paths.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56201 Jinja is an extensible templating engine. In versions on the 3.x branch prior to 3.1.5, a bug in the Jinja compiler allows an attacker that controls both the content and filename of a template to execute arbitrary Python code, regardless of if Jinja's sandbox is used. To exploit the vulnerability, an attacker needs to control both the filename and the contents of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates where the template author can also choose the template filename. This vulnerability is fixed in 3.1.5.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56326 Jinja is an extensible templating engine. Prior to 3.1.5, An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox. This vulnerability is fixed in 3.1.5.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56372 In the Linux kernel, the following vulnerability has been resolved: net: tun: fix tun_napi_alloc_frags() syzbot reported the following crash [1] Issue came with the blamed commit. Instead of going through all the iov components, we keep using the first one and end up with a malformed skb. [1] kernel BUG at net/core/skbuff.c:2849 ! Oops: invalid opcode: 0000 [#1] PREEMPT SMP KASAN PTI CPU: 0 UID: 0 PID: 6230 Comm: syz-executor132 Not tainted 6.13.0-rc1-syzkaller-00407-g96b6fcc0ee41 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 11/25/2024 RIP: 0010:__pskb_pull_tail+0x1568/0x1570 net/core/skbuff.c:2848 Code: 38 c1 0f 8c 32 f1 ff ff 4c 89 f7 e8 92 96 74 f8 e9 25 f1 ff ff e8 e8 ae 09 f8 48 8b 5c 24 08 e9 eb fb ff ff e8 d9 ae 09 f8 90 <0f> 0b 66 0f 1f 44 00 00 90 90 90 90 90 90 90 90 90 90 90 90 90 90 RSP: 0018:ffffc90004cbef30 EFLAGS: 00010293 RAX: ffffffff8995c347 RBX: 00000000fffffff2 RCX: ffff88802cf45a00 RDX: 0000000000000000 RSI: 00000000fffffff2 RDI: 0000000000000000 RBP: ffff88807df0c06a R08: ffffffff8995b084 R09: 1ffff1100fbe185c R10: dffffc0000000000 R11: ffffed100fbe185d R12: ffff888076e85d50 R13: ffff888076e85c80 R14: ffff888076e85cf4 R15: ffff888076e85c80 FS: 00007f0dca6ea6c0(0000) GS:ffff8880b8600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f0dca6ead58 CR3: 00000000119da000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> skb_cow_data+0x2da/0xcb0 net/core/skbuff.c:5284 tipc_aead_decrypt net/tipc/crypto.c:894 [inline] tipc_crypto_rcv+0x402/0x24e0 net/tipc/crypto.c:1844 tipc_rcv+0x57e/0x12a0 net/tipc/node.c:2109 tipc_l2_rcv_msg+0x2bd/0x450 net/tipc/bearer.c:668 __netif_receive_skb_list_ptype net/core/dev.c:5720 [inline] __netif_receive_skb_list_core+0x8b7/0x980 net/core/dev.c:5762 __netif_receive_skb_list net/core/dev.c:5814 [inline] netif_receive_skb_list_internal+0xa51/0xe30 net/core/dev.c:5905 gro_normal_list include/net/gro.h:515 [inline] napi_complete_done+0x2b5/0x870 net/core/dev.c:6256 napi_complete include/linux/netdevice.h:567 [inline] tun_get_user+0x2ea0/0x4890 drivers/net/tun.c:1982 tun_chr_write_iter+0x10d/0x1f0 drivers/net/tun.c:2057 do_iter_readv_writev+0x600/0x880 vfs_writev+0x376/0xba0 fs/read_write.c:1050 do_writev+0x1b6/0x360 fs/read_write.c:1096 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56602 In the Linux kernel, the following vulnerability has been resolved: net: ieee802154: do not leave a dangling sk pointer in ieee802154_create() sock_init_data() attaches the allocated sk object to the provided sock object. If ieee802154_create() fails later, the allocated sk object is freed, but the dangling pointer remains in the provided sock object, which may allow use-after-free. Clear the sk pointer in the sock object on error.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-56652 In the Linux kernel, the following vulnerability has been resolved: drm/xe/reg_sr: Remove register pool That pool implementation doesn't really work: if the krealloc happens to move the memory and return another address, the entries in the xarray become invalid, leading to use-after-free later: BUG: KASAN: slab-use-after-free in xe_reg_sr_apply_mmio+0x570/0x760 [xe] Read of size 4 at addr ffff8881244b2590 by task modprobe/2753 Allocated by task 2753: kasan_save_stack+0x39/0x70 kasan_save_track+0x14/0x40 kasan_save_alloc_info+0x37/0x60 __kasan_kmalloc+0xc3/0xd0 __kmalloc_node_track_caller_noprof+0x200/0x6d0 krealloc_noprof+0x229/0x380 Simplify the code to fix the bug. A better pooling strategy may be added back later if needed. (cherry picked from commit e5283bd4dfecbd3335f43b62a68e24dae23f59e4)

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56653 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: btmtk: avoid UAF in btmtk_process_coredump hci_devcd_append may lead to the release of the skb, so it cannot be accessed once it is called. ================================================================== BUG: KASAN: slab-use-after-free in btmtk_process_coredump+0x2a7/0x2d0 [btmtk] Read of size 4 at addr ffff888033cfabb0 by task kworker/0:3/82 CPU: 0 PID: 82 Comm: kworker/0:3 Tainted: G U 6.6.40-lockdep-03464-g1d8b4eb3060e #1 b0b3c1cc0c842735643fb411799d97921d1f688c Hardware name: Google Yaviks_Ufs/Yaviks_Ufs, BIOS Google_Yaviks_Ufs.15217.552.0 05/07/2024 Workqueue: events btusb_rx_work [btusb] Call Trace: <TASK> dump_stack_lvl+0xfd/0x150 print_report+0x131/0x780 kasan_report+0x177/0x1c0 btmtk_process_coredump+0x2a7/0x2d0 [btmtk 03edd567dd71a65958807c95a65db31d433e1d01] btusb_recv_acl_mtk+0x11c/0x1a0 [btusb 675430d1e87c4f24d0c1f80efe600757a0f32bec] btusb_rx_work+0x9e/0xe0 [btusb 675430d1e87c4f24d0c1f80efe600757a0f32bec] worker_thread+0xe44/0x2cc0 kthread+0x2ff/0x3a0 ret_from_fork+0x51/0x80 ret_from_fork_asm+0x1b/0x30 </TASK> Allocated by task 82: stack_trace_save+0xdc/0x190 kasan_set_track+0x4e/0x80 __kasan_slab_alloc+0x4e/0x60 kmem_cache_alloc+0x19f/0x360 skb_clone+0x132/0xf70 btusb_recv_acl_mtk+0x104/0x1a0 [btusb] btusb_rx_work+0x9e/0xe0 [btusb] worker_thread+0xe44/0x2cc0 kthread+0x2ff/0x3a0 ret_from_fork+0x51/0x80 ret_from_fork_asm+0x1b/0x30 Freed by task 1733: stack_trace_save+0xdc/0x190 kasan_set_track+0x4e/0x80 kasan_save_free_info+0x28/0xb0 ____kasan_slab_free+0xfd/0x170 kmem_cache_free+0x183/0x3f0 hci_devcd_rx+0x91a/0x2060 [bluetooth] worker_thread+0xe44/0x2cc0 kthread+0x2ff/0x3a0 ret_from_fork+0x51/0x80 ret_from_fork_asm+0x1b/0x30 The buggy address belongs to the object at ffff888033cfab40 which belongs to the cache skbuff_head_cache of size 232 The buggy address is located 112 bytes inside of freed 232-byte region [ffff888033cfab40, ffff888033cfac28) The buggy address belongs to the physical page: page:00000000a174ba93 refcount:1 mapcount:0 mapping:0000000000000000 index:0x0 pfn:0x33cfa head:00000000a174ba93 order:1 entire_mapcount:0 nr_pages_mapped:0 pincount:0 anon flags: 0x4000000000000840(slab|head|zone=1) page_type: 0xffffffff() raw: 4000000000000840 ffff888100848a00 0000000000000000 0000000000000001 raw: 0000000000000000 0000000080190019 00000001ffffffff 0000000000000000 page dumped because: kasan: bad access detected Memory state around the buggy address: ffff888033cfaa80: fb fb fb fb fb fb fb fb fb fb fb fb fb fc fc fc ffff888033cfab00: fc fc fc fc fc fc fc fc fa fb fb fb fb fb fb fb >ffff888033cfab80: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ^ ffff888033cfac00: fb fb fb fb fb fc fc fc fc fc fc fc fc fc fc fc ffff888033cfac80: fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb fb ================================================================== Check if we need to call hci_devcd_complete before calling hci_devcd_append. That requires that we check data->cd_info.cnt >= MTK_COREDUMP_NUM instead of data->cd_info.cnt > MTK_COREDUMP_NUM, as we increment data->cd_info.cnt only once the call to hci_devcd_append succeeds.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4

CVE-2024-56654 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: hci_event: Fix using rcu_read_(un)lock while iterating The usage of rcu_read_(un)lock while inside list_for_each_entry_rcu is not safe since for the most part entries fetched this way shall be treated as rcu_dereference: Note that the value returned by rcu_dereference() is valid only within the enclosing RCU read-side critical section [1]_. For example, the following is **not** legal:: rcu_read_lock(); p = rcu_dereference(head.next); rcu_read_unlock(); x = p->address; /* BUG!!! */ rcu_read_lock(); y = p->data; /* BUG!!! */ rcu_read_unlock();

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56656 In the Linux kernel, the following vulnerability has been resolved: bnxt_en: Fix aggregation ID mask to prevent oops on 5760X chips The 5760X (P7) chip's HW GRO/LRO interface is very similar to that of the previous generation (5750X or P5). However, the aggregation ID fields in the completion structures on P7 have been redefined from 16 bits to 12 bits. The freed up 4 bits are redefined for part of the metadata such as the VLAN ID. The aggregation ID mask was not modified when adding support for P7 chips. Including the extra 4 bits for the aggregation ID can potentially cause the driver to store or fetch the packet header of GRO/LRO packets in the wrong TPA buffer. It may hit the BUG() condition in __skb_pull() because the SKB contains no valid packet header: kernel BUG at include/linux/skbuff.h:2766! Oops: invalid opcode: 0000 1 PREEMPT SMP NOPTI CPU: 4 UID: 0 PID: 0 Comm: swapper/4 Kdump: loaded Tainted: G OE 6.12.0-rc2+ #7 Tainted: [O]=OOT_MODULE, [E]=UNSIGNED_MODULE Hardware name: Dell Inc. PowerEdge R760/0VRV9X, BIOS 1.0.1 12/27/2022 RIP: 0010:eth_type_trans+0xda/0x140 Code: 80 00 00 00 eb c1 8b 47 70 2b 47 74 48 8b 97 d0 00 00 00 83 f8 01 7e 1b 48 85 d2 74 06 66 83 3a ff 74 09 b8 00 04 00 00 eb a5 <0f> 0b b8 00 01 00 00 eb 9c 48 85 ff 74 eb 31 f6 b9 02 00 00 00 48 RSP: 0018:ff615003803fcc28 EFLAGS: 00010283 RAX: 00000000000022d2 RBX: 0000000000000003 RCX: ff2e8c25da334040 RDX: 0000000000000040 RSI: ff2e8c25c1ce8000 RDI: ff2e8c25869f9000 RBP: ff2e8c258c31c000 R08: ff2e8c25da334000 R09: 0000000000000001 R10: ff2e8c25da3342c0 R11: ff2e8c25c1ce89c0 R12: ff2e8c258e0990b0 R13: ff2e8c25bb120000 R14: ff2e8c25c1ce89c0 R15: ff2e8c25869f9000 FS: 0000000000000000(0000) GS:ff2e8c34be300000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 000055f05317e4c8 CR3: 000000108bac6006 CR4: 0000000000773ef0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe07f0 DR7: 0000000000000400 PKRU: 55555554 Call Trace: <IRQ> ? die+0x33/0x90 ? do_trap+0xd9/0x100 ? eth_type_trans+0xda/0x140 ? do_error_trap+0x65/0x80 ? eth_type_trans+0xda/0x140 ? exc_invalid_op+0x4e/0x70 ? eth_type_trans+0xda/0x140 ? asm_exc_invalid_op+0x16/0x20 ? eth_type_trans+0xda/0x140 bnxt_tpa_end+0x10b/0x6b0 [bnxt_en] ? bnxt_tpa_start+0x195/0x320 [bnxt_en] bnxt_rx_pkt+0x902/0xd90 [bnxt_en] ? __bnxt_tx_int.constprop.0+0x89/0x300 [bnxt_en] ? kmem_cache_free+0x343/0x440 ? __bnxt_tx_int.constprop.0+0x24f/0x300 [bnxt_en] __bnxt_poll_work+0x193/0x370 [bnxt_en] bnxt_poll_p5+0x9a/0x300 [bnxt_en] ? try_to_wake_up+0x209/0x670 __napi_poll+0x29/0x1b0 Fix it by redefining the aggregation ID mask for P5_PLUS chips to be 12 bits. This will work because the maximum aggregation ID is less than 4096 on all P5_PLUS chips.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56662 In the Linux kernel, the following vulnerability has been resolved: acpi: nfit: vmalloc-out-of-bounds Read in acpi_nfit_ctl Fix an issue detected by syzbot with KASAN: BUG: KASAN: vmalloc-out-of-bounds in cmd_to_func drivers/acpi/nfit/ core.c:416 [inline] BUG: KASAN: vmalloc-out-of-bounds in acpi_nfit_ctl+0x20e8/0x24a0 drivers/acpi/nfit/core.c:459 The issue occurs in cmd_to_func when the call_pkg->nd_reserved2 array is accessed without verifying that call_pkg points to a buffer that is appropriately sized as a struct nd_cmd_pkg. This can lead to out-of-bounds access and undefined behavior if the buffer does not have sufficient space. To address this, a check was added in acpi_nfit_ctl() to ensure that buf is not NULL and that buf_len is less than sizeof(*call_pkg) before accessing it. This ensures safe access to the members of call_pkg, including the nd_reserved2 array.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56670 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: u_serial: Fix the issue that gs_start_io crashed due to accessing null pointer Considering that in some extreme cases, when u_serial driver is accessed by multiple threads, Thread A is executing the open operation and calling the gs_open, Thread B is executing the disconnect operation and calling the gserial_disconnect function,The port->port_usb pointer will be set to NULL. E.g. Thread A Thread B gs_open() gadget_unbind_driver() gs_start_io() composite_disconnect() gs_start_rx() gserial_disconnect() ... ... spin_unlock(&port->port_lock) status = usb_ep_queue() spin_lock(&port->port_lock) spin_lock(&port->port_lock) port->port_usb = NULL gs_free_requests(port->port_usb->in) spin_unlock(&port->port_lock) Crash This causes thread A to access a null pointer (port->port_usb is null) when calling the gs_free_requests function, causing a crash. If port_usb is NULL, the release request will be skipped as it will be done by gserial_disconnect. So add a null pointer check to gs_start_io before attempting to access the value of the pointer port->port_usb. Call trace: gs_start_io+0x164/0x25c gs_open+0x108/0x13c tty_open+0x314/0x638 chrdev_open+0x1b8/0x258 do_dentry_open+0x2c4/0x700 vfs_open+0x2c/0x3c path_openat+0xa64/0xc60 do_filp_open+0xb8/0x164 do_sys_openat2+0x84/0xf0 __arm64_sys_openat+0x70/0x9c invoke_syscall+0x58/0x114 el0_svc_common+0x80/0xe0 do_el0_svc+0x1c/0x28 el0_svc+0x38/0x68

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56675 In the Linux kernel, the following vulnerability has been resolved: bpf: Fix UAF via mismatching bpf_prog/attachment RCU flavors Uprobes always use bpf_prog_run_array_uprobe() under tasks-trace-RCU protection. But it is possible to attach a non-sleepable BPF program to a uprobe, and non-sleepable BPF programs are freed via normal RCU (see __bpf_prog_put_noref()). This leads to UAF of the bpf_prog because a normal RCU grace period does not imply a tasks-trace-RCU grace period. Fix it by explicitly waiting for a tasks-trace-RCU grace period after removing the attachment of a bpf_prog to a perf_event.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56677 In the Linux kernel, the following vulnerability has been resolved: powerpc/fadump: Move fadump_cma_init to setup_arch() after initmem_init() During early init CMA_MIN_ALIGNMENT_BYTES can be PAGE_SIZE, since pageblock_order is still zero and it gets initialized later during initmem_init() e.g. setup_arch() -> initmem_init() -> sparse_init() -> set_pageblock_order() One such use case where this causes issue is - early_setup() -> early_init_devtree() -> fadump_reserve_mem() -> fadump_cma_init() This causes CMA memory alignment check to be bypassed in cma_init_reserved_mem(). Then later cma_activate_area() can hit a VM_BUG_ON_PAGE(pfn & ((1 << order) - 1)) if the reserved memory area was not pageblock_order aligned. Fix it by moving the fadump_cma_init() after initmem_init(), where other such cma reservations also gets called. <stack trace> ============== page: refcount:0 mapcount:0 mapping:0000000000000000 index:0x0 pfn:0x10010 flags: 0x13ffff800000000(node=1|zone=0|lastcpupid=0x7ffff) CMA raw: 013ffff800000000 5deadbeef0000100 5deadbeef0000122 0000000000000000 raw: 0000000000000000 0000000000000000 00000000ffffffff 0000000000000000 page dumped because: VM_BUG_ON_PAGE(pfn & ((1 << order) - 1)) ------------[ cut here ]------------ kernel BUG at mm/page_alloc.c:778! Call Trace: __free_one_page+0x57c/0x7b0 (unreliable) free_pcppages_bulk+0x1a8/0x2c8 free_unref_page_commit+0x3d4/0x4e4 free_unref_page+0x458/0x6d0 init_cma_reserved_pageblock+0x114/0x198 cma_init_reserved_areas+0x270/0x3e0 do_one_initcall+0x80/0x2f8 kernel_init_freeable+0x33c/0x530 kernel_init+0x34/0x26c ret_from_kernel_user_thread+0x14/0x1c

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-56710 In the Linux kernel, the following vulnerability has been resolved: ceph: fix memory leak in ceph_direct_read_write() The bvecs array which is allocated in iter_get_bvecs_alloc() is leaked and pages remain pinned if ceph_alloc_sparse_ext_map() fails. There is no need to delay the allocation of sparse_ext map until after the bvecs array is set up, so fix this by moving sparse_ext allocation a bit earlier. Also, make a similar adjustment in __ceph_sync_read() for consistency (a leak of the same kind in __ceph_sync_read() has been addressed differently).

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56758 In the Linux kernel, the following vulnerability has been resolved: btrfs: check folio mapping after unlock in relocate_one_folio() When we call btrfs_read_folio() to bring a folio uptodate, we unlock the folio. The result of that is that a different thread can modify the mapping (like remove it with invalidate) before we call folio_lock(). This results in an invalid page and we need to try again. In particular, if we are relocating concurrently with aborting a transaction, this can result in a crash like the following: BUG: kernel NULL pointer dereference, address: 0000000000000000 PGD 0 P4D 0 Oops: 0000 [#1] SMP CPU: 76 PID: 1411631 Comm: kworker/u322:5 Workqueue: events_unbound btrfs_reclaim_bgs_work RIP: 0010:set_page_extent_mapped+0x20/0xb0 RSP: 0018:ffffc900516a7be8 EFLAGS: 00010246 RAX: ffffea009e851d08 RBX: ffffea009e0b1880 RCX: 0000000000000000 RDX: 0000000000000000 RSI: ffffc900516a7b90 RDI: ffffea009e0b1880 RBP: 0000000003573000 R08: 0000000000000001 R09: ffff88c07fd2f3f0 R10: 0000000000000000 R11: 0000194754b575be R12: 0000000003572000 R13: 0000000003572fff R14: 0000000000100cca R15: 0000000005582fff FS: 0000000000000000(0000) GS:ffff88c07fd00000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000000000000000 CR3: 000000407d00f002 CR4: 00000000007706f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 PKRU: 55555554 Call Trace: <TASK> ? __die+0x78/0xc0 ? page_fault_oops+0x2a8/0x3a0 ? __switch_to+0x133/0x530 ? wq_worker_running+0xa/0x40 ? exc_page_fault+0x63/0x130 ? asm_exc_page_fault+0x22/0x30 ? set_page_extent_mapped+0x20/0xb0 relocate_file_extent_cluster+0x1a7/0x940 relocate_data_extent+0xaf/0x120 relocate_block_group+0x20f/0x480 btrfs_relocate_block_group+0x152/0x320 btrfs_relocate_chunk+0x3d/0x120 btrfs_reclaim_bgs_work+0x2ae/0x4e0 process_scheduled_works+0x184/0x370 worker_thread+0xc6/0x3e0 ? blk_add_timer+0xb0/0xb0 kthread+0xae/0xe0 ? flush_tlb_kernel_range+0x90/0x90 ret_from_fork+0x2f/0x40 ? flush_tlb_kernel_range+0x90/0x90 ret_from_fork_asm+0x11/0x20 </TASK> This occurs because cleanup_one_transaction() calls destroy_delalloc_inodes() which calls invalidate_inode_pages2() which takes the folio_lock before setting mapping to NULL. We fail to check this, and subsequently call set_extent_mapping(), which assumes that mapping != NULL (in fact it asserts that in debug mode) Note that the "fixes" patch here is not the one that introduced the race (the very first iteration of this code from 2009) but a more recent change that made this particular crash happen in practice.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56759 In the Linux kernel, the following vulnerability has been resolved: btrfs: fix use-after-free when COWing tree bock and tracing is enabled When a COWing a tree block, at btrfs_cow_block(), and we have the tracepoint trace_btrfs_cow_block() enabled and preemption is also enabled (CONFIG_PREEMPT=y), we can trigger a use-after-free in the COWed extent buffer while inside the tracepoint code. This is because in some paths that call btrfs_cow_block(), such as btrfs_search_slot(), we are holding the last reference on the extent buffer @buf so btrfs_force_cow_block() drops the last reference on the @buf extent buffer when it calls free_extent_buffer_stale(buf), which schedules the release of the extent buffer with RCU. This means that if we are on a kernel with preemption, the current task may be preempted before calling trace_btrfs_cow_block() and the extent buffer already released by the time trace_btrfs_cow_block() is called, resulting in a use-after-free. Fix this by moving the trace_btrfs_cow_block() from btrfs_cow_block() to btrfs_force_cow_block() before the COWed extent buffer is freed. This also has a side effect of invoking the tracepoint in the tree defrag code, at defrag.c:btrfs_realloc_node(), since btrfs_force_cow_block() is called there, but this is fine and it was actually missing there.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56760 In the Linux kernel, the following vulnerability has been resolved: PCI/MSI: Handle lack of irqdomain gracefully Alexandre observed a warning emitted from pci_msi_setup_msi_irqs() on a RISCV platform which does not provide PCI/MSI support: WARNING: CPU: 1 PID: 1 at drivers/pci/msi/msi.h:121 pci_msi_setup_msi_irqs+0x2c/0x32 __pci_enable_msix_range+0x30c/0x596 pci_msi_setup_msi_irqs+0x2c/0x32 pci_alloc_irq_vectors_affinity+0xb8/0xe2 RISCV uses hierarchical interrupt domains and correctly does not implement the legacy fallback. The warning triggers from the legacy fallback stub. That warning is bogus as the PCI/MSI layer knows whether a PCI/MSI parent domain is associated with the device or not. There is a check for MSI-X, which has a legacy assumption. But that legacy fallback assumption is only valid when legacy support is enabled, but otherwise the check should simply return -ENOTSUPP. Loongarch tripped over the same problem and blindly enabled legacy support without implementing the legacy fallbacks. There are weak implementations which return an error, so the problem was papered over. Correct pci_msi_domain_supports() to evaluate the legacy mode and add the missing supported check into the MSI enable path to complete it.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56761 In the Linux kernel, the following vulnerability has been resolved: x86/fred: Clear WFE in missing-ENDBRANCH #CPs An indirect branch instruction sets the CPU indirect branch tracker (IBT) into WAIT_FOR_ENDBRANCH (WFE) state and WFE stays asserted across the instruction boundary. When the decoder finds an inappropriate instruction while WFE is set ENDBR, the CPU raises a #CP fault. For the "kernel IBT no ENDBR" selftest where #CPs are deliberately triggered, the WFE state of the interrupted context needs to be cleared to let execution continue. Otherwise when the CPU resumes from the instruction that just caused the previous #CP, another missing-ENDBRANCH #CP is raised and the CPU enters a dead loop. This is not a problem with IDT because it doesn't preserve WFE and IRET doesn't set WFE. But FRED provides space on the entry stack (in an expanded CS area) to save and restore the WFE state, thus the WFE state is no longer clobbered, so software must clear it. Clear WFE to avoid dead looping in ibt_clear_fred_wfe() and the !ibt_fatal code path when execution is allowed to continue. Clobbering WFE in any other circumstance is a security-relevant bug. [ dhansen: changelog rewording ]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56764 In the Linux kernel, the following vulnerability has been resolved: ublk: detach gendisk from ublk device if add_disk() fails Inside ublk_abort_requests(), gendisk is grabbed for aborting all inflight requests. And ublk_abort_requests() is called when exiting the uring context or handling timeout. If add_disk() fails, the gendisk may have been freed when calling ublk_abort_requests(), so use-after-free can be caused when getting disk's reference in ublk_abort_requests(). Fixes the bug by detaching gendisk from ublk device if add_disk() fails.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-56767 In the Linux kernel, the following vulnerability has been resolved: dmaengine: at_xdmac: avoid null_prt_deref in at_xdmac_prep_dma_memset The at_xdmac_memset_create_desc may return NULL, which will lead to a null pointer dereference. For example, the len input is error, or the atchan->free_descs_list is empty and memory is exhausted. Therefore, add check to avoid this.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57792 In the Linux kernel, the following vulnerability has been resolved: power: supply: gpio-charger: Fix set charge current limits Fix set charge current limits for devices which allow to set the lowest charge current limit to be greater zero. If requested charge current limit is below lowest limit, the index equals current_limit_map_size which leads to accessing memory beyond allocated memory.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57793 In the Linux kernel, the following vulnerability has been resolved: virt: tdx-guest: Just leak decrypted memory on unrecoverable errors In CoCo VMs it is possible for the untrusted host to cause set_memory_decrypted() to fail such that an error is returned and the resulting memory is shared. Callers need to take care to handle these errors to avoid returning decrypted (shared) memory to the page allocator, which could lead to functional or security issues. Leak the decrypted memory when set_memory_decrypted() fails, and don't need to print an error since set_memory_decrypted() will call WARN_ONCE().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57801 In the Linux kernel, the following vulnerability has been resolved: net/mlx5e: Skip restore TC rules for vport rep without loaded flag During driver unload, unregister_netdev is called after unloading vport rep. So, the mlx5e_rep_priv is already freed while trying to get rpriv->netdev, or walk rpriv->tc_ht, which results in use-after-free. So add the checking to make sure access the data of vport rep which is still loaded.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57802 In the Linux kernel, the following vulnerability has been resolved: netrom: check buffer length before accessing it Syzkaller reports an uninit value read from ax25cmp when sending raw message through ieee802154 implementation. ===================================================== BUG: KMSAN: uninit-value in ax25cmp+0x3a5/0x460 net/ax25/ax25_addr.c:119 ax25cmp+0x3a5/0x460 net/ax25/ax25_addr.c:119 nr_dev_get+0x20e/0x450 net/netrom/nr_route.c:601 nr_route_frame+0x1a2/0xfc0 net/netrom/nr_route.c:774 nr_xmit+0x5a/0x1c0 net/netrom/nr_dev.c:144 __netdev_start_xmit include/linux/netdevice.h:4940 [inline] netdev_start_xmit include/linux/netdevice.h:4954 [inline] xmit_one net/core/dev.c:3548 [inline] dev_hard_start_xmit+0x247/0xa10 net/core/dev.c:3564 __dev_queue_xmit+0x33b8/0x5130 net/core/dev.c:4349 dev_queue_xmit include/linux/netdevice.h:3134 [inline] raw_sendmsg+0x654/0xc10 net/ieee802154/socket.c:299 ieee802154_sock_sendmsg+0x91/0xc0 net/ieee802154/socket.c:96 sock_sendmsg_nosec net/socket.c:730 [inline] __sock_sendmsg net/socket.c:745 [inline] ____sys_sendmsg+0x9c2/0xd60 net/socket.c:2584 ___sys_sendmsg+0x28d/0x3c0 net/socket.c:2638 __sys_sendmsg net/socket.c:2667 [inline] __do_sys_sendmsg net/socket.c:2676 [inline] __se_sys_sendmsg net/socket.c:2674 [inline] __x64_sys_sendmsg+0x307/0x490 net/socket.c:2674 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0x44/0x110 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x63/0x6b Uninit was created at: slab_post_alloc_hook+0x129/0xa70 mm/slab.h:768 slab_alloc_node mm/slub.c:3478 [inline] kmem_cache_alloc_node+0x5e9/0xb10 mm/slub.c:3523 kmalloc_reserve+0x13d/0x4a0 net/core/skbuff.c:560 __alloc_skb+0x318/0x740 net/core/skbuff.c:651 alloc_skb include/linux/skbuff.h:1286 [inline] alloc_skb_with_frags+0xc8/0xbd0 net/core/skbuff.c:6334 sock_alloc_send_pskb+0xa80/0xbf0 net/core/sock.c:2780 sock_alloc_send_skb include/net/sock.h:1884 [inline] raw_sendmsg+0x36d/0xc10 net/ieee802154/socket.c:282 ieee802154_sock_sendmsg+0x91/0xc0 net/ieee802154/socket.c:96 sock_sendmsg_nosec net/socket.c:730 [inline] __sock_sendmsg net/socket.c:745 [inline] ____sys_sendmsg+0x9c2/0xd60 net/socket.c:2584 ___sys_sendmsg+0x28d/0x3c0 net/socket.c:2638 __sys_sendmsg net/socket.c:2667 [inline] __do_sys_sendmsg net/socket.c:2676 [inline] __se_sys_sendmsg net/socket.c:2674 [inline] __x64_sys_sendmsg+0x307/0x490 net/socket.c:2674 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0x44/0x110 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x63/0x6b CPU: 0 PID: 5037 Comm: syz-executor166 Not tainted 6.7.0-rc7-syzkaller-00003-gfbafc3e621c3 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 11/17/2023 ===================================================== This issue occurs because the skb buffer is too small, and it's actual allocation is aligned. This hides an actual issue, which is that nr_route_frame does not validate the buffer size before using it. Fix this issue by checking skb->len before accessing any fields in skb->data. Found by Linux Verification Center (linuxtesting.org) with Syzkaller.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57805 In the Linux kernel, the following vulnerability has been resolved: ASoC: SOF: Intel: hda-dai: Do not release the link DMA on STOP The linkDMA should not be released on stop trigger since a stream re-start might happen without closing of the stream. This leaves a short time for other streams to 'steal' the linkDMA since it has been released. This issue is not easy to reproduce under normal conditions as usually after stop the stream is closed, or the same stream is restarted, but if another stream got in between the stop and start, like this: aplay -Dhw:0,3 -c2 -r48000 -fS32_LE /dev/zero -d 120 CTRL+z aplay -Dhw:0,0 -c2 -r48000 -fS32_LE /dev/zero -d 120 then the link DMA channels will be mixed up, resulting firmware error or crash.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57806 In the Linux kernel, the following vulnerability has been resolved: btrfs: fix transaction atomicity bug when enabling simple quotas Set squota incompat bit before committing the transaction that enables the feature. With the config CONFIG_BTRFS_ASSERT enabled, an assertion failure occurs regarding the simple quota feature. [5.596534] assertion failed: btrfs_fs_incompat(fs_info, SIMPLE_QUOTA), in fs/btrfs/qgroup.c:365 [5.597098] ------------[ cut here ]------------ [5.597371] kernel BUG at fs/btrfs/qgroup.c:365! [5.597946] CPU: 1 UID: 0 PID: 268 Comm: mount Not tainted 6.13.0-rc2-00031-gf92f4749861b #146 [5.598450] Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.16.2-debian-1.16.2-1 04/01/2014 [5.599008] RIP: 0010:btrfs_read_qgroup_config+0x74d/0x7a0 [5.604303] <TASK> [5.605230] ? btrfs_read_qgroup_config+0x74d/0x7a0 [5.605538] ? exc_invalid_op+0x56/0x70 [5.605775] ? btrfs_read_qgroup_config+0x74d/0x7a0 [5.606066] ? asm_exc_invalid_op+0x1f/0x30 [5.606441] ? btrfs_read_qgroup_config+0x74d/0x7a0 [5.606741] ? btrfs_read_qgroup_config+0x74d/0x7a0 [5.607038] ? try_to_wake_up+0x317/0x760 [5.607286] open_ctree+0xd9c/0x1710 [5.607509] btrfs_get_tree+0x58a/0x7e0 [5.608002] vfs_get_tree+0x2e/0x100 [5.608224] fc_mount+0x16/0x60 [5.608420] btrfs_get_tree+0x2f8/0x7e0 [5.608897] vfs_get_tree+0x2e/0x100 [5.609121] path_mount+0x4c8/0xbc0 [5.609538] __x64_sys_mount+0x10d/0x150 The issue can be easily reproduced using the following reproducer: root@q:linux# cat repro.sh set -e mkfs.btrfs -q -f /dev/sdb mount /dev/sdb /mnt/btrfs btrfs quota enable -s /mnt/btrfs umount /mnt/btrfs mount /dev/sdb /mnt/btrfs The issue is that when enabling quotas, at btrfs_quota_enable(), we set BTRFS_QGROUP_STATUS_FLAG_SIMPLE_MODE at fs_info->qgroup_flags and persist it in the quota root in the item with the key BTRFS_QGROUP_STATUS_KEY, but we only set the incompat bit BTRFS_FEATURE_INCOMPAT_SIMPLE_QUOTA after we commit the transaction used to enable simple quotas. This means that if after that transaction commit we unmount the filesystem without starting and committing any other transaction, or we have a power failure, the next time we mount the filesystem we will find the flag BTRFS_QGROUP_STATUS_FLAG_SIMPLE_MODE set in the item with the key BTRFS_QGROUP_STATUS_KEY but we will not find the incompat bit BTRFS_FEATURE_INCOMPAT_SIMPLE_QUOTA set in the superblock, triggering an assertion failure at: btrfs_read_qgroup_config() -> qgroup_read_enable_gen() To fix this issue, set the BTRFS_FEATURE_INCOMPAT_SIMPLE_QUOTA flag immediately after setting the BTRFS_QGROUP_STATUS_FLAG_SIMPLE_MODE. This ensures that both flags are flushed to disk within the same transaction.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57807 In the Linux kernel, the following vulnerability has been resolved: scsi: megaraid_sas: Fix for a potential deadlock This fixes a 'possible circular locking dependency detected' warning CPU0 CPU1 ---- ---- lock(&instance->reset_mutex); lock(&shost->scan_mutex); lock(&instance->reset_mutex); lock(&shost->scan_mutex); Fix this by temporarily releasing the reset_mutex.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57834 In the Linux kernel, the following vulnerability has been resolved: media: vidtv: Fix a null-ptr-deref in vidtv_mux_stop_thread syzbot report a null-ptr-deref in vidtv_mux_stop_thread. [1] If dvb->mux is not initialized successfully by vidtv_mux_init() in the vidtv_start_streaming(), it will trigger null pointer dereference about mux in vidtv_mux_stop_thread(). Adjust the timing of streaming initialization and check it before stopping it. [1] KASAN: null-ptr-deref in range [0x0000000000000128-0x000000000000012f] CPU: 0 UID: 0 PID: 5842 Comm: syz-executor248 Not tainted 6.13.0-rc4-syzkaller-00012-g9b2ffa6148b1 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 RIP: 0010:vidtv_mux_stop_thread+0x26/0x80 drivers/media/test-drivers/vidtv/vidtv_mux.c:471 Code: 90 90 90 90 66 0f 1f 00 55 53 48 89 fb e8 82 2e c8 f9 48 8d bb 28 01 00 00 48 b8 00 00 00 00 00 fc ff df 48 89 fa 48 c1 ea 03 <0f> b6 04 02 84 c0 74 02 7e 3b 0f b6 ab 28 01 00 00 31 ff 89 ee e8 RSP: 0018:ffffc90003f2faa8 EFLAGS: 00010202 RAX: dffffc0000000000 RBX: 0000000000000000 RCX: ffffffff87cfb125 RDX: 0000000000000025 RSI: ffffffff87d120ce RDI: 0000000000000128 RBP: ffff888029b8d220 R08: 0000000000000005 R09: 0000000000000000 R10: 0000000000000000 R11: 0000000000000003 R12: ffff888029b8d188 R13: ffffffff8f590aa0 R14: ffffc9000581c5c8 R15: ffff888029a17710 FS: 00007f7eef5156c0(0000) GS:ffff8880b8600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f7eef5e635c CR3: 0000000076ca6000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> vidtv_stop_streaming drivers/media/test-drivers/vidtv/vidtv_bridge.c:209 [inline] vidtv_stop_feed+0x151/0x250 drivers/media/test-drivers/vidtv/vidtv_bridge.c:252 dmx_section_feed_stop_filtering+0x90/0x160 drivers/media/dvb-core/dvb_demux.c:1000 dvb_dmxdev_feed_stop.isra.0+0x1ee/0x270 drivers/media/dvb-core/dmxdev.c:486 dvb_dmxdev_filter_stop+0x22a/0x3a0 drivers/media/dvb-core/dmxdev.c:559 dvb_dmxdev_filter_free drivers/media/dvb-core/dmxdev.c:840 [inline] dvb_demux_release+0x92/0x550 drivers/media/dvb-core/dmxdev.c:1246 __fput+0x3f8/0xb60 fs/file_table.c:450 task_work_run+0x14e/0x250 kernel/task_work.c:239 get_signal+0x1d3/0x2610 kernel/signal.c:2790 arch_do_signal_or_restart+0x90/0x7e0 arch/x86/kernel/signal.c:337 exit_to_user_mode_loop kernel/entry/common.c:111 [inline] exit_to_user_mode_prepare include/linux/entry-common.h:329 [inline] __syscall_exit_to_user_mode_work kernel/entry/common.c:207 [inline] syscall_exit_to_user_mode+0x150/0x2a0 kernel/entry/common.c:218 do_syscall_64+0xda/0x250 arch/x86/entry/common.c:89 entry_SYSCALL_64_after_hwframe+0x77/0x7f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2024-57841 In the Linux kernel, the following vulnerability has been resolved: net: fix memory leak in tcp_conn_request() If inet_csk_reqsk_queue_hash_add() return false, tcp_conn_request() will return without free the dst memory, which allocated in af_ops->route_req. Here is the kmemleak stack: unreferenced object 0xffff8881198631c0 (size 240): comm "softirq", pid 0, jiffies 4299266571 (age 1802.392s) hex dump (first 32 bytes): 00 10 9b 03 81 88 ff ff 80 98 da bc ff ff ff ff ................ 81 55 18 bb ff ff ff ff 00 00 00 00 00 00 00 00 .U.............. backtrace: [<ffffffffb93e8d4c>] kmem_cache_alloc+0x60c/0xa80 [<ffffffffba11b4c5>] dst_alloc+0x55/0x250 [<ffffffffba227bf6>] rt_dst_alloc+0x46/0x1d0 [<ffffffffba23050a>] __mkroute_output+0x29a/0xa50 [<ffffffffba23456b>] ip_route_output_key_hash+0x10b/0x240 [<ffffffffba2346bd>] ip_route_output_flow+0x1d/0x90 [<ffffffffba254855>] inet_csk_route_req+0x2c5/0x500 [<ffffffffba26b331>] tcp_conn_request+0x691/0x12c0 [<ffffffffba27bd08>] tcp_rcv_state_process+0x3c8/0x11b0 [<ffffffffba2965c6>] tcp_v4_do_rcv+0x156/0x3b0 [<ffffffffba299c98>] tcp_v4_rcv+0x1cf8/0x1d80 [<ffffffffba239656>] ip_protocol_deliver_rcu+0xf6/0x360 [<ffffffffba2399a6>] ip_local_deliver_finish+0xe6/0x1e0 [<ffffffffba239b8e>] ip_local_deliver+0xee/0x360 [<ffffffffba239ead>] ip_rcv+0xad/0x2f0 [<ffffffffba110943>] __netif_receive_skb_one_core+0x123/0x140 Call dst_release() to free the dst memory when inet_csk_reqsk_queue_hash_add() return false in tcp_conn_request().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57849 In the Linux kernel, the following vulnerability has been resolved: s390/cpum_sf: Handle CPU hotplug remove during sampling CPU hotplug remove handling triggers the following function call sequence: CPUHP_AP_PERF_S390_SF_ONLINE --> s390_pmu_sf_offline_cpu() ... CPUHP_AP_PERF_ONLINE --> perf_event_exit_cpu() The s390 CPUMF sampling CPU hotplug handler invokes: s390_pmu_sf_offline_cpu() +--> cpusf_pmu_setup() +--> setup_pmc_cpu() +--> deallocate_buffers() This function de-allocates all sampling data buffers (SDBs) allocated for that CPU at event initialization. It also clears the PMU_F_RESERVED bit. The CPU is gone and can not be sampled. With the event still being active on the removed CPU, the CPU event hotplug support in kernel performance subsystem triggers the following function calls on the removed CPU: perf_event_exit_cpu() +--> perf_event_exit_cpu_context() +--> __perf_event_exit_context() +--> __perf_remove_from_context() +--> event_sched_out() +--> cpumsf_pmu_del() +--> cpumsf_pmu_stop() +--> hw_perf_event_update() to stop and remove the event. During removal of the event, the sampling device driver tries to read out the remaining samples from the sample data buffers (SDBs). But they have already been freed (and may have been re-assigned). This may lead to a use after free situation in which case the samples are most likely invalid. In the best case the memory has not been reassigned and still contains valid data. Remedy this situation and check if the CPU is still in reserved state (bit PMU_F_RESERVED set). In this case the SDBs have not been released an contain valid data. This is always the case when the event is removed (and no CPU hotplug off occured). If the PMU_F_RESERVED bit is not set, the SDB buffers are gone.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2024-57875 In the Linux kernel, the following vulnerability has been resolved: block: RCU protect disk->conv_zones_bitmap Ensure that a disk revalidation changing the conventional zones bitmap of a disk does not cause invalid memory references when using the disk_zone_is_conv() helper by RCU protecting the disk->conv_zones_bitmap pointer. disk_zone_is_conv() is modified to operate under the RCU read lock and the function disk_set_conv_zones_bitmap() is added to update a disk conv_zones_bitmap pointer using rcu_replace_pointer() with the disk zone_wplugs_lock spinlock held. disk_free_zone_resources() is modified to call disk_update_zone_resources() with a NULL bitmap pointer to free the disk conv_zones_bitmap. disk_set_conv_zones_bitmap() is also used in disk_update_zone_resources() to set the new (revalidated) bitmap and free the old one.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2024-57879 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: iso: Always release hdev at the end of iso_listen_bis Since hci_get_route holds the device before returning, the hdev should be released with hci_dev_put at the end of iso_listen_bis even if the function returns with an error.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57882 In the Linux kernel, the following vulnerability has been resolved: mptcp: fix TCP options overflow. Syzbot reported the following splat: Oops: general protection fault, probably for non-canonical address 0xdffffc0000000001: 0000 [#1] PREEMPT SMP KASAN PTI KASAN: null-ptr-deref in range [0x0000000000000008-0x000000000000000f] CPU: 1 UID: 0 PID: 5836 Comm: sshd Not tainted 6.13.0-rc3-syzkaller #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 11/25/2024 RIP: 0010:_compound_head include/linux/page-flags.h:242 [inline] RIP: 0010:put_page+0x23/0x260 include/linux/mm.h:1552 Code: 90 90 90 90 90 90 90 55 41 57 41 56 53 49 89 fe 48 bd 00 00 00 00 00 fc ff df e8 f8 5e 12 f8 49 8d 5e 08 48 89 d8 48 c1 e8 03 <80> 3c 28 00 74 08 48 89 df e8 8f c7 78 f8 48 8b 1b 48 89 de 48 83 RSP: 0000:ffffc90003916c90 EFLAGS: 00010202 RAX: 0000000000000001 RBX: 0000000000000008 RCX: ffff888030458000 RDX: 0000000000000100 RSI: 0000000000000000 RDI: 0000000000000000 RBP: dffffc0000000000 R08: ffffffff898ca81d R09: 1ffff110054414ac R10: dffffc0000000000 R11: ffffed10054414ad R12: 0000000000000007 R13: ffff88802a20a542 R14: 0000000000000000 R15: 0000000000000000 FS: 00007f34f496e800(0000) GS:ffff8880b8700000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f9d6ec9ec28 CR3: 000000004d260000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> skb_page_unref include/linux/skbuff_ref.h:43 [inline] __skb_frag_unref include/linux/skbuff_ref.h:56 [inline] skb_release_data+0x483/0x8a0 net/core/skbuff.c:1119 skb_release_all net/core/skbuff.c:1190 [inline] __kfree_skb+0x55/0x70 net/core/skbuff.c:1204 tcp_clean_rtx_queue net/ipv4/tcp_input.c:3436 [inline] tcp_ack+0x2442/0x6bc0 net/ipv4/tcp_input.c:4032 tcp_rcv_state_process+0x8eb/0x44e0 net/ipv4/tcp_input.c:6805 tcp_v4_do_rcv+0x77d/0xc70 net/ipv4/tcp_ipv4.c:1939 tcp_v4_rcv+0x2dc0/0x37f0 net/ipv4/tcp_ipv4.c:2351 ip_protocol_deliver_rcu+0x22e/0x440 net/ipv4/ip_input.c:205 ip_local_deliver_finish+0x341/0x5f0 net/ipv4/ip_input.c:233 NF_HOOK+0x3a4/0x450 include/linux/netfilter.h:314 NF_HOOK+0x3a4/0x450 include/linux/netfilter.h:314 __netif_receive_skb_one_core net/core/dev.c:5672 [inline] __netif_receive_skb+0x2bf/0x650 net/core/dev.c:5785 process_backlog+0x662/0x15b0 net/core/dev.c:6117 __napi_poll+0xcb/0x490 net/core/dev.c:6883 napi_poll net/core/dev.c:6952 [inline] net_rx_action+0x89b/0x1240 net/core/dev.c:7074 handle_softirqs+0x2d4/0x9b0 kernel/softirq.c:561 __do_softirq kernel/softirq.c:595 [inline] invoke_softirq kernel/softirq.c:435 [inline] __irq_exit_rcu+0xf7/0x220 kernel/softirq.c:662 irq_exit_rcu+0x9/0x30 kernel/softirq.c:678 instr_sysvec_apic_timer_interrupt arch/x86/kernel/apic/apic.c:1049 [inline] sysvec_apic_timer_interrupt+0x57/0xc0 arch/x86/kernel/apic/apic.c:1049 asm_sysvec_apic_timer_interrupt+0x1a/0x20 arch/x86/include/asm/idtentry.h:702 RIP: 0033:0x7f34f4519ad5 Code: 85 d2 74 0d 0f 10 02 48 8d 54 24 20 0f 11 44 24 20 64 8b 04 25 18 00 00 00 85 c0 75 27 41 b8 08 00 00 00 b8 0f 01 00 00 0f 05 <48> 3d 00 f0 ff ff 76 75 48 8b 15 24 73 0d 00 f7 d8 64 89 02 48 83 RSP: 002b:00007ffec5b32ce0 EFLAGS: 00000246 RAX: 0000000000000001 RBX: 00000000000668a0 RCX: 00007f34f4519ad5 RDX: 00007ffec5b32d00 RSI: 0000000000000004 RDI: 0000564f4bc6cae0 RBP: 0000564f4bc6b5a0 R08: 0000000000000008 R09: 0000000000000000 R10: 00007ffec5b32de8 R11: 0000000000000246 R12: 0000564f48ea8aa4 R13: 0000000000000001 R14: 0000564f48ea93e8 R15: 00007ffec5b32d68 </TASK> Eric noted a probable shinfo->nr_frags corruption, which indeed occurs. The root cause is a buggy MPTCP option len computation in some circumstances: the ADD_ADDR option should be mutually exclusive with DSS since the blamed commit. Still, mptcp_established_options_add_addr() tries to set the relevant info in mptcp_out_options, if ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57885 In the Linux kernel, the following vulnerability has been resolved: mm/kmemleak: fix sleeping function called from invalid context at print message Address a bug in the kernel that triggers a "sleeping function called from invalid context" warning when /sys/kernel/debug/kmemleak is printed under specific conditions: - CONFIG_PREEMPT_RT=y - Set SELinux as the LSM for the system - Set kptr_restrict to 1 - kmemleak buffer contains at least one item BUG: sleeping function called from invalid context at kernel/locking/spinlock_rt.c:48 in_atomic(): 1, irqs_disabled(): 1, non_block: 0, pid: 136, name: cat preempt_count: 1, expected: 0 RCU nest depth: 2, expected: 2 6 locks held by cat/136: #0: ffff32e64bcbf950 (&p->lock){+.+.}-{3:3}, at: seq_read_iter+0xb8/0xe30 #1: ffffafe6aaa9dea0 (scan_mutex){+.+.}-{3:3}, at: kmemleak_seq_start+0x34/0x128 #3: ffff32e6546b1cd0 (&object->lock){....}-{2:2}, at: kmemleak_seq_show+0x3c/0x1e0 #4: ffffafe6aa8d8560 (rcu_read_lock){....}-{1:2}, at: has_ns_capability_noaudit+0x8/0x1b0 #5: ffffafe6aabbc0f8 (notif_lock){+.+.}-{2:2}, at: avc_compute_av+0xc4/0x3d0 irq event stamp: 136660 hardirqs last enabled at (136659): [<ffffafe6a80fd7a0>] _raw_spin_unlock_irqrestore+0xa8/0xd8 hardirqs last disabled at (136660): [<ffffafe6a80fd85c>] _raw_spin_lock_irqsave+0x8c/0xb0 softirqs last enabled at (0): [<ffffafe6a5d50b28>] copy_process+0x11d8/0x3df8 softirqs last disabled at (0): [<0000000000000000>] 0x0 Preemption disabled at: [<ffffafe6a6598a4c>] kmemleak_seq_show+0x3c/0x1e0 CPU: 1 UID: 0 PID: 136 Comm: cat Tainted: G E 6.11.0-rt7+ #34 Tainted: [E]=UNSIGNED_MODULE Hardware name: linux,dummy-virt (DT) Call trace: dump_backtrace+0xa0/0x128 show_stack+0x1c/0x30 dump_stack_lvl+0xe8/0x198 dump_stack+0x18/0x20 rt_spin_lock+0x8c/0x1a8 avc_perm_nonode+0xa0/0x150 cred_has_capability.isra.0+0x118/0x218 selinux_capable+0x50/0x80 security_capable+0x7c/0xd0 has_ns_capability_noaudit+0x94/0x1b0 has_capability_noaudit+0x20/0x30 restricted_pointer+0x21c/0x4b0 pointer+0x298/0x760 vsnprintf+0x330/0xf70 seq_printf+0x178/0x218 print_unreferenced+0x1a4/0x2d0 kmemleak_seq_show+0xd0/0x1e0 seq_read_iter+0x354/0xe30 seq_read+0x250/0x378 full_proxy_read+0xd8/0x148 vfs_read+0x190/0x918 ksys_read+0xf0/0x1e0 __arm64_sys_read+0x70/0xa8 invoke_syscall.constprop.0+0xd4/0x1d8 el0_svc+0x50/0x158 el0t_64_sync+0x17c/0x180 %pS and %pK, in the same back trace line, are redundant, and %pS can void %pK service in certain contexts. %pS alone already provides the necessary information, and if it cannot resolve the symbol, it falls back to printing the raw address voiding the original intent behind the %pK. Additionally, %pK requires a privilege check CAP_SYSLOG enforced through the LSM, which can trigger a "sleeping function called from invalid context" warning under RT_PREEMPT kernels when the check occurs in an atomic context. This issue may also affect other LSMs. This change avoids the unnecessary privilege check and resolves the sleeping function warning without any loss of information.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57889 In the Linux kernel, the following vulnerability has been resolved: pinctrl: mcp23s08: Fix sleeping in atomic context due to regmap locking If a device uses MCP23xxx IO expander to receive IRQs, the following bug can happen: BUG: sleeping function called from invalid context at kernel/locking/mutex.c:283 in_atomic(): 1, irqs_disabled(): 1, non_block: 0, ... preempt_count: 1, expected: 0 ... Call Trace: ... __might_resched+0x104/0x10e __might_sleep+0x3e/0x62 mutex_lock+0x20/0x4c regmap_lock_mutex+0x10/0x18 regmap_update_bits_base+0x2c/0x66 mcp23s08_irq_set_type+0x1ae/0x1d6 __irq_set_trigger+0x56/0x172 __setup_irq+0x1e6/0x646 request_threaded_irq+0xb6/0x160 ... We observed the problem while experimenting with a touchscreen driver which used MCP23017 IO expander (I2C). The regmap in the pinctrl-mcp23s08 driver uses a mutex for protection from concurrent accesses, which is the default for regmaps without .fast_io, .disable_locking, etc. mcp23s08_irq_set_type() calls regmap_update_bits_base(), and the latter locks the mutex. However, __setup_irq() locks desc->lock spinlock before calling these functions. As a result, the system tries to lock the mutex whole holding the spinlock. It seems, the internal regmap locks are not needed in this driver at all. mcp->lock seems to protect the regmap from concurrent accesses already, except, probably, in mcp_pinconf_get/set. mcp23s08_irq_set_type() and mcp23s08_irq_mask/unmask() are called under chip_bus_lock(), which calls mcp23s08_irq_bus_lock(). The latter takes mcp->lock and enables regmap caching, so that the potentially slow I2C accesses are deferred until chip_bus_unlock(). The accesses to the regmap from mcp23s08_probe_one() do not need additional locking. In all remaining places where the regmap is accessed, except mcp_pinconf_get/set(), the driver already takes mcp->lock. This patch adds locking in mcp_pinconf_get/set() and disables internal locking in the regmap config. Among other things, it fixes the sleeping in atomic context described above.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57892 In the Linux kernel, the following vulnerability has been resolved: ocfs2: fix slab-use-after-free due to dangling pointer dqi_priv When mounting ocfs2 and then remounting it as read-only, a slab-use-after-free occurs after the user uses a syscall to quota_getnextquota. Specifically, sb_dqinfo(sb, type)->dqi_priv is the dangling pointer. During the remounting process, the pointer dqi_priv is freed but is never set as null leaving it to be accessed. Additionally, the read-only option for remounting sets the DQUOT_SUSPENDED flag instead of setting the DQUOT_USAGE_ENABLED flags. Moreover, later in the process of getting the next quota, the function ocfs2_get_next_id is called and only checks the quota usage flags and not the quota suspended flags. To fix this, I set dqi_priv to null when it is freed after remounting with read-only and put a check for DQUOT_SUSPENDED in ocfs2_get_next_id. [akpm@linux-foundation.org: coding-style cleanups]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57896 In the Linux kernel, the following vulnerability has been resolved: btrfs: flush delalloc workers queue before stopping cleaner kthread during unmount During the unmount path, at close_ctree(), we first stop the cleaner kthread, using kthread_stop() which frees the associated task_struct, and then stop and destroy all the work queues. However after we stopped the cleaner we may still have a worker from the delalloc_workers queue running inode.c:submit_compressed_extents(), which calls btrfs_add_delayed_iput(), which in turn tries to wake up the cleaner kthread - which was already destroyed before, resulting in a use-after-free on the task_struct. Syzbot reported this with the following stack traces: BUG: KASAN: slab-use-after-free in __lock_acquire+0x78/0x2100 kernel/locking/lockdep.c:5089 Read of size 8 at addr ffff8880259d2818 by task kworker/u8:3/52 CPU: 1 UID: 0 PID: 52 Comm: kworker/u8:3 Not tainted 6.13.0-rc1-syzkaller-00002-gcdd30ebb1b9f #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 Workqueue: btrfs-delalloc btrfs_work_helper Call Trace: <TASK> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x241/0x360 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x169/0x550 mm/kasan/report.c:489 kasan_report+0x143/0x180 mm/kasan/report.c:602 __lock_acquire+0x78/0x2100 kernel/locking/lockdep.c:5089 lock_acquire+0x1ed/0x550 kernel/locking/lockdep.c:5849 __raw_spin_lock_irqsave include/linux/spinlock_api_smp.h:110 [inline] _raw_spin_lock_irqsave+0xd5/0x120 kernel/locking/spinlock.c:162 class_raw_spinlock_irqsave_constructor include/linux/spinlock.h:551 [inline] try_to_wake_up+0xc2/0x1470 kernel/sched/core.c:4205 submit_compressed_extents+0xdf/0x16e0 fs/btrfs/inode.c:1615 run_ordered_work fs/btrfs/async-thread.c:288 [inline] btrfs_work_helper+0x96f/0xc40 fs/btrfs/async-thread.c:324 process_one_work kernel/workqueue.c:3229 [inline] process_scheduled_works+0xa66/0x1840 kernel/workqueue.c:3310 worker_thread+0x870/0xd30 kernel/workqueue.c:3391 kthread+0x2f0/0x390 kernel/kthread.c:389 ret_from_fork+0x4b/0x80 arch/x86/kernel/process.c:147 ret_from_fork_asm+0x1a/0x30 arch/x86/entry/entry_64.S:244 </TASK> Allocated by task 2: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x3f/0x80 mm/kasan/common.c:68 unpoison_slab_object mm/kasan/common.c:319 [inline] __kasan_slab_alloc+0x66/0x80 mm/kasan/common.c:345 kasan_slab_alloc include/linux/kasan.h:250 [inline] slab_post_alloc_hook mm/slub.c:4104 [inline] slab_alloc_node mm/slub.c:4153 [inline] kmem_cache_alloc_node_noprof+0x1d9/0x380 mm/slub.c:4205 alloc_task_struct_node kernel/fork.c:180 [inline] dup_task_struct+0x57/0x8c0 kernel/fork.c:1113 copy_process+0x5d1/0x3d50 kernel/fork.c:2225 kernel_clone+0x223/0x870 kernel/fork.c:2807 kernel_thread+0x1bc/0x240 kernel/fork.c:2869 create_kthread kernel/kthread.c:412 [inline] kthreadd+0x60d/0x810 kernel/kthread.c:767 ret_from_fork+0x4b/0x80 arch/x86/kernel/process.c:147 ret_from_fork_asm+0x1a/0x30 arch/x86/entry/entry_64.S:244 Freed by task 24: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x3f/0x80 mm/kasan/common.c:68 kasan_save_free_info+0x40/0x50 mm/kasan/generic.c:582 poison_slab_object mm/kasan/common.c:247 [inline] __kasan_slab_free+0x59/0x70 mm/kasan/common.c:264 kasan_slab_free include/linux/kasan.h:233 [inline] slab_free_hook mm/slub.c:2338 [inline] slab_free mm/slub.c:4598 [inline] kmem_cache_free+0x195/0x410 mm/slub.c:4700 put_task_struct include/linux/sched/task.h:144 [inline] delayed_put_task_struct+0x125/0x300 kernel/exit.c:227 rcu_do_batch kernel/rcu/tree.c:2567 [inline] rcu_core+0xaaa/0x17a0 kernel/rcu/tree.c:2823 handle_softirqs+0x2d4/0x9b0 kernel/softirq.c:554 run_ksoftirqd+0xca/0x130 kernel/softirq.c:943 ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57897 In the Linux kernel, the following vulnerability has been resolved: drm/amdkfd: Correct the migration DMA map direction The SVM DMA device map direction should be set the same as the DMA unmap setting, otherwise the DMA core will report the following warning. Before finialize this solution, there're some discussion on the DMA mapping type(stream-based or coherent) in this KFD migration case, followed by https://lore.kernel.org/all/04d4ab32 -45a1-4b88-86ee-fb0f35a0ca40@amd.com/T/. As there's no dma_sync_single_for_*() in the DMA buffer accessed that because this migration operation should be sync properly and automatically. Give that there's might not be a performance problem in various cache sync policy of DMA sync. Therefore, in order to simplify the DMA direction setting alignment, let's set the DMA map direction as BIDIRECTIONAL. [ 150.834218] WARNING: CPU: 8 PID: 1812 at kernel/dma/debug.c:1028 check_unmap+0x1cc/0x930 [ 150.834225] Modules linked in: amdgpu(OE) amdxcp drm_exec(OE) gpu_sched drm_buddy(OE) drm_ttm_helper(OE) ttm(OE) drm_suballoc_helper(OE) drm_display_helper(OE) drm_kms_helper(OE) i2c_algo_bit rpcsec_gss_krb5 auth_rpcgss nfsv4 nfs lockd grace netfs xt_conntrack xt_MASQUERADE nf_conntrack_netlink xfrm_user xfrm_algo iptable_nat xt_addrtype iptable_filter br_netfilter nvme_fabrics overlay nfnetlink_cttimeout nfnetlink openvswitch nsh nf_conncount nf_nat nf_conntrack nf_defrag_ipv6 nf_defrag_ipv4 libcrc32c bridge stp llc sch_fq_codel intel_rapl_msr amd_atl intel_rapl_common snd_hda_codec_realtek snd_hda_codec_generic snd_hda_scodec_component snd_hda_codec_hdmi snd_hda_intel snd_intel_dspcfg edac_mce_amd snd_pci_acp6x snd_hda_codec snd_acp_config snd_hda_core snd_hwdep snd_soc_acpi kvm_amd sunrpc snd_pcm kvm binfmt_misc snd_seq_midi crct10dif_pclmul snd_seq_midi_event ghash_clmulni_intel sha512_ssse3 snd_rawmidi nls_iso8859_1 sha256_ssse3 sha1_ssse3 snd_seq aesni_intel snd_seq_device crypto_simd snd_timer cryptd input_leds [ 150.834310] wmi_bmof serio_raw k10temp rapl snd sp5100_tco ipmi_devintf soundcore ccp ipmi_msghandler cm32181 industrialio mac_hid msr parport_pc ppdev lp parport efi_pstore drm(OE) ip_tables x_tables pci_stub crc32_pclmul nvme ahci libahci i2c_piix4 r8169 nvme_core i2c_designware_pci realtek i2c_ccgx_ucsi video wmi hid_generic cdc_ether usbnet usbhid hid r8152 mii [ 150.834354] CPU: 8 PID: 1812 Comm: rocrtst64 Tainted: G OE 6.10.0-custom #492 [ 150.834358] Hardware name: AMD Majolica-RN/Majolica-RN, BIOS RMJ1009A 06/13/2021 [ 150.834360] RIP: 0010:check_unmap+0x1cc/0x930 [ 150.834363] Code: c0 4c 89 4d c8 e8 34 bf 86 00 4c 8b 4d c8 4c 8b 45 c0 48 8b 4d b8 48 89 c6 41 57 4c 89 ea 48 c7 c7 80 49 b4 84 e8 b4 81 f3 ff <0f> 0b 48 c7 c7 04 83 ac 84 e8 76 ba fc ff 41 8b 76 4c 49 8d 7e 50 [ 150.834365] RSP: 0018:ffffaac5023739e0 EFLAGS: 00010086 [ 150.834368] RAX: 0000000000000000 RBX: ffffffff8566a2e0 RCX: 0000000000000027 [ 150.834370] RDX: ffff8f6a8f621688 RSI: 0000000000000001 RDI: ffff8f6a8f621680 [ 150.834372] RBP: ffffaac502373a30 R08: 00000000000000c9 R09: ffffaac502373850 [ 150.834373] R10: ffffaac502373848 R11: ffffffff84f46328 R12: ffffaac502373a40 [ 150.834375] R13: ffff8f6741045330 R14: ffff8f6741a77700 R15: ffffffff84ac831b [ 150.834377] FS: 00007faf0fc94c00(0000) GS:ffff8f6a8f600000(0000) knlGS:0000000000000000 [ 150.834379] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 150.834381] CR2: 00007faf0b600020 CR3: 000000010a52e000 CR4: 0000000000350ef0 [ 150.834383] Call Trace: [ 150.834385] <TASK> [ 150.834387] ? show_regs+0x6d/0x80 [ 150.834393] ? __warn+0x8c/0x140 [ 150.834397] ? check_unmap+0x1cc/0x930 [ 150.834400] ? report_bug+0x193/0x1a0 [ 150.834406] ? handle_bug+0x46/0x80 [ 150.834410] ? exc_invalid_op+0x1d/0x80 [ 150.834413] ? asm_exc_invalid_op+0x1f/0x30 [ 150.834420] ? check_unmap+0x1cc/0x930 [ 150.834425] debug_dma_unmap_page+0x86/0x90 [ 150.834431] ? srso_return_thunk+0x5/0x5f [ 150.834435] ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57899 In the Linux kernel, the following vulnerability has been resolved: wifi: mac80211: fix mbss changed flags corruption on 32 bit systems On 32-bit systems, the size of an unsigned long is 4 bytes, while a u64 is 8 bytes. Therefore, when using or_each_set_bit(bit, &bits, sizeof(changed) * BITS_PER_BYTE), the code is incorrectly searching for a bit in a 32-bit variable that is expected to be 64 bits in size, leading to incorrect bit finding. Solution: Ensure that the size of the bits variable is correctly adjusted for each architecture. Call Trace: ? show_regs+0x54/0x58 ? __warn+0x6b/0xd4 ? ieee80211_link_info_change_notify+0xcc/0xd4 [mac80211] ? report_bug+0x113/0x150 ? exc_overflow+0x30/0x30 ? handle_bug+0x27/0x44 ? exc_invalid_op+0x18/0x50 ? handle_exception+0xf6/0xf6 ? exc_overflow+0x30/0x30 ? ieee80211_link_info_change_notify+0xcc/0xd4 [mac80211] ? exc_overflow+0x30/0x30 ? ieee80211_link_info_change_notify+0xcc/0xd4 [mac80211] ? ieee80211_mesh_work+0xff/0x260 [mac80211] ? cfg80211_wiphy_work+0x72/0x98 [cfg80211] ? process_one_work+0xf1/0x1fc ? worker_thread+0x2c0/0x3b4 ? kthread+0xc7/0xf0 ? mod_delayed_work_on+0x4c/0x4c ? kthread_complete_and_exit+0x14/0x14 ? ret_from_fork+0x24/0x38 ? kthread_complete_and_exit+0x14/0x14 ? ret_from_fork_asm+0xf/0x14 ? entry_INT80_32+0xf0/0xf0 [restore no-op path for no changes]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-57900 In the Linux kernel, the following vulnerability has been resolved: ila: serialize calls to nf_register_net_hooks() syzbot found a race in ila_add_mapping() [1] commit 031ae72825ce ("ila: call nf_unregister_net_hooks() sooner") attempted to fix a similar issue. Looking at the syzbot repro, we have concurrent ILA_CMD_ADD commands. Add a mutex to make sure at most one thread is calling nf_register_net_hooks(). [1] BUG: KASAN: slab-use-after-free in rht_key_hashfn include/linux/rhashtable.h:159 [inline] BUG: KASAN: slab-use-after-free in __rhashtable_lookup.constprop.0+0x426/0x550 include/linux/rhashtable.h:604 Read of size 4 at addr ffff888028f40008 by task dhcpcd/5501 CPU: 1 UID: 0 PID: 5501 Comm: dhcpcd Not tainted 6.13.0-rc4-syzkaller-00054-gd6ef8b40d075 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 Call Trace: <IRQ> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x116/0x1f0 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0xc3/0x620 mm/kasan/report.c:489 kasan_report+0xd9/0x110 mm/kasan/report.c:602 rht_key_hashfn include/linux/rhashtable.h:159 [inline] __rhashtable_lookup.constprop.0+0x426/0x550 include/linux/rhashtable.h:604 rhashtable_lookup include/linux/rhashtable.h:646 [inline] rhashtable_lookup_fast include/linux/rhashtable.h:672 [inline] ila_lookup_wildcards net/ipv6/ila/ila_xlat.c:127 [inline] ila_xlat_addr net/ipv6/ila/ila_xlat.c:652 [inline] ila_nf_input+0x1ee/0x620 net/ipv6/ila/ila_xlat.c:185 nf_hook_entry_hookfn include/linux/netfilter.h:154 [inline] nf_hook_slow+0xbb/0x200 net/netfilter/core.c:626 nf_hook.constprop.0+0x42e/0x750 include/linux/netfilter.h:269 NF_HOOK include/linux/netfilter.h:312 [inline] ipv6_rcv+0xa4/0x680 net/ipv6/ip6_input.c:309 __netif_receive_skb_one_core+0x12e/0x1e0 net/core/dev.c:5672 __netif_receive_skb+0x1d/0x160 net/core/dev.c:5785 process_backlog+0x443/0x15f0 net/core/dev.c:6117 __napi_poll.constprop.0+0xb7/0x550 net/core/dev.c:6883 napi_poll net/core/dev.c:6952 [inline] net_rx_action+0xa94/0x1010 net/core/dev.c:7074 handle_softirqs+0x213/0x8f0 kernel/softirq.c:561 __do_softirq kernel/softirq.c:595 [inline] invoke_softirq kernel/softirq.c:435 [inline] __irq_exit_rcu+0x109/0x170 kernel/softirq.c:662 irq_exit_rcu+0x9/0x30 kernel/softirq.c:678 instr_sysvec_apic_timer_interrupt arch/x86/kernel/apic/apic.c:1049 [inline] sysvec_apic_timer_interrupt+0xa4/0xc0 arch/x86/kernel/apic/apic.c:1049

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57901 In the Linux kernel, the following vulnerability has been resolved: af_packet: fix vlan_get_protocol_dgram() vs MSG_PEEK Blamed commit forgot MSG_PEEK case, allowing a crash [1] as found by syzbot. Rework vlan_get_protocol_dgram() to not touch skb at all, so that it can be used from many cpus on the same skb. Add a const qualifier to skb argument. [1] skbuff: skb_under_panic: text:ffffffff8a8ccd05 len:29 put:14 head:ffff88807fc8e400 data:ffff88807fc8e3f4 tail:0x11 end:0x140 dev:<NULL> ------------[ cut here ]------------ kernel BUG at net/core/skbuff.c:206 ! Oops: invalid opcode: 0000 [#1] PREEMPT SMP KASAN PTI CPU: 1 UID: 0 PID: 5892 Comm: syz-executor883 Not tainted 6.13.0-rc4-syzkaller-00054-gd6ef8b40d075 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 RIP: 0010:skb_panic net/core/skbuff.c:206 [inline] RIP: 0010:skb_under_panic+0x14b/0x150 net/core/skbuff.c:216 Code: 0b 8d 48 c7 c6 86 d5 25 8e 48 8b 54 24 08 8b 0c 24 44 8b 44 24 04 4d 89 e9 50 41 54 41 57 41 56 e8 5a 69 79 f7 48 83 c4 20 90 <0f> 0b 0f 1f 00 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 f3 RSP: 0018:ffffc900038d7638 EFLAGS: 00010282 RAX: 0000000000000087 RBX: dffffc0000000000 RCX: 609ffd18ea660600 RDX: 0000000000000000 RSI: 0000000080000000 RDI: 0000000000000000 RBP: ffff88802483c8d0 R08: ffffffff817f0a8c R09: 1ffff9200071ae60 R10: dffffc0000000000 R11: fffff5200071ae61 R12: 0000000000000140 R13: ffff88807fc8e400 R14: ffff88807fc8e3f4 R15: 0000000000000011 FS: 00007fbac5e006c0(0000) GS:ffff8880b8700000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007fbac5e00d58 CR3: 000000001238e000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> skb_push+0xe5/0x100 net/core/skbuff.c:2636 vlan_get_protocol_dgram+0x165/0x290 net/packet/af_packet.c:585 packet_recvmsg+0x948/0x1ef0 net/packet/af_packet.c:3552 sock_recvmsg_nosec net/socket.c:1033 [inline] sock_recvmsg+0x22f/0x280 net/socket.c:1055 ____sys_recvmsg+0x1c6/0x480 net/socket.c:2803 ___sys_recvmsg net/socket.c:2845 [inline] do_recvmmsg+0x426/0xab0 net/socket.c:2940 __sys_recvmmsg net/socket.c:3014 [inline] __do_sys_recvmmsg net/socket.c:3037 [inline] __se_sys_recvmmsg net/socket.c:3030 [inline] __x64_sys_recvmmsg+0x199/0x250 net/socket.c:3030 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57902 In the Linux kernel, the following vulnerability has been resolved: af_packet: fix vlan_get_tci() vs MSG_PEEK Blamed commit forgot MSG_PEEK case, allowing a crash [1] as found by syzbot. Rework vlan_get_tci() to not touch skb at all, so that it can be used from many cpus on the same skb. Add a const qualifier to skb argument. [1] skbuff: skb_under_panic: text:ffffffff8a8da482 len:32 put:14 head:ffff88807a1d5800 data:ffff88807a1d5810 tail:0x14 end:0x140 dev:<NULL> ------------[ cut here ]------------ kernel BUG at net/core/skbuff.c:206 ! Oops: invalid opcode: 0000 [#1] PREEMPT SMP KASAN PTI CPU: 0 UID: 0 PID: 5880 Comm: syz-executor172 Not tainted 6.13.0-rc3-syzkaller-00762-g9268abe611b0 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 RIP: 0010:skb_panic net/core/skbuff.c:206 [inline] RIP: 0010:skb_under_panic+0x14b/0x150 net/core/skbuff.c:216 Code: 0b 8d 48 c7 c6 9e 6c 26 8e 48 8b 54 24 08 8b 0c 24 44 8b 44 24 04 4d 89 e9 50 41 54 41 57 41 56 e8 3a 5a 79 f7 48 83 c4 20 90 <0f> 0b 0f 1f 00 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 f3 RSP: 0018:ffffc90003baf5b8 EFLAGS: 00010286 RAX: 0000000000000087 RBX: dffffc0000000000 RCX: 8565c1eec37aa000 RDX: 0000000000000000 RSI: 0000000080000000 RDI: 0000000000000000 RBP: ffff88802616fb50 R08: ffffffff817f0a4c R09: 1ffff92000775e50 R10: dffffc0000000000 R11: fffff52000775e51 R12: 0000000000000140 R13: ffff88807a1d5800 R14: ffff88807a1d5810 R15: 0000000000000014 FS: 00007fa03261f6c0(0000) GS:ffff8880b8600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007ffd65753000 CR3: 0000000031720000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> skb_push+0xe5/0x100 net/core/skbuff.c:2636 vlan_get_tci+0x272/0x550 net/packet/af_packet.c:565 packet_recvmsg+0x13c9/0x1ef0 net/packet/af_packet.c:3616 sock_recvmsg_nosec net/socket.c:1044 [inline] sock_recvmsg+0x22f/0x280 net/socket.c:1066 ____sys_recvmsg+0x1c6/0x480 net/socket.c:2814 ___sys_recvmsg net/socket.c:2856 [inline] do_recvmmsg+0x426/0xab0 net/socket.c:2951 __sys_recvmmsg net/socket.c:3025 [inline] __do_sys_recvmmsg net/socket.c:3048 [inline] __se_sys_recvmmsg net/socket.c:3041 [inline] __x64_sys_recvmmsg+0x199/0x250 net/socket.c:3041 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57904 In the Linux kernel, the following vulnerability has been resolved: iio: adc: at91: call input_free_device() on allocated iio_dev Current implementation of at91_ts_register() calls input_free_deivce() on st->ts_input, however, the err label can be reached before the allocated iio_dev is stored to st->ts_input. Thus call input_free_device() on input instead of st->ts_input.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57906 In the Linux kernel, the following vulnerability has been resolved: iio: adc: ti-ads8688: fix information leak in triggered buffer The 'buffer' local array is used to push data to user space from a triggered buffer, but it does not set values for inactive channels, as it only uses iio_for_each_active_channel() to assign new values. Initialize the array to zero before using it to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57907 In the Linux kernel, the following vulnerability has been resolved: iio: adc: rockchip_saradc: fix information leak in triggered buffer The 'data' local struct is used to push data to user space from a triggered buffer, but it does not set values for inactive channels, as it only uses iio_for_each_active_channel() to assign new values. Initialize the struct to zero before using it to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57908 In the Linux kernel, the following vulnerability has been resolved: iio: imu: kmx61: fix information leak in triggered buffer The 'buffer' local array is used to push data to user space from a triggered buffer, but it does not set values for inactive channels, as it only uses iio_for_each_active_channel() to assign new values. Initialize the array to zero before using it to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57910 In the Linux kernel, the following vulnerability has been resolved: iio: light: vcnl4035: fix information leak in triggered buffer The 'buffer' local array is used to push data to userspace from a triggered buffer, but it does not set an initial value for the single data element, which is an u16 aligned to 8 bytes. That leaves at least 4 bytes uninitialized even after writing an integer value with regmap_read(). Initialize the array to zero before using it to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57911 In the Linux kernel, the following vulnerability has been resolved: iio: dummy: iio_simply_dummy_buffer: fix information leak in triggered buffer The 'data' array is allocated via kmalloc() and it is used to push data to user space from a triggered buffer, but it does not set values for inactive channels, as it only uses iio_for_each_active_channel() to assign new values. Use kzalloc for the memory allocation to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57912 In the Linux kernel, the following vulnerability has been resolved: iio: pressure: zpa2326: fix information leak in triggered buffer The 'sample' local struct is used to push data to user space from a triggered buffer, but it has a hole between the temperature and the timestamp (u32 pressure, u16 temperature, GAP, u64 timestamp). This hole is never initialized. Initialize the struct to zero before using it to avoid pushing uninitialized information to userspace.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57913 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: f_fs: Remove WARN_ON in functionfs_bind This commit addresses an issue related to below kernel panic where panic_on_warn is enabled. It is caused by the unnecessary use of WARN_ON in functionsfs_bind, which easily leads to the following scenarios. 1.adb_write in adbd 2. UDC write via configfs ================= ===================== ->usb_ffs_open_thread() ->UDC write ->open_functionfs() ->configfs_write_iter() ->adb_open() ->gadget_dev_desc_UDC_store() ->adb_write() ->usb_gadget_register_driver_owner ->driver_register() ->StartMonitor() ->bus_add_driver() ->adb_read() ->gadget_bind_driver() <times-out without BIND event> ->configfs_composite_bind() ->usb_add_function() ->open_functionfs() ->ffs_func_bind() ->adb_open() ->functionfs_bind() <ffs->state !=FFS_ACTIVE> The adb_open, adb_read, and adb_write operations are invoked from the daemon, but trying to bind the function is a process that is invoked by UDC write through configfs, which opens up the possibility of a race condition between the two paths. In this race scenario, the kernel panic occurs due to the WARN_ON from functionfs_bind when panic_on_warn is enabled. This commit fixes the kernel panic by removing the unnecessary WARN_ON. Kernel panic - not syncing: kernel: panic_on_warn set ... [ 14.542395] Call trace: [ 14.542464] ffs_func_bind+0x1c8/0x14a8 [ 14.542468] usb_add_function+0xcc/0x1f0 [ 14.542473] configfs_composite_bind+0x468/0x588 [ 14.542478] gadget_bind_driver+0x108/0x27c [ 14.542483] really_probe+0x190/0x374 [ 14.542488] __driver_probe_device+0xa0/0x12c [ 14.542492] driver_probe_device+0x3c/0x220 [ 14.542498] __driver_attach+0x11c/0x1fc [ 14.542502] bus_for_each_dev+0x104/0x160 [ 14.542506] driver_attach+0x24/0x34 [ 14.542510] bus_add_driver+0x154/0x270 [ 14.542514] driver_register+0x68/0x104 [ 14.542518] usb_gadget_register_driver_owner+0x48/0xf4 [ 14.542523] gadget_dev_desc_UDC_store+0xf8/0x144 [ 14.542526] configfs_write_iter+0xf0/0x138

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57916 In the Linux kernel, the following vulnerability has been resolved: misc: microchip: pci1xxxx: Resolve kernel panic during GPIO IRQ handling Resolve kernel panic caused by improper handling of IRQs while accessing GPIO values. This is done by replacing generic_handle_irq with handle_nested_irq.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57925 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix a missing return value check bug In the smb2_send_interim_resp(), if ksmbd_alloc_work_struct() fails to allocate a node, it returns a NULL pointer to the in_work pointer. This can lead to an illegal memory write of in_work->response_buf when allocate_interim_rsp_buf() attempts to perform a kzalloc() on it. To address this issue, incorporating a check for the return value of ksmbd_alloc_work_struct() ensures that the function returns immediately upon allocation failure, thereby preventing the aforementioned illegal memory access.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57926 In the Linux kernel, the following vulnerability has been resolved: drm/mediatek: Set private->all_drm_private[i]->drm to NULL if mtk_drm_bind returns err The pointer need to be set to NULL, otherwise KASAN complains about use-after-free. Because in mtk_drm_bind, all private's drm are set as follows. private->all_drm_private[i]->drm = drm; And drm will be released by drm_dev_put in case mtk_drm_kms_init returns failure. However, the shutdown path still accesses the previous allocated memory in drm_atomic_helper_shutdown. [ 84.874820] watchdog: watchdog0: watchdog did not stop! [ 86.512054] ================================================================== [ 86.513162] BUG: KASAN: use-after-free in drm_atomic_helper_shutdown+0x33c/0x378 [ 86.514258] Read of size 8 at addr ffff0000d46fc068 by task shutdown/1 [ 86.515213] [ 86.515455] CPU: 1 UID: 0 PID: 1 Comm: shutdown Not tainted 6.13.0-rc1-mtk+gfa1a78e5d24b-dirty #55 [ 86.516752] Hardware name: Unknown Product/Unknown Product, BIOS 2022.10 10/01/2022 [ 86.517960] Call trace: [ 86.518333] show_stack+0x20/0x38 (C) [ 86.518891] dump_stack_lvl+0x90/0xd0 [ 86.519443] print_report+0xf8/0x5b0 [ 86.519985] kasan_report+0xb4/0x100 [ 86.520526] __asan_report_load8_noabort+0x20/0x30 [ 86.521240] drm_atomic_helper_shutdown+0x33c/0x378 [ 86.521966] mtk_drm_shutdown+0x54/0x80 [ 86.522546] platform_shutdown+0x64/0x90 [ 86.523137] device_shutdown+0x260/0x5b8 [ 86.523728] kernel_restart+0x78/0xf0 [ 86.524282] __do_sys_reboot+0x258/0x2f0 [ 86.524871] __arm64_sys_reboot+0x90/0xd8 [ 86.525473] invoke_syscall+0x74/0x268 [ 86.526041] el0_svc_common.constprop.0+0xb0/0x240 [ 86.526751] do_el0_svc+0x4c/0x70 [ 86.527251] el0_svc+0x4c/0xc0 [ 86.527719] el0t_64_sync_handler+0x144/0x168 [ 86.528367] el0t_64_sync+0x198/0x1a0 [ 86.528920] [ 86.529157] The buggy address belongs to the physical page: [ 86.529972] page: refcount:0 mapcount:0 mapping:0000000000000000 index:0xffff0000d46fd4d0 pfn:0x1146fc [ 86.531319] flags: 0xbfffc0000000000(node=0|zone=2|lastcpupid=0xffff) [ 86.532267] raw: 0bfffc0000000000 0000000000000000 dead000000000122 0000000000000000 [ 86.533390] raw: ffff0000d46fd4d0 0000000000000000 00000000ffffffff 0000000000000000 [ 86.534511] page dumped because: kasan: bad access detected [ 86.535323] [ 86.535559] Memory state around the buggy address: [ 86.536265] ffff0000d46fbf00: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff [ 86.537314] ffff0000d46fbf80: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff [ 86.538363] >ffff0000d46fc000: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff [ 86.544733] ^ [ 86.551057] ffff0000d46fc080: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff [ 86.557510] ffff0000d46fc100: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff [ 86.563928] ================================================================== [ 86.571093] Disabling lock debugging due to kernel taint [ 86.577642] Unable to handle kernel paging request at virtual address e0e9c0920000000b [ 86.581834] KASAN: maybe wild-memory-access in range [0x0752049000000058-0x075204900000005f] ...

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57939 In the Linux kernel, the following vulnerability has been resolved: riscv: Fix sleeping in invalid context in die() die() can be called in exception handler, and therefore cannot sleep. However, die() takes spinlock_t which can sleep with PREEMPT_RT enabled. That causes the following warning: BUG: sleeping function called from invalid context at kernel/locking/spinlock_rt.c:48 in_atomic(): 1, irqs_disabled(): 1, non_block: 0, pid: 285, name: mutex preempt_count: 110001, expected: 0 RCU nest depth: 0, expected: 0 CPU: 0 UID: 0 PID: 285 Comm: mutex Not tainted 6.12.0-rc7-00022-ge19049cf7d56-dirty #234 Hardware name: riscv-virtio,qemu (DT) Call Trace: dump_backtrace+0x1c/0x24 show_stack+0x2c/0x38 dump_stack_lvl+0x5a/0x72 dump_stack+0x14/0x1c __might_resched+0x130/0x13a rt_spin_lock+0x2a/0x5c die+0x24/0x112 do_trap_insn_illegal+0xa0/0xea _new_vmalloc_restore_context_a0+0xcc/0xd8 Oops - illegal instruction [#1] Switch to use raw_spinlock_t, which does not sleep even with PREEMPT_RT enabled.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57940 In the Linux kernel, the following vulnerability has been resolved: exfat: fix the infinite loop in exfat_readdir() If the file system is corrupted so that a cluster is linked to itself in the cluster chain, and there is an unused directory entry in the cluster, 'dentry' will not be incremented, causing condition 'dentry < max_dentries' unable to prevent an infinite loop. This infinite loop causes s_lock not to be released, and other tasks will hang, such as exfat_sync_fs(). This commit stops traversing the cluster chain when there is unused directory entry in the cluster to avoid this infinite loop.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57945 In the Linux kernel, the following vulnerability has been resolved: riscv: mm: Fix the out of bound issue of vmemmap address In sparse vmemmap model, the virtual address of vmemmap is calculated as: ((struct page *)VMEMMAP_START - (phys_ram_base >> PAGE_SHIFT)). And the struct page's va can be calculated with an offset: (vmemmap + (pfn)). However, when initializing struct pages, kernel actually starts from the first page from the same section that phys_ram_base belongs to. If the first page's physical address is not (phys_ram_base >> PAGE_SHIFT), then we get an va below VMEMMAP_START when calculating va for it's struct page. For example, if phys_ram_base starts from 0x82000000 with pfn 0x82000, the first page in the same section is actually pfn 0x80000. During init_unavailable_range(), we will initialize struct page for pfn 0x80000 with virtual address ((struct page *)VMEMMAP_START - 0x2000), which is below VMEMMAP_START as well as PCI_IO_END. This commit fixes this bug by introducing a new variable 'vmemmap_start_pfn' which is aligned with memory section size and using it to calculate vmemmap address instead of phys_ram_base.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-57949 In the Linux kernel, the following vulnerability has been resolved: irqchip/gic-v3-its: Don't enable interrupts in its_irq_set_vcpu_affinity() The following call-chain leads to enabling interrupts in a nested interrupt disabled section: irq_set_vcpu_affinity() irq_get_desc_lock() raw_spin_lock_irqsave() <--- Disable interrupts its_irq_set_vcpu_affinity() guard(raw_spinlock_irq) <--- Enables interrupts when leaving the guard() irq_put_desc_unlock() <--- Warns because interrupts are enabled This was broken in commit b97e8a2f7130, which replaced the original raw_spin_[un]lock() pair with guard(raw_spinlock_irq). Fix the issue by using guard(raw_spinlock). [ tglx: Massaged change log ]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57951 In the Linux kernel, the following vulnerability has been resolved: hrtimers: Handle CPU state correctly on hotplug Consider a scenario where a CPU transitions from CPUHP_ONLINE to halfway through a CPU hotunplug down to CPUHP_HRTIMERS_PREPARE, and then back to CPUHP_ONLINE: Since hrtimers_prepare_cpu() does not run, cpu_base.hres_active remains set to 1 throughout. However, during a CPU unplug operation, the tick and the clockevents are shut down at CPUHP_AP_TICK_DYING. On return to the online state, for instance CFS incorrectly assumes that the hrtick is already active, and the chance of the clockevent device to transition to oneshot mode is also lost forever for the CPU, unless it goes back to a lower state than CPUHP_HRTIMERS_PREPARE once. This round-trip reveals another issue; cpu_base.online is not set to 1 after the transition, which appears as a WARN_ON_ONCE in enqueue_hrtimer(). Aside of that, the bulk of the per CPU state is not reset either, which means there are dangling pointers in the worst case. Address this by adding a corresponding startup() callback, which resets the stale per CPU state and sets the online flag. [ tglx: Make the new callback unconditionally available, remove the online modification in the prepare() callback and clear the remaining state in the starting callback instead of the prepare callback ]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57953 In the Linux kernel, the following vulnerability has been resolved: rtc: tps6594: Fix integer overflow on 32bit systems The problem is this multiply in tps6594_rtc_set_offset() tmp = offset * TICKS_PER_HOUR; The "tmp" variable is an s64 but "offset" is a long in the (-277774)-277774 range. On 32bit systems a long can hold numbers up to approximately two billion. The number of TICKS_PER_HOUR is really large, (32768 * 3600) or roughly a hundred million. When you start multiplying by a hundred million it doesn't take long to overflow the two billion mark. Probably the safest way to fix this is to change the type of TICKS_PER_HOUR to long long because it's such a large number.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57979 In the Linux kernel, the following vulnerability has been resolved: pps: Fix a use-after-free On a board running ntpd and gpsd, I'm seeing a consistent use-after-free in sys_exit() from gpsd when rebooting: pps pps1: removed ------------[ cut here ]------------ kobject: '(null)' (00000000db4bec24): is not initialized, yet kobject_put() is being called. WARNING: CPU: 2 PID: 440 at lib/kobject.c:734 kobject_put+0x120/0x150 CPU: 2 UID: 299 PID: 440 Comm: gpsd Not tainted 6.11.0-rc6-00308-gb31c44928842 #1 Hardware name: Raspberry Pi 4 Model B Rev 1.1 (DT) pstate: 60000005 (nZCv daif -PAN -UAO -TCO -DIT -SSBS BTYPE=--) pc : kobject_put+0x120/0x150 lr : kobject_put+0x120/0x150 sp : ffffffc0803d3ae0 x29: ffffffc0803d3ae0 x28: ffffff8042dc9738 x27: 0000000000000001 x26: 0000000000000000 x25: ffffff8042dc9040 x24: ffffff8042dc9440 x23: ffffff80402a4620 x22: ffffff8042ef4bd0 x21: ffffff80405cb600 x20: 000000000008001b x19: ffffff8040b3b6e0 x18: 0000000000000000 x17: 0000000000000000 x16: 0000000000000000 x15: 696e6920746f6e20 x14: 7369203a29343263 x13: 205d303434542020 x12: 0000000000000000 x11: 0000000000000000 x10: 0000000000000000 x9 : 0000000000000000 x8 : 0000000000000000 x7 : 0000000000000000 x6 : 0000000000000000 x5 : 0000000000000000 x4 : 0000000000000000 x3 : 0000000000000000 x2 : 0000000000000000 x1 : 0000000000000000 x0 : 0000000000000000 Call trace: kobject_put+0x120/0x150 cdev_put+0x20/0x3c __fput+0x2c4/0x2d8 ____fput+0x1c/0x38 task_work_run+0x70/0xfc do_exit+0x2a0/0x924 do_group_exit+0x34/0x90 get_signal+0x7fc/0x8c0 do_signal+0x128/0x13b4 do_notify_resume+0xdc/0x160 el0_svc+0xd4/0xf8 el0t_64_sync_handler+0x140/0x14c el0t_64_sync+0x190/0x194 ---[ end trace 0000000000000000 ]--- ...followed by more symptoms of corruption, with similar stacks: refcount_t: underflow; use-after-free. kernel BUG at lib/list_debug.c:62! Kernel panic - not syncing: Oops - BUG: Fatal exception This happens because pps_device_destruct() frees the pps_device with the embedded cdev immediately after calling cdev_del(), but, as the comment above cdev_del() notes, fops for previously opened cdevs are still callable even after cdev_del() returns. I think this bug has always been there: I can't explain why it suddenly started happening every time I reboot this particular board. In commit d953e0e837e6 ("pps: Fix a use-after free bug when unregistering a source."), George Spelvin suggested removing the embedded cdev. That seems like the simplest way to fix this, so I've implemented his suggestion, using __register_chrdev() with pps_idr becoming the source of truth for which minor corresponds to which device. But now that pps_idr defines userspace visibility instead of cdev_add(), we need to be sure the pps->dev refcount can't reach zero while userspace can still find it again. So, the idr_remove() call moves to pps_unregister_cdev(), and pps_idr now holds a reference to pps->dev. pps_core: source serial1 got cdev (251:1) <...> pps pps1: removed pps_core: unregistering pps1 pps_core: deallocating pps1

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57980 In the Linux kernel, the following vulnerability has been resolved: media: uvcvideo: Fix double free in error path If the uvc_status_init() function fails to allocate the int_urb, it will free the dev->status pointer but doesn't reset the pointer to NULL. This results in the kfree() call in uvc_status_cleanup() trying to double-free the memory. Fix it by resetting the dev->status pointer to NULL after freeing it. Reviewed by: Ricardo Ribalda <ribalda@chromium.org>

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57990 In the Linux kernel, the following vulnerability has been resolved: wifi: mt76: mt7925: fix off by one in mt7925_load_clc() This comparison should be >= instead of > to prevent an out of bounds read and write.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57994 In the Linux kernel, the following vulnerability has been resolved: ptr_ring: do not block hard interrupts in ptr_ring_resize_multiple() Jakub added a lockdep_assert_no_hardirq() check in __page_pool_put_page() to increase test coverage. syzbot found a splat caused by hard irq blocking in ptr_ring_resize_multiple() [1] As current users of ptr_ring_resize_multiple() do not require hard irqs being masked, replace it to only block BH. Rename helpers to better reflect they are safe against BH only. - ptr_ring_resize_multiple() to ptr_ring_resize_multiple_bh() - skb_array_resize_multiple() to skb_array_resize_multiple_bh() [1] WARNING: CPU: 1 PID: 9150 at net/core/page_pool.c:709 __page_pool_put_page net/core/page_pool.c:709 [inline] WARNING: CPU: 1 PID: 9150 at net/core/page_pool.c:709 page_pool_put_unrefed_netmem+0x157/0xa40 net/core/page_pool.c:780 Modules linked in: CPU: 1 UID: 0 PID: 9150 Comm: syz.1.1052 Not tainted 6.11.0-rc3-syzkaller-00202-gf8669d7b5f5d #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 08/06/2024 RIP: 0010:__page_pool_put_page net/core/page_pool.c:709 [inline] RIP: 0010:page_pool_put_unrefed_netmem+0x157/0xa40 net/core/page_pool.c:780 Code: 74 0e e8 7c aa fb f7 eb 43 e8 75 aa fb f7 eb 3c 65 8b 1d 38 a8 6a 76 31 ff 89 de e8 a3 ae fb f7 85 db 74 0b e8 5a aa fb f7 90 <0f> 0b 90 eb 1d 65 8b 1d 15 a8 6a 76 31 ff 89 de e8 84 ae fb f7 85 RSP: 0018:ffffc9000bda6b58 EFLAGS: 00010083 RAX: ffffffff8997e523 RBX: 0000000000000000 RCX: 0000000000040000 RDX: ffffc9000fbd0000 RSI: 0000000000001842 RDI: 0000000000001843 RBP: 0000000000000000 R08: ffffffff8997df2c R09: 1ffffd40003a000d R10: dffffc0000000000 R11: fffff940003a000e R12: ffffea0001d00040 R13: ffff88802e8a4000 R14: dffffc0000000000 R15: 00000000ffffffff FS: 00007fb7aaf716c0(0000) GS:ffff8880b9300000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007fa15a0d4b72 CR3: 00000000561b0000 CR4: 00000000003506f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> tun_ptr_free drivers/net/tun.c:617 [inline] __ptr_ring_swap_queue include/linux/ptr_ring.h:571 [inline] ptr_ring_resize_multiple_noprof include/linux/ptr_ring.h:643 [inline] tun_queue_resize drivers/net/tun.c:3694 [inline] tun_device_event+0xaaf/0x1080 drivers/net/tun.c:3714 notifier_call_chain+0x19f/0x3e0 kernel/notifier.c:93 call_netdevice_notifiers_extack net/core/dev.c:2032 [inline] call_netdevice_notifiers net/core/dev.c:2046 [inline] dev_change_tx_queue_len+0x158/0x2a0 net/core/dev.c:9024 do_setlink+0xff6/0x41f0 net/core/rtnetlink.c:2923 rtnl_setlink+0x40d/0x5a0 net/core/rtnetlink.c:3201 rtnetlink_rcv_msg+0x73f/0xcf0 net/core/rtnetlink.c:6647 netlink_rcv_skb+0x1e3/0x430 net/netlink/af_netlink.c:2550

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57997 In the Linux kernel, the following vulnerability has been resolved: wifi: wcn36xx: fix channel survey memory allocation size KASAN reported a memory allocation issue in wcn->chan_survey due to incorrect size calculation. This commit uses kcalloc to allocate memory for wcn->chan_survey, ensuring proper initialization and preventing the use of uninitialized values when there are no frames on the channel.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57998 In the Linux kernel, the following vulnerability has been resolved: OPP: add index check to assert to avoid buffer overflow in _read_freq() Pass the freq index to the assert function to make sure we do not read a freq out of the opp->rates[] table when called from the indexed variants: dev_pm_opp_find_freq_exact_indexed() or dev_pm_opp_find_freq_ceil/floor_indexed(). Add a secondary parameter to the assert function, unused for assert_single_clk() then add assert_clk_index() which will check for the clock index when called from the _indexed() find functions.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-57999 In the Linux kernel, the following vulnerability has been resolved: powerpc/pseries/iommu: IOMMU incorrectly marks MMIO range in DDW Power Hypervisor can possibily allocate MMIO window intersecting with Dynamic DMA Window (DDW) range, which is over 32-bit addressing. These MMIO pages needs to be marked as reserved so that IOMMU doesn't map DMA buffers in this range. The current code is not marking these pages correctly which is resulting in LPAR to OOPS while booting. The stack is at below BUG: Unable to handle kernel data access on read at 0xc00800005cd40000 Faulting instruction address: 0xc00000000005cdac Oops: Kernel access of bad area, sig: 11 [#1] LE PAGE_SIZE=64K MMU=Hash SMP NR_CPUS=2048 NUMA pSeries Modules linked in: af_packet rfkill ibmveth(X) lpfc(+) nvmet_fc nvmet nvme_keyring crct10dif_vpmsum nvme_fc nvme_fabrics nvme_core be2net(+) nvme_auth rtc_generic nfsd auth_rpcgss nfs_acl lockd grace sunrpc fuse configfs ip_tables x_tables xfs libcrc32c dm_service_time ibmvfc(X) scsi_transport_fc vmx_crypto gf128mul crc32c_vpmsum dm_mirror dm_region_hash dm_log dm_multipath dm_mod sd_mod scsi_dh_emc scsi_dh_rdac scsi_dh_alua t10_pi crc64_rocksoft_generic crc64_rocksoft sg crc64 scsi_mod Supported: Yes, External CPU: 8 PID: 241 Comm: kworker/8:1 Kdump: loaded Not tainted 6.4.0-150600.23.14-default #1 SLE15-SP6 b44ee71c81261b9e4bab5e0cde1f2ed891d5359b Hardware name: IBM,9080-M9S POWER9 (raw) 0x4e2103 0xf000005 of:IBM,FW950.B0 (VH950_149) hv:phyp pSeries Workqueue: events work_for_cpu_fn NIP: c00000000005cdac LR: c00000000005e830 CTR: 0000000000000000 REGS: c00001400c9ff770 TRAP: 0300 Not tainted (6.4.0-150600.23.14-default) MSR: 800000000280b033 <SF,VEC,VSX,EE,FP,ME,IR,DR,RI,LE> CR: 24228448 XER: 00000001 CFAR: c00000000005cdd4 DAR: c00800005cd40000 DSISR: 40000000 IRQMASK: 0 GPR00: c00000000005e830 c00001400c9ffa10 c000000001987d00 c00001400c4fe800 GPR04: 0000080000000000 0000000000000001 0000000004000000 0000000000800000 GPR08: 0000000004000000 0000000000000001 c00800005cd40000 ffffffffffffffff GPR12: 0000000084228882 c00000000a4c4f00 0000000000000010 0000080000000000 GPR16: c00001400c4fe800 0000000004000000 0800000000000000 c00000006088b800 GPR20: c00001401a7be980 c00001400eff3800 c000000002a2da68 000000000000002b GPR24: c0000000026793a8 c000000002679368 000000000000002a c0000000026793c8 GPR28: 000008007effffff 0000080000000000 0000000000800000 c00001400c4fe800 NIP [c00000000005cdac] iommu_table_reserve_pages+0xac/0x100 LR [c00000000005e830] iommu_init_table+0x80/0x1e0 Call Trace: [c00001400c9ffa10] [c00000000005e810] iommu_init_table+0x60/0x1e0 (unreliable) [c00001400c9ffa90] [c00000000010356c] iommu_bypass_supported_pSeriesLP+0x9cc/0xe40 [c00001400c9ffc30] [c00000000005c300] dma_iommu_dma_supported+0xf0/0x230 [c00001400c9ffcb0] [c00000000024b0c4] dma_supported+0x44/0x90 [c00001400c9ffcd0] [c00000000024b14c] dma_set_mask+0x3c/0x80 [c00001400c9ffd00] [c0080000555b715c] be_probe+0xc4/0xb90 [be2net] [c00001400c9ffdc0] [c000000000986f3c] local_pci_probe+0x6c/0x110 [c00001400c9ffe40] [c000000000188f28] work_for_cpu_fn+0x38/0x60 [c00001400c9ffe70] [c00000000018e454] process_one_work+0x314/0x620 [c00001400c9fff10] [c00000000018f280] worker_thread+0x2b0/0x620 [c00001400c9fff90] [c00000000019bb18] kthread+0x148/0x150 [c00001400c9fffe0] [c00000000000ded8] start_kernel_thread+0x14/0x18 There are 2 issues in the code 1. The index is "int" while the address is "unsigned long". This results in negative value when setting the bitmap. 2. The DMA offset is page shifted but the MMIO range is used as-is (64-bit address). MMIO address needs to be page shifted as well.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-58001 In the Linux kernel, the following vulnerability has been resolved: ocfs2: handle a symlink read error correctly Patch series "Convert ocfs2 to use folios". Mark did a conversion of ocfs2 to use folios and sent it to me as a giant patch for review ;-) So I've redone it as individual patches, and credited Mark for the patches where his code is substantially the same. It's not a bad way to do it; his patch had some bugs and my patches had some bugs. Hopefully all our bugs were different from each other. And hopefully Mark likes all the changes I made to his code! This patch (of 23): If we can't read the buffer, be sure to unlock the page before returning.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58002 In the Linux kernel, the following vulnerability has been resolved: media: uvcvideo: Remove dangling pointers When an async control is written, we copy a pointer to the file handle that started the operation. That pointer will be used when the device is done. Which could be anytime in the future. If the user closes that file descriptor, its structure will be freed, and there will be one dangling pointer per pending async control, that the driver will try to use. Clean all the dangling pointers during release(). To avoid adding a performance penalty in the most common case (no async operation), a counter has been introduced with some logic to make sure that it is properly handled.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58003 In the Linux kernel, the following vulnerability has been resolved: media: i2c: ds90ub9x3: Fix extra fwnode_handle_put() The ub913 and ub953 drivers call fwnode_handle_put(priv->sd.fwnode) as part of their remove process, and if the driver is removed multiple times, eventually leads to put "overflow", possibly causing memory corruption or crash. The fwnode_handle_put() is a leftover from commit 905f88ccebb1 ("media: i2c: ds90ub9x3: Fix sub-device matching"), which changed the code related to the sd.fwnode, but missed removing these fwnode_handle_put() calls.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58006 In the Linux kernel, the following vulnerability has been resolved: PCI: dwc: ep: Prevent changing BAR size/flags in pci_epc_set_bar() In commit 4284c88fff0e ("PCI: designware-ep: Allow pci_epc_set_bar() update inbound map address") set_bar() was modified to support dynamically changing the backing physical address of a BAR that was already configured. This means that set_bar() can be called twice, without ever calling clear_bar() (as calling clear_bar() would clear the BAR's PCI address assigned by the host). This can only be done if the new BAR size/flags does not differ from the existing BAR configuration. Add these missing checks. If we allow set_bar() to set e.g. a new BAR size that differs from the existing BAR size, the new address translation range will be smaller than the BAR size already determined by the host, which would mean that a read past the new BAR size would pass the iATU untranslated, which could allow the host to read memory not belonging to the new struct pci_epf_bar. While at it, add comments which clarifies the support for dynamically changing the physical address of a BAR. (Which was also missing.)

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58007 In the Linux kernel, the following vulnerability has been resolved: soc: qcom: socinfo: Avoid out of bounds read of serial number On MSM8916 devices, the serial number exposed in sysfs is constant and does not change across individual devices. It's always: db410c:/sys/devices/soc0$ cat serial_number 2644893864 The firmware used on MSM8916 exposes SOCINFO_VERSION(0, 8), which does not have support for the serial_num field in the socinfo struct. There is an existing check to avoid exposing the serial number in that case, but it's not correct: When checking the item_size returned by SMEM, we need to make sure the *end* of the serial_num is within bounds, instead of comparing with the *start* offset. The serial_number currently exposed on MSM8916 devices is just an out of bounds read of whatever comes after the socinfo struct in SMEM. Fix this by changing offsetof() to offsetofend(), so that the size of the field is also taken into account.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58010 In the Linux kernel, the following vulnerability has been resolved: binfmt_flat: Fix integer overflow bug on 32 bit systems Most of these sizes and counts are capped at 256MB so the math doesn't result in an integer overflow. The "relocs" count needs to be checked as well. Otherwise on 32bit systems the calculation of "full_data" could be wrong. full_data = data_len + relocs * sizeof(unsigned long);

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58016 In the Linux kernel, the following vulnerability has been resolved: safesetid: check size of policy writes syzbot attempts to write a buffer with a large size to a sysfs entry with writes handled by handle_policy_update(), triggering a warning in kmalloc. Check the size specified for write buffers before allocating. [PM: subject tweak]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58019 In the Linux kernel, the following vulnerability has been resolved: nvkm/gsp: correctly advance the read pointer of GSP message queue A GSP event message consists three parts: message header, RPC header, message body. GSP calculates the number of pages to write from the total size of a GSP message. This behavior can be observed from the movement of the write pointer. However, nvkm takes only the size of RPC header and message body as the message size when advancing the read pointer. When handling a two-page GSP message in the non rollback case, It wrongly takes the message body of the previous message as the message header of the next message. As the "message length" tends to be zero, in the calculation of size needs to be copied (0 - size of (message header)), the size needs to be copied will be "0xffffffxx". It also triggers a kernel panic due to a NULL pointer error. [ 547.614102] msg: 00000f90: ff ff ff ff ff ff ff ff 40 d7 18 fb 8b 00 00 00 ........@....... [ 547.622533] msg: 00000fa0: 00 00 00 00 ff ff ff ff ff ff ff ff 00 00 00 00 ................ [ 547.630965] msg: 00000fb0: ff ff ff ff ff ff ff ff 00 00 00 00 ff ff ff ff ................ [ 547.639397] msg: 00000fc0: ff ff ff ff 00 00 00 00 ff ff ff ff ff ff ff ff ................ [ 547.647832] nvkm 0000:c1:00.0: gsp: peek msg rpc fn:0 len:0x0/0xffffffffffffffe0 [ 547.655225] nvkm 0000:c1:00.0: gsp: get msg rpc fn:0 len:0x0/0xffffffffffffffe0 [ 547.662532] BUG: kernel NULL pointer dereference, address: 0000000000000020 [ 547.669485] #PF: supervisor read access in kernel mode [ 547.674624] #PF: error_code(0x0000) - not-present page [ 547.679755] PGD 0 P4D 0 [ 547.682294] Oops: 0000 [#1] PREEMPT SMP NOPTI [ 547.686643] CPU: 22 PID: 322 Comm: kworker/22:1 Tainted: G E 6.9.0-rc6+ #1 [ 547.694893] Hardware name: ASRockRack 1U1G-MILAN/N/ROMED8-NL, BIOS L3.12E 09/06/2022 [ 547.702626] Workqueue: events r535_gsp_msgq_work [nvkm] [ 547.707921] RIP: 0010:r535_gsp_msg_recv+0x87/0x230 [nvkm] [ 547.713375] Code: 00 8b 70 08 48 89 e1 31 d2 4c 89 f7 e8 12 f5 ff ff 48 89 c5 48 85 c0 0f 84 cf 00 00 00 48 81 fd 00 f0 ff ff 0f 87 c4 00 00 00 <8b> 55 10 41 8b 46 30 85 d2 0f 85 f6 00 00 00 83 f8 04 76 10 ba 05 [ 547.732119] RSP: 0018:ffffabe440f87e10 EFLAGS: 00010203 [ 547.737335] RAX: 0000000000000010 RBX: 0000000000000008 RCX: 000000000000003f [ 547.744461] RDX: 0000000000000000 RSI: ffffabe4480a8030 RDI: 0000000000000010 [ 547.751585] RBP: 0000000000000010 R08: 0000000000000000 R09: ffffabe440f87bb0 [ 547.758707] R10: ffffabe440f87dc8 R11: 0000000000000010 R12: 0000000000000000 [ 547.765834] R13: 0000000000000000 R14: ffff9351df1e5000 R15: 0000000000000000 [ 547.772958] FS: 0000000000000000(0000) GS:ffff93708eb00000(0000) knlGS:0000000000000000 [ 547.781035] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 547.786771] CR2: 0000000000000020 CR3: 00000003cc220002 CR4: 0000000000770ef0 [ 547.793896] PKRU: 55555554 [ 547.796600] Call Trace: [ 547.799046] <TASK> [ 547.801152] ? __die+0x20/0x70 [ 547.804211] ? page_fault_oops+0x75/0x170 [ 547.808221] ? print_hex_dump+0x100/0x160 [ 547.812226] ? exc_page_fault+0x64/0x150 [ 547.816152] ? asm_exc_page_fault+0x22/0x30 [ 547.820341] ? r535_gsp_msg_recv+0x87/0x230 [nvkm] [ 547.825184] r535_gsp_msgq_work+0x42/0x50 [nvkm] [ 547.829845] process_one_work+0x196/0x3d0 [ 547.833861] worker_thread+0x2fc/0x410 [ 547.837613] ? __pfx_worker_thread+0x10/0x10 [ 547.841885] kthread+0xdf/0x110 [ 547.845031] ? __pfx_kthread+0x10/0x10 [ 547.848775] ret_from_fork+0x30/0x50 [ 547.852354] ? __pfx_kthread+0x10/0x10 [ 547.856097] ret_from_fork_asm+0x1a/0x30 [ 547.860019] </TASK> [ 547.862208] Modules linked in: nvkm(E) gsp_log(E) snd_seq_dummy(E) snd_hrtimer(E) snd_seq(E) snd_timer(E) snd_seq_device(E) snd(E) soundcore(E) rfkill(E) qrtr(E) vfat(E) fat(E) ipmi_ssif(E) amd_atl(E) intel_rapl_msr(E) intel_rapl_common(E) amd64_edac(E) mlx5_ib(E) edac_mce_amd(E) kvm_amd ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2024-58020 In the Linux kernel, the following vulnerability has been resolved: HID: multitouch: Add NULL check in mt_input_configured devm_kasprintf() can return a NULL pointer on failure,but this returned value in mt_input_configured() is not checked. Add NULL check in mt_input_configured(), to handle kernel NULL pointer dereference error.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2024-58034 In the Linux kernel, the following vulnerability has been resolved: memory: tegra20-emc: fix an OF node reference bug in tegra_emc_find_node_by_ram_code() As of_find_node_by_name() release the reference of the argument device node, tegra_emc_find_node_by_ram_code() releases some device nodes while still in use, resulting in possible UAFs. According to the bindings and the in-tree DTS files, the "emc-tables" node is always device's child node with the property "nvidia,use-ram-code", and the "lpddr2" node is a child of the "emc-tables" node. Thus utilize the for_each_child_of_node() macro and of_get_child_by_name() instead of of_find_node_by_name() to simplify the code. This bug was found by an experimental verification tool that I am developing. [krzysztof: applied v1, adjust the commit msg to incorporate v2 parts]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58051 In the Linux kernel, the following vulnerability has been resolved: ipmi: ipmb: Add check devm_kasprintf() returned value devm_kasprintf() can return a NULL pointer on failure but this returned value is not checked.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58052 In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: Fix potential NULL pointer dereference in atomctrl_get_smc_sclk_range_table The function atomctrl_get_smc_sclk_range_table() does not check the return value of smu_atom_get_data_table(). If smu_atom_get_data_table() fails to retrieve SMU_Info table, it returns NULL which is later dereferenced. Found by Linux Verification Center (linuxtesting.org) with SVACE. In practice this should never happen as this code only gets called on polaris chips and the vbios data table will always be present on those chips.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58055 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: f_tcm: Don't free command immediately Don't prematurely free the command. Wait for the status completion of the sense status. It can be freed then. Otherwise we will double-free the command.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58056 In the Linux kernel, the following vulnerability has been resolved: remoteproc: core: Fix ida_free call while not allocated In the rproc_alloc() function, on error, put_device(&rproc->dev) is called, leading to the call of the rproc_type_release() function. An error can occurs before ida_alloc is called. In such case in rproc_type_release(), the condition (rproc->index >= 0) is true as rproc->index has been initialized to 0. ida_free() is called reporting a warning: [ 4.181906] WARNING: CPU: 1 PID: 24 at lib/idr.c:525 ida_free+0x100/0x164 [ 4.186378] stm32-display-dsi 5a000000.dsi: Fixed dependency cycle(s) with /soc/dsi@5a000000/panel@0 [ 4.188854] ida_free called for id=0 which is not allocated. [ 4.198256] mipi-dsi 5a000000.dsi.0: Fixed dependency cycle(s) with /soc/dsi@5a000000 [ 4.203556] Modules linked in: panel_orisetech_otm8009a dw_mipi_dsi_stm(+) gpu_sched dw_mipi_dsi stm32_rproc stm32_crc32 stm32_ipcc(+) optee(+) [ 4.224307] CPU: 1 UID: 0 PID: 24 Comm: kworker/u10:0 Not tainted 6.12.0 #442 [ 4.231481] Hardware name: STM32 (Device Tree Support) [ 4.236627] Workqueue: events_unbound deferred_probe_work_func [ 4.242504] Call trace: [ 4.242522] unwind_backtrace from show_stack+0x10/0x14 [ 4.250218] show_stack from dump_stack_lvl+0x50/0x64 [ 4.255274] dump_stack_lvl from __warn+0x80/0x12c [ 4.260134] __warn from warn_slowpath_fmt+0x114/0x188 [ 4.265199] warn_slowpath_fmt from ida_free+0x100/0x164 [ 4.270565] ida_free from rproc_type_release+0x38/0x60 [ 4.275832] rproc_type_release from device_release+0x30/0xa0 [ 4.281601] device_release from kobject_put+0xc4/0x294 [ 4.286762] kobject_put from rproc_alloc.part.0+0x208/0x28c [ 4.292430] rproc_alloc.part.0 from devm_rproc_alloc+0x80/0xc4 [ 4.298393] devm_rproc_alloc from stm32_rproc_probe+0xd0/0x844 [stm32_rproc] [ 4.305575] stm32_rproc_probe [stm32_rproc] from platform_probe+0x5c/0xbc Calling ida_alloc earlier in rproc_alloc ensures that the rproc->index is properly set.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58058 In the Linux kernel, the following vulnerability has been resolved: ubifs: skip dumping tnc tree when zroot is null Clearing slab cache will free all znode in memory and make c->zroot.znode = NULL, then dumping tnc tree will access c->zroot.znode which cause null pointer dereference.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58063 In the Linux kernel, the following vulnerability has been resolved: wifi: rtlwifi: fix memory leaks and invalid access at probe error path Deinitialize at reverse order when probe fails. When init_sw_vars fails, rtl_deinit_core should not be called, specially now that it destroys the rtl_wq workqueue. And call rtl_pci_deinit and deinit_sw_vars, otherwise, memory will be leaked. Remove pci_set_drvdata call as it will already be cleaned up by the core driver code and could lead to memory leaks too. cf. commit 8d450935ae7f ("wireless: rtlwifi: remove unnecessary pci_set_drvdata()") and commit 3d86b93064c7 ("rtlwifi: Fix PCI probe error path orphaned memory").

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58068 In the Linux kernel, the following vulnerability has been resolved: OPP: fix dev_pm_opp_find_bw_*() when bandwidth table not initialized If a driver calls dev_pm_opp_find_bw_ceil/floor() the retrieve bandwidth from the OPP table but the bandwidth table was not created because the interconnect properties were missing in the OPP consumer node, the kernel will crash with: Unable to handle kernel NULL pointer dereference at virtual address 0000000000000004 ... pc : _read_bw+0x8/0x10 lr : _opp_table_find_key+0x9c/0x174 ... Call trace: _read_bw+0x8/0x10 (P) _opp_table_find_key+0x9c/0x174 (L) _find_key+0x98/0x168 dev_pm_opp_find_bw_ceil+0x50/0x88 ... In order to fix the crash, create an assert function to check if the bandwidth table was created before trying to get a bandwidth with _read_bw().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58069 In the Linux kernel, the following vulnerability has been resolved: rtc: pcf85063: fix potential OOB write in PCF85063 NVMEM read The nvmem interface supports variable buffer sizes, while the regmap interface operates with fixed-size storage. If an nvmem client uses a buffer size less than 4 bytes, regmap_read will write out of bounds as it expects the buffer to point at an unsigned int. Fix this by using an intermediary unsigned int to hold the value.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58070 In the Linux kernel, the following vulnerability has been resolved: bpf: bpf_local_storage: Always use bpf_mem_alloc in PREEMPT_RT In PREEMPT_RT, kmalloc(GFP_ATOMIC) is still not safe in non preemptible context. bpf_mem_alloc must be used in PREEMPT_RT. This patch is to enforce bpf_mem_alloc in the bpf_local_storage when CONFIG_PREEMPT_RT is enabled. [ 35.118559] BUG: sleeping function called from invalid context at kernel/locking/spinlock_rt.c:48 [ 35.118566] in_atomic(): 1, irqs_disabled(): 0, non_block: 0, pid: 1832, name: test_progs [ 35.118569] preempt_count: 1, expected: 0 [ 35.118571] RCU nest depth: 1, expected: 1 [ 35.118577] INFO: lockdep is turned off. ... [ 35.118647] __might_resched+0x433/0x5b0 [ 35.118677] rt_spin_lock+0xc3/0x290 [ 35.118700] ___slab_alloc+0x72/0xc40 [ 35.118723] __kmalloc_noprof+0x13f/0x4e0 [ 35.118732] bpf_map_kzalloc+0xe5/0x220 [ 35.118740] bpf_selem_alloc+0x1d2/0x7b0 [ 35.118755] bpf_local_storage_update+0x2fa/0x8b0 [ 35.118784] bpf_sk_storage_get_tracing+0x15a/0x1d0 [ 35.118791] bpf_prog_9a118d86fca78ebb_trace_inet_sock_set_state+0x44/0x66 [ 35.118795] bpf_trace_run3+0x222/0x400 [ 35.118820] __bpf_trace_inet_sock_set_state+0x11/0x20 [ 35.118824] trace_inet_sock_set_state+0x112/0x130 [ 35.118830] inet_sk_state_store+0x41/0x90 [ 35.118836] tcp_set_state+0x3b3/0x640 There is no need to adjust the gfp_flags passing to the bpf_mem_cache_alloc_flags() which only honors the GFP_KERNEL. The verifier has ensured GFP_KERNEL is passed only in sleepable context. It has been an old issue since the first introduction of the bpf_local_storage ~5 years ago, so this patch targets the bpf-next. bpf_mem_alloc is needed to solve it, so the Fixes tag is set to the commit when bpf_mem_alloc was first used in the bpf_local_storage.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58071 In the Linux kernel, the following vulnerability has been resolved: team: prevent adding a device which is already a team device lower Prevent adding a device which is already a team device lower, e.g. adding veth0 if vlan1 was already added and veth0 is a lower of vlan1. This is not useful in practice and can lead to recursive locking: $ ip link add veth0 type veth peer name veth1 $ ip link set veth0 up $ ip link set veth1 up $ ip link add link veth0 name veth0.1 type vlan protocol 802.1Q id 1 $ ip link add team0 type team $ ip link set veth0.1 down $ ip link set veth0.1 master team0 team0: Port device veth0.1 added $ ip link set veth0 down $ ip link set veth0 master team0 ============================================ WARNING: possible recursive locking detected 6.13.0-rc2-virtme-00441-ga14a429069bb #46 Not tainted -------------------------------------------- ip/7684 is trying to acquire lock: ffff888016848e00 (team->team_lock_key){+.+.}-{4:4}, at: team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) but task is already holding lock: ffff888016848e00 (team->team_lock_key){+.+.}-{4:4}, at: team_add_slave (drivers/net/team/team_core.c:1147 drivers/net/team/team_core.c:1977) other info that might help us debug this: Possible unsafe locking scenario: CPU0 ---- lock(team->team_lock_key); lock(team->team_lock_key); *** DEADLOCK *** May be due to missing lock nesting notation 2 locks held by ip/7684: stack backtrace: CPU: 3 UID: 0 PID: 7684 Comm: ip Not tainted 6.13.0-rc2-virtme-00441-ga14a429069bb #46 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.16.3-debian-1.16.3-2 04/01/2014 Call Trace: <TASK> dump_stack_lvl (lib/dump_stack.c:122) print_deadlock_bug.cold (kernel/locking/lockdep.c:3040) __lock_acquire (kernel/locking/lockdep.c:3893 kernel/locking/lockdep.c:5226) ? netlink_broadcast_filtered (net/netlink/af_netlink.c:1548) lock_acquire.part.0 (kernel/locking/lockdep.c:467 kernel/locking/lockdep.c:5851) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) ? trace_lock_acquire (./include/trace/events/lock.h:24 (discriminator 2)) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) ? lock_acquire (kernel/locking/lockdep.c:5822) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) __mutex_lock (kernel/locking/mutex.c:587 kernel/locking/mutex.c:735) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) ? fib_sync_up (net/ipv4/fib_semantics.c:2167) ? team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) team_device_event (drivers/net/team/team_core.c:2928 drivers/net/team/team_core.c:2951 drivers/net/team/team_core.c:2973) notifier_call_chain (kernel/notifier.c:85) call_netdevice_notifiers_info (net/core/dev.c:1996) __dev_notify_flags (net/core/dev.c:8993) ? __dev_change_flags (net/core/dev.c:8975) dev_change_flags (net/core/dev.c:9027) vlan_device_event (net/8021q/vlan.c:85 net/8021q/vlan.c:470) ? br_device_event (net/bridge/br.c:143) notifier_call_chain (kernel/notifier.c:85) call_netdevice_notifiers_info (net/core/dev.c:1996) dev_open (net/core/dev.c:1519 net/core/dev.c:1505) team_add_slave (drivers/net/team/team_core.c:1219 drivers/net/team/team_core.c:1977) ? __pfx_team_add_slave (drivers/net/team/team_core.c:1972) do_set_master (net/core/rtnetlink.c:2917) do_setlink.isra.0 (net/core/rtnetlink.c:3117)

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58076 In the Linux kernel, the following vulnerability has been resolved: clk: qcom: gcc-sm6350: Add missing parent_map for two clocks If a clk_rcg2 has a parent, it should also have parent_map defined, otherwise we'll get a NULL pointer dereference when calling clk_set_rate like the following: [ 3.388105] Call trace: [ 3.390664] qcom_find_src_index+0x3c/0x70 (P) [ 3.395301] qcom_find_src_index+0x1c/0x70 (L) [ 3.399934] _freq_tbl_determine_rate+0x48/0x100 [ 3.404753] clk_rcg2_determine_rate+0x1c/0x28 [ 3.409387] clk_core_determine_round_nolock+0x58/0xe4 [ 3.421414] clk_core_round_rate_nolock+0x48/0xfc [ 3.432974] clk_core_round_rate_nolock+0xd0/0xfc [ 3.444483] clk_core_set_rate_nolock+0x8c/0x300 [ 3.455886] clk_set_rate+0x38/0x14c Add the parent_map property for two clocks where it's missing and also un-inline the parent_data as well to keep the matching parent_map and parent_data together.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58080 In the Linux kernel, the following vulnerability has been resolved: clk: qcom: dispcc-sm6350: Add missing parent_map for a clock If a clk_rcg2 has a parent, it should also have parent_map defined, otherwise we'll get a NULL pointer dereference when calling clk_set_rate like the following: [ 3.388105] Call trace: [ 3.390664] qcom_find_src_index+0x3c/0x70 (P) [ 3.395301] qcom_find_src_index+0x1c/0x70 (L) [ 3.399934] _freq_tbl_determine_rate+0x48/0x100 [ 3.404753] clk_rcg2_determine_rate+0x1c/0x28 [ 3.409387] clk_core_determine_round_nolock+0x58/0xe4 [ 3.421414] clk_core_round_rate_nolock+0x48/0xfc [ 3.432974] clk_core_round_rate_nolock+0xd0/0xfc [ 3.444483] clk_core_set_rate_nolock+0x8c/0x300 [ 3.455886] clk_set_rate+0x38/0x14c Add the parent_map property for the clock where it's missing and also un-inline the parent_data as well to keep the matching parent_map and parent_data together.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58081 In the Linux kernel, the following vulnerability has been resolved: clk: mmp2: call pm_genpd_init() only after genpd.name is set Setting the genpd's struct device's name with dev_set_name() is happening within pm_genpd_init(). If it remains NULL, things can blow up later, such as when crafting the devfs hierarchy for the power domain: Unable to handle kernel NULL pointer dereference at virtual address 00000000 when read ... Call trace: strlen from start_creating+0x90/0x138 start_creating from debugfs_create_dir+0x20/0x178 debugfs_create_dir from genpd_debug_add.part.0+0x4c/0x144 genpd_debug_add.part.0 from genpd_debug_init+0x74/0x90 genpd_debug_init from do_one_initcall+0x5c/0x244 do_one_initcall from kernel_init_freeable+0x19c/0x1f4 kernel_init_freeable from kernel_init+0x1c/0x12c kernel_init from ret_from_fork+0x14/0x28 Bisecting tracks this crash back to commit 899f44531fe6 ("pmdomain: core: Add GENPD_FLAG_DEV_NAME_FW flag"), which exchanges use of genpd->name with dev_name(&genpd->dev) in genpd_debug_add.part().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58082 In the Linux kernel, the following vulnerability has been resolved: media: nuvoton: Fix an error check in npcm_video_ece_init() When function of_find_device_by_node() fails, it returns NULL instead of an error code. So the corresponding error check logic should be modified to check whether the return value is NULL and set the error code to be returned as -ENODEV.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58085 In the Linux kernel, the following vulnerability has been resolved: tomoyo: don't emit warning in tomoyo_write_control() syzbot is reporting too large allocation warning at tomoyo_write_control(), for one can write a very very long line without new line character. To fix this warning, I use __GFP_NOWARN rather than checking for KMALLOC_MAX_SIZE, for practically a valid line should be always shorter than 32KB where the "too small to fail" memory-allocation rule applies. One might try to write a valid line that is longer than 32KB, but such request will likely fail with -ENOMEM. Therefore, I feel that separately returning -EINVAL when a line is longer than KMALLOC_MAX_SIZE is redundant. There is no need to distinguish over-32KB and over-KMALLOC_MAX_SIZE.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58086 In the Linux kernel, the following vulnerability has been resolved: drm/v3d: Stop active perfmon if it is being destroyed If the active performance monitor (`v3d->active_perfmon`) is being destroyed, stop it first. Currently, the active perfmon is not stopped during destruction, leaving the `v3d->active_perfmon` pointer stale. This can lead to undefined behavior and instability. This patch ensures that the active perfmon is stopped before being destroyed, aligning with the behavior introduced in commit 7d1fd3638ee3 ("drm/v3d: Stop the active perfmon before being destroyed").

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2024-58087 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix racy issue from session lookup and expire Increment the session reference count within the lock for lookup to avoid racy issue with session expire.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2024-58088 In the Linux kernel, the following vulnerability has been resolved: bpf: Fix deadlock when freeing cgroup storage The following commit bc235cdb423a ("bpf: Prevent deadlock from recursive bpf_task_storage_[get|delete]") first introduced deadlock prevention for fentry/fexit programs attaching on bpf_task_storage helpers. That commit also employed the logic in map free path in its v6 version. Later bpf_cgrp_storage was first introduced in c4bcfb38a95e ("bpf: Implement cgroup storage available to non-cgroup-attached bpf progs") which faces the same issue as bpf_task_storage, instead of its busy counter, NULL was passed to bpf_local_storage_map_free() which opened a window to cause deadlock: <TASK> (acquiring local_storage->lock) _raw_spin_lock_irqsave+0x3d/0x50 bpf_local_storage_update+0xd1/0x460 bpf_cgrp_storage_get+0x109/0x130 bpf_prog_a4d4a370ba857314_cgrp_ptr+0x139/0x170 ? __bpf_prog_enter_recur+0x16/0x80 bpf_trampoline_6442485186+0x43/0xa4 cgroup_storage_ptr+0x9/0x20 (holding local_storage->lock) bpf_selem_unlink_storage_nolock.constprop.0+0x135/0x160 bpf_selem_unlink_storage+0x6f/0x110 bpf_local_storage_map_free+0xa2/0x110 bpf_map_free_deferred+0x5b/0x90 process_one_work+0x17c/0x390 worker_thread+0x251/0x360 kthread+0xd2/0x100 ret_from_fork+0x34/0x50 ret_from_fork_asm+0x1a/0x30 </TASK> Progs: - A: SEC("fentry/cgroup_storage_ptr") - cgid (BPF_MAP_TYPE_HASH) Record the id of the cgroup the current task belonging to in this hash map, using the address of the cgroup as the map key. - cgrpa (BPF_MAP_TYPE_CGRP_STORAGE) If current task is a kworker, lookup the above hash map using function parameter @owner as the key to get its corresponding cgroup id which is then used to get a trusted pointer to the cgroup through bpf_cgroup_from_id(). This trusted pointer can then be passed to bpf_cgrp_storage_get() to finally trigger the deadlock issue. - B: SEC("tp_btf/sys_enter") - cgrpb (BPF_MAP_TYPE_CGRP_STORAGE) The only purpose of this prog is to fill Prog A's hash map by calling bpf_cgrp_storage_get() for as many userspace tasks as possible. Steps to reproduce: - Run A; - while (true) { Run B; Destroy B; } Fix this issue by passing its busy counter to the free procedure so it can be properly incremented before storage/smap locking.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2024-58093 In the Linux kernel, the following vulnerability has been resolved: PCI/ASPM: Fix link state exit during switch upstream function removal Before 456d8aa37d0f ("PCI/ASPM: Disable ASPM on MFD function removal to avoid use-after-free"), we would free the ASPM link only after the last function on the bus pertaining to the given link was removed. That was too late. If function 0 is removed before sibling function, link->downstream would point to free'd memory after. After above change, we freed the ASPM parent link state upon any function removal on the bus pertaining to a given link. That is too early. If the link is to a PCIe switch with MFD on the upstream port, then removing functions other than 0 first would free a link which still remains parent_link to the remaining downstream ports. The resulting GPFs are especially frequent during hot-unplug, because pciehp removes devices on the link bus in reverse order. On that switch, function 0 is the virtual P2P bridge to the internal bus. Free exactly when function 0 is removed -- before the parent link is obsolete, but after all subordinate links are gone. [kwilczynski: commit log]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-1176 A vulnerability was found in GNU Binutils 2.43 and classified as critical. This issue affects the function _bfd_elf_gc_mark_rsec of the file elflink.c of the component ld. The manipulation leads to heap-based buffer overflow. The attack may be initiated remotely. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. The patch is named f9978defb6fab0bd8583942d97c112b0932ac814. It is recommended to apply a patch to fix this issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-1178 A vulnerability was found in GNU Binutils 2.43. It has been declared as problematic. Affected by this vulnerability is the function bfd_putl64 of the file libbfd.c of the component ld. The manipulation leads to memory corruption. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The identifier of the patch is 75086e9de1707281172cc77f178e7949a4414ed0. It is recommended to apply a patch to fix this issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-1180 A vulnerability classified as problematic has been found in GNU Binutils 2.43. This affects the function _bfd_elf_write_section_eh_frame of the file bfd/elf-eh-frame.c of the component ld. The manipulation leads to memory corruption. It is possible to initiate the attack remotely. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. It is recommended to apply a patch to fix this issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-1181 A vulnerability classified as critical was found in GNU Binutils 2.43. This vulnerability affects the function _bfd_elf_gc_mark_rsec of the file bfd/elflink.c of the component ld. The manipulation leads to memory corruption. The attack can be initiated remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The name of the patch is 931494c9a89558acb36a03a340c01726545eef24. It is recommended to apply a patch to fix this issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-1182 A vulnerability, which was classified as critical, was found in GNU Binutils 2.43. Affected is the function bfd_elf_reloc_symbol_deleted_p of the file bfd/elflink.c of the component ld. The manipulation leads to memory corruption. It is possible to launch the attack remotely. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The patch is identified as b425859021d17adf62f06fb904797cf8642986ad. It is recommended to apply a patch to fix this issue.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-2148 A vulnerability was found in PyTorch 2.6.0+cu124. It has been declared as critical. Affected by this vulnerability is the function torch.ops.profiler._call_end_callbacks_on_jit_fut of the component Tuple Handler. The manipulation of the argument None leads to memory corruption. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-2149 A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnq_Sigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zero_point leads to improper initialization. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-2953 A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The security policy of the project warns to use unknown models which might establish malicious effects.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-2998 A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-3277 An integer overflow can be triggered in SQLite’s `concat_ws()` function. The resulting, truncated integer is then used to allocate a buffer. When SQLite then writes the resulting string to the buffer, it uses the original, untruncated size and thus a wild Heap Buffer overflow of size ~4GB can be triggered. This can result in arbitrary code execution.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-3576 A vulnerability in the MIT Kerberos implementation allows GSSAPI-protected messages using RC4-HMAC-MD5 to be spoofed due to weaknesses in the MD5 checksum design. If RC4 is preferred over stronger encryption types, an attacker could exploit MD5 collisions to forge message integrity codes. This may lead to unauthorized message tampering.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-3730 A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue. The security policy of the project warns to use unknown models which might establish malicious effects.

nemotron_nano_12b_v2_vl_v150
nim-bigcode-starcoder2-7b-v1.14.1
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-4138 Allows the extraction filter to be ignored, allowing symlink targets to point outside the destination directory, and the modification of some file metadata. You are affected by this vulnerability if using the tarfile module to extract untrusted tar archives using TarFile.extractall() or TarFile.extract() using the filter= parameter with a value of "data" or "tar". See the tarfile extraction filters documentation https://docs.python.org/3/library/tarfile.html#tarfile-extraction-filter  for more information. Note that for Python 3.14 or later the default value of filter= changed from "no filtering" to `"data", so if you are relying on this new default behavior then your usage is also affected. Note that none of these vulnerabilities significantly affect the installation of source distributions which are tar archives as source distributions already allow arbitrary code execution during the build process. However when evaluating source distributions it's important to avoid installing source distributions with suspicious links.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-4330 Allows the extraction filter to be ignored, allowing symlink targets to point outside the destination directory, and the modification of some file metadata. You are affected by this vulnerability if using the tarfile module to extract untrusted tar archives using TarFile.extractall() or TarFile.extract() using the filter= parameter with a value of "data" or "tar". See the tarfile extraction filters documentation https://docs.python.org/3/library/tarfile.html#tarfile-extraction-filter  for more information. Note that for Python 3.14 or later the default value of filter= changed from "no filtering" to `"data", so if you are relying on this new default behavior then your usage is also affected. Note that none of these vulnerabilities significantly affect the installation of source distributions which are tar archives as source distributions already allow arbitrary code execution during the build process. However when evaluating source distributions it's important to avoid installing source distributions with suspicious links.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-4435 When using a TarFile.errorlevel = 0 and extracting with a filter the documented behavior is that any filtered members would be skipped and not extracted. However the actual behavior of TarFile.errorlevel = 0 in affected versions is that the member would still be extracted and not skipped.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-4517 Allows arbitrary filesystem writes outside the extraction directory during extraction with filter="data". You are affected by this vulnerability if using the tarfile module to extract untrusted tar archives using TarFile.extractall() or TarFile.extract() using the filter= parameter with a value of "data" or "tar". See the tarfile extraction filters documentation https://docs.python.org/3/library/tarfile.html#tarfile-extraction-filter  for more information. Note that for Python 3.14 or later the default value of filter= changed from "no filtering" to `"data", so if you are relying on this new default behavior then your usage is also affected. Note that none of these vulnerabilities significantly affect the installation of source distributions which are tar archives as source distributions already allow arbitrary code execution during the build process. However when evaluating source distributions it's important to avoid installing source distributions with suspicious links.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-5187 A vulnerability exists in the NodeRestriction admission controller in Kubernetes clusters where node users can delete their corresponding node object by patching themselves with an OwnerReference to a cluster-scoped resource. If the OwnerReference resource does not exist or is subsequently deleted, the given node object will be deleted via garbage collection.

dwx

CVE-2025-6021 A flaw was found in libxml2's xmlBuildQName function, where integer overflows in buffer size calculations can lead to a stack-based buffer overflow. This issue can result in memory corruption or a denial of service when processing crafted input.

nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-6052 A flaw was found in how GLib’s GString manages memory when adding data to strings. If a string is already very large, combining it with more input can cause a hidden overflow in the size calculation. This makes the system think it has enough memory when it doesn’t. As a result, data may be written past the end of the allocated memory, leading to crashes or memory corruption.

nemotron_nano_12b_v2_vl_v150
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-6170 A flaw was found in the interactive shell of the xmllint command-line tool, used for parsing XML files. When a user inputs an overly long command, the program does not check the input size properly, which can cause it to crash. This issue might allow attackers to run harmful code in rare configurations without modern protections.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-6176 Scrapy versions up to 2.13.2 are vulnerable to a denial of service (DoS) attack due to a flaw in its brotli decompression implementation. The protection mechanism against decompression bombs fails to mitigate the brotli variant, allowing remote servers to crash clients with less than 80GB of available memory. This occurs because brotli can achieve extremely high compression ratios for zero-filled data, leading to excessive memory consumption during decompression.

nim-baidu-paddleocr-v1.5.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-whisper-large-v3-v1.3.0

CVE-2025-6965 There exists a vulnerability in SQLite versions before 3.50.2 where the number of aggregate terms could exceed the number of columns available. This could lead to a memory corruption issue. We recommend upgrading to version 3.50.2 or above.

cml-addon-hadoop-cli-7.3.1.200-90
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-6966 NULL pointer dereference in TagSection.keys() in python-apt on APT-based Linux systems allows a local attacker to cause a denial of service (process crash) via a crafted deb822 file with a malformed non-UTF-8 key.

nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-7425 A flaw was found in libxslt where the attribute type, atype, flags are modified in a way that corrupts internal memory management. When XSLT functions, such as the key() process, result in tree fragments, this corruption prevents the proper cleanup of ID attributes. As a result, the system may access freed memory, causing crashes or enabling attackers to trigger heap corruption.

nim-baidu-paddleocr-v1.5.0
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
runtimedataviz

CVE-2025-8176 A vulnerability was found in LibTIFF up to 4.7.0. It has been declared as critical. This vulnerability affects the function get_histogram of the file tools/tiffmedian.c. The manipulation leads to use after free. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used. The patch is identified as fe10872e53efba9cc36c66ac4ab3b41a839d5172. It is recommended to apply a patch to fix this issue.

dex_ingress-nginx-controller

CVE-2025-8177 A vulnerability was found in LibTIFF up to 4.7.0. It has been rated as critical. This issue affects the function setrow of the file tools/thumbnail.c. The manipulation leads to buffer overflow. An attack has to be approached locally. The patch is named e8c9d6c616b19438695fd829e58ae4fde5bfbc22. It is recommended to apply a patch to fix this issue. This vulnerability only affects products that are no longer supported by the maintainer.

dex_ingress-nginx-controller

CVE-2025-8556 A flaw was found in CIRCL's implementation of the FourQ elliptic curve. This vulnerability allows an attacker to compromise session security via low-order point injection and incorrect point validation during Diffie-Hellman key exchange.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-8732 A vulnerability was found in libxml2 up to 2.14.5. It has been declared as problematic. This vulnerability affects the function xmlParseSGMLCatalog of the component xmlcatalog. The manipulation leads to uncontrolled recursion. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The code maintainer explains, that "[t]he issue can only be triggered with untrusted SGML catalogs and it makes absolutely no sense to use untrusted catalogs. I also doubt that anyone is still using SGML catalogs at all."

nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-9714 Uncontrolled recursion in XPath evaluation in libxml2 up to and including version 2.9.14 allows a local attacker to cause a stack overflow via crafted expressions. XPath processing functions `xmlXPathRunEval`, `xmlXPathCtxtCompile`, and `xmlXPathEvalExpr` were resetting recursion depth to zero before making potentially recursive calls. When such functions were called recursively this could allow for uncontrolled recursion and lead to a stack overflow. These functions now preserve recursion depth across recursive calls, allowing recursion depth to be controlled.

nim-baidu-paddleocr-v1.5.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-10263 Arm C1-Ultra, C1-Premium, Neoverse V3 & V3AE, Neoverse V2, Neoverse V1, Neoverse-N2, Neoverse-N1, Cortex-X925, Cortex-X4, Cortex-X3, Cortex-X2, Cortex-X1 & X1C, Cortex-A710, Cortex-A78, A78AE & A78C, Cortex-A77, Cortex-A76 & A76A may allow writes to resources owned by a higher exception level.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2025-10911 A use-after-free vulnerability was found in libxslt while parsing xsl nodes that may lead to the dereference of expired pointers and application crash.

runtimedataviz

CVE-2025-11065 A flaw was found in github.com/go-viper/mapstructure/v2, in the field processing component using mapstructure.WeakDecode. This vulnerability allows information disclosure through detailed error messages that may leak sensitive input values via malformed user-supplied data processed in security-critical contexts.

install-cni

CVE-2025-13601 A heap-based buffer overflow problem was found in glib through an incorrect calculation of buffer size in the g_escape_uri_string() function. If the string to escape contains a very large number of unacceptable characters (which would need escaping), the calculation of the length of the escaped string could overflow, leading to a potential write off the end of the newly allocated string.

nemotron_nano_12b_v2_vl_v150
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-14009 A critical vulnerability exists in the NLTK downloader component of nltk/nltk, affecting all versions. The _unzip_iter function in nltk/downloader.py uses zipfile.extractall() without performing path validation or security checks. This allows attackers to craft malicious zip packages that, when downloaded and extracted by NLTK, can execute arbitrary code. The vulnerability arises because NLTK assumes all downloaded packages are trusted and extracts them without validation. If a malicious package contains Python files, such as __init__.py, these files are executed automatically upon import, leading to remote code execution. This issue can result in full system compromise, including file system access, network access, and potential persistence mechanisms.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-14087 A flaw was found in GLib (Gnome Lib). This vulnerability allows a remote attacker to cause heap corruption, leading to a denial of service or potential code execution via a buffer-underflow in the GVariant parser when processing maliciously crafted input strings.

nemotron_nano_12b_v2_vl_v150
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-14512 A flaw was found in glib. This vulnerability allows a heap buffer overflow and denial-of-service (DoS) via an integer overflow in GLib's GIO (GLib Input/Output) escape_byte_string() function when processing malicious file or remote filesystem attribute values.

nemotron_nano_12b_v2_vl_v150
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-14821 A flaw was found in libssh. This vulnerability allows local man-in-the-middle attacks, security downgrades of SSH (Secure Shell) connections, and manipulation of trusted host information, posing a significant risk to the confidentiality, integrity, and availability of SSH communications via an insecure default configuration on Windows systems where the library automatically loads configuration files from the C:\etc directory, which can be created and modified by unprivileged local users.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2025-15661 libssh2 through 1.11.1, fixed in commit 2dae302, contains an out-of-bounds heap read vulnerability in the sftp_symlink() function in src/sftp.c that allows a malicious SSH server or man-in-the-middle attacker to disclose heap memory contents or cause a crash by sending a crafted SSH_FXP_NAME response. Attackers can supply a link_len value larger than the actual packet data in SSH_FXP_NAME responses for SFTP READLINK and REALPATH operations, triggering a heap buffer over-read of up to target_len minus one bytes due to the missing validation of available packet buffer size before the memcpy operation.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2025-21631 In the Linux kernel, the following vulnerability has been resolved: block, bfq: fix waker_bfqq UAF after bfq_split_bfqq() Our syzkaller report a following UAF for v6.6: BUG: KASAN: slab-use-after-free in bfq_init_rq+0x175d/0x17a0 block/bfq-iosched.c:6958 Read of size 8 at addr ffff8881b57147d8 by task fsstress/232726 CPU: 2 PID: 232726 Comm: fsstress Not tainted 6.6.0-g3629d1885222 #39 Call Trace: <TASK> __dump_stack lib/dump_stack.c:88 [inline] dump_stack_lvl+0x91/0xf0 lib/dump_stack.c:106 print_address_description.constprop.0+0x66/0x300 mm/kasan/report.c:364 print_report+0x3e/0x70 mm/kasan/report.c:475 kasan_report+0xb8/0xf0 mm/kasan/report.c:588 hlist_add_head include/linux/list.h:1023 [inline] bfq_init_rq+0x175d/0x17a0 block/bfq-iosched.c:6958 bfq_insert_request.isra.0+0xe8/0xa20 block/bfq-iosched.c:6271 bfq_insert_requests+0x27f/0x390 block/bfq-iosched.c:6323 blk_mq_insert_request+0x290/0x8f0 block/blk-mq.c:2660 blk_mq_submit_bio+0x1021/0x15e0 block/blk-mq.c:3143 __submit_bio+0xa0/0x6b0 block/blk-core.c:639 __submit_bio_noacct_mq block/blk-core.c:718 [inline] submit_bio_noacct_nocheck+0x5b7/0x810 block/blk-core.c:747 submit_bio_noacct+0xca0/0x1990 block/blk-core.c:847 __ext4_read_bh fs/ext4/super.c:205 [inline] ext4_read_bh+0x15e/0x2e0 fs/ext4/super.c:230 __read_extent_tree_block+0x304/0x6f0 fs/ext4/extents.c:567 ext4_find_extent+0x479/0xd20 fs/ext4/extents.c:947 ext4_ext_map_blocks+0x1a3/0x2680 fs/ext4/extents.c:4182 ext4_map_blocks+0x929/0x15a0 fs/ext4/inode.c:660 ext4_iomap_begin_report+0x298/0x480 fs/ext4/inode.c:3569 iomap_iter+0x3dd/0x1010 fs/iomap/iter.c:91 iomap_fiemap+0x1f4/0x360 fs/iomap/fiemap.c:80 ext4_fiemap+0x181/0x210 fs/ext4/extents.c:5051 ioctl_fiemap.isra.0+0x1b4/0x290 fs/ioctl.c:220 do_vfs_ioctl+0x31c/0x11a0 fs/ioctl.c:811 __do_sys_ioctl fs/ioctl.c:869 [inline] __se_sys_ioctl+0xae/0x190 fs/ioctl.c:857 do_syscall_x64 arch/x86/entry/common.c:51 [inline] do_syscall_64+0x70/0x120 arch/x86/entry/common.c:81 entry_SYSCALL_64_after_hwframe+0x78/0xe2 Allocated by task 232719: kasan_save_stack+0x22/0x50 mm/kasan/common.c:45 kasan_set_track+0x25/0x30 mm/kasan/common.c:52 __kasan_slab_alloc+0x87/0x90 mm/kasan/common.c:328 kasan_slab_alloc include/linux/kasan.h:188 [inline] slab_post_alloc_hook mm/slab.h:768 [inline] slab_alloc_node mm/slub.c:3492 [inline] kmem_cache_alloc_node+0x1b8/0x6f0 mm/slub.c:3537 bfq_get_queue+0x215/0x1f00 block/bfq-iosched.c:5869 bfq_get_bfqq_handle_split+0x167/0x5f0 block/bfq-iosched.c:6776 bfq_init_rq+0x13a4/0x17a0 block/bfq-iosched.c:6938 bfq_insert_request.isra.0+0xe8/0xa20 block/bfq-iosched.c:6271 bfq_insert_requests+0x27f/0x390 block/bfq-iosched.c:6323 blk_mq_insert_request+0x290/0x8f0 block/blk-mq.c:2660 blk_mq_submit_bio+0x1021/0x15e0 block/blk-mq.c:3143 __submit_bio+0xa0/0x6b0 block/blk-core.c:639 __submit_bio_noacct_mq block/blk-core.c:718 [inline] submit_bio_noacct_nocheck+0x5b7/0x810 block/blk-core.c:747 submit_bio_noacct+0xca0/0x1990 block/blk-core.c:847 __ext4_read_bh fs/ext4/super.c:205 [inline] ext4_read_bh_nowait+0x15a/0x240 fs/ext4/super.c:217 ext4_read_bh_lock+0xac/0xd0 fs/ext4/super.c:242 ext4_bread_batch+0x268/0x500 fs/ext4/inode.c:958 __ext4_find_entry+0x448/0x10f0 fs/ext4/namei.c:1671 ext4_lookup_entry fs/ext4/namei.c:1774 [inline] ext4_lookup.part.0+0x359/0x6f0 fs/ext4/namei.c:1842 ext4_lookup+0x72/0x90 fs/ext4/namei.c:1839 __lookup_slow+0x257/0x480 fs/namei.c:1696 lookup_slow fs/namei.c:1713 [inline] walk_component+0x454/0x5c0 fs/namei.c:2004 link_path_walk.part.0+0x773/0xda0 fs/namei.c:2331 link_path_walk fs/namei.c:3826 [inline] path_openat+0x1b9/0x520 fs/namei.c:3826 do_filp_open+0x1b7/0x400 fs/namei.c:3857 do_sys_openat2+0x5dc/0x6e0 fs/open.c:1428 do_sys_open fs/open.c:1443 [inline] __do_sys_openat fs/open.c:1459 [inline] __se_sys_openat fs/open.c:1454 [inline] __x64_sys_openat+0x148/0x200 fs/open.c:1454 do_syscall_x64 arch/x86/entry/common.c:51 [inline] do_syscall_6 ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21632 In the Linux kernel, the following vulnerability has been resolved: x86/fpu: Ensure shadow stack is active before "getting" registers The x86 shadow stack support has its own set of registers. Those registers are XSAVE-managed, but they are "supervisor state components" which means that userspace can not touch them with XSAVE/XRSTOR. It also means that they are not accessible from the existing ptrace ABI for XSAVE state. Thus, there is a new ptrace get/set interface for it. The regset code that ptrace uses provides an ->active() handler in addition to the get/set ones. For shadow stack this ->active() handler verifies that shadow stack is enabled via the ARCH_SHSTK_SHSTK bit in the thread struct. The ->active() handler is checked from some call sites of the regset get/set handlers, but not the ptrace ones. This was not understood when shadow stack support was put in place. As a result, both the set/get handlers can be called with XFEATURE_CET_USER in its init state, which would cause get_xsave_addr() to return NULL and trigger a WARN_ON(). The ssp_set() handler luckily has an ssp_active() check to avoid surprising the kernel with shadow stack behavior when the kernel is not ready for it (ARCH_SHSTK_SHSTK==0). That check just happened to avoid the warning. But the ->get() side wasn't so lucky. It can be called with shadow stacks disabled, triggering the warning in practice, as reported by Christina Schimpe: WARNING: CPU: 5 PID: 1773 at arch/x86/kernel/fpu/regset.c:198 ssp_get+0x89/0xa0 [...] Call Trace: <TASK> ? show_regs+0x6e/0x80 ? ssp_get+0x89/0xa0 ? __warn+0x91/0x150 ? ssp_get+0x89/0xa0 ? report_bug+0x19d/0x1b0 ? handle_bug+0x46/0x80 ? exc_invalid_op+0x1d/0x80 ? asm_exc_invalid_op+0x1f/0x30 ? __pfx_ssp_get+0x10/0x10 ? ssp_get+0x89/0xa0 ? ssp_get+0x52/0xa0 __regset_get+0xad/0xf0 copy_regset_to_user+0x52/0xc0 ptrace_regset+0x119/0x140 ptrace_request+0x13c/0x850 ? wait_task_inactive+0x142/0x1d0 ? do_syscall_64+0x6d/0x90 arch_ptrace+0x102/0x300 [...] Ensure that shadow stacks are active in a thread before looking them up in the XSAVE buffer. Since ARCH_SHSTK_SHSTK and user_ssp[SHSTK_EN] are set at the same time, the active check ensures that there will be something to find in the XSAVE buffer. [ dhansen: changelog/subject tweaks ]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21642 In the Linux kernel, the following vulnerability has been resolved: mptcp: sysctl: sched: avoid using current->nsproxy Using the 'net' structure via 'current' is not recommended for different reasons. First, if the goal is to use it to read or write per-netns data, this is inconsistent with how the "generic" sysctl entries are doing: directly by only using pointers set to the table entry, e.g. table->data. Linked to that, the per-netns data should always be obtained from the table linked to the netns it had been created for, which may not coincide with the reader's or writer's netns. Another reason is that access to current->nsproxy->netns can oops if attempted when current->nsproxy had been dropped when the current task is exiting. This is what syzbot found, when using acct(2): Oops: general protection fault, probably for non-canonical address 0xdffffc0000000005: 0000 [#1] PREEMPT SMP KASAN PTI KASAN: null-ptr-deref in range [0x0000000000000028-0x000000000000002f] CPU: 1 UID: 0 PID: 5924 Comm: syz-executor Not tainted 6.13.0-rc5-syzkaller-00004-gccb98ccef0e5 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 RIP: 0010:proc_scheduler+0xc6/0x3c0 net/mptcp/ctrl.c:125 Code: 03 42 80 3c 38 00 0f 85 fe 02 00 00 4d 8b a4 24 08 09 00 00 48 b8 00 00 00 00 00 fc ff df 49 8d 7c 24 28 48 89 fa 48 c1 ea 03 <80> 3c 02 00 0f 85 cc 02 00 00 4d 8b 7c 24 28 48 8d 84 24 c8 00 00 RSP: 0018:ffffc900034774e8 EFLAGS: 00010206 RAX: dffffc0000000000 RBX: 1ffff9200068ee9e RCX: ffffc90003477620 RDX: 0000000000000005 RSI: ffffffff8b08f91e RDI: 0000000000000028 RBP: 0000000000000001 R08: ffffc90003477710 R09: 0000000000000040 R10: 0000000000000040 R11: 00000000726f7475 R12: 0000000000000000 R13: ffffc90003477620 R14: ffffc90003477710 R15: dffffc0000000000 FS: 0000000000000000(0000) GS:ffff8880b8700000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007fee3cd452d8 CR3: 000000007d116000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> proc_sys_call_handler+0x403/0x5d0 fs/proc/proc_sysctl.c:601 __kernel_write_iter+0x318/0xa80 fs/read_write.c:612 __kernel_write+0xf6/0x140 fs/read_write.c:632 do_acct_process+0xcb0/0x14a0 kernel/acct.c:539 acct_pin_kill+0x2d/0x100 kernel/acct.c:192 pin_kill+0x194/0x7c0 fs/fs_pin.c:44 mnt_pin_kill+0x61/0x1e0 fs/fs_pin.c:81 cleanup_mnt+0x3ac/0x450 fs/namespace.c:1366 task_work_run+0x14e/0x250 kernel/task_work.c:239 exit_task_work include/linux/task_work.h:43 [inline] do_exit+0xad8/0x2d70 kernel/exit.c:938 do_group_exit+0xd3/0x2a0 kernel/exit.c:1087 get_signal+0x2576/0x2610 kernel/signal.c:3017 arch_do_signal_or_restart+0x90/0x7e0 arch/x86/kernel/signal.c:337 exit_to_user_mode_loop kernel/entry/common.c:111 [inline] exit_to_user_mode_prepare include/linux/entry-common.h:329 [inline] __syscall_exit_to_user_mode_work kernel/entry/common.c:207 [inline] syscall_exit_to_user_mode+0x150/0x2a0 kernel/entry/common.c:218 do_syscall_64+0xda/0x250 arch/x86/entry/common.c:89 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7fee3cb87a6a Code: Unable to access opcode bytes at 0x7fee3cb87a40. RSP: 002b:00007fffcccac688 EFLAGS: 00000202 ORIG_RAX: 0000000000000037 RAX: 0000000000000000 RBX: 00007fffcccac710 RCX: 00007fee3cb87a6a RDX: 0000000000000041 RSI: 0000000000000000 RDI: 0000000000000003 RBP: 0000000000000003 R08: 00007fffcccac6ac R09: 00007fffcccacac7 R10: 00007fffcccac710 R11: 0000000000000202 R12: 00007fee3cd49500 R13: 00007fffcccac6ac R14: 0000000000000000 R15: 00007fee3cd4b000 </TASK> Modules linked in: ---[ end trace 0000000000000000 ]--- RIP: 0010:proc_scheduler+0xc6/0x3c0 net/mptcp/ctrl.c:125 Code: 03 42 80 3c 38 00 0f 85 fe 02 00 00 4d 8b a4 24 08 09 00 00 48 b8 00 00 00 00 00 fc ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21643 In the Linux kernel, the following vulnerability has been resolved: netfs: Fix kernel async DIO Netfslib needs to be able to handle kernel-initiated asynchronous DIO that is supplied with a bio_vec[] array. Currently, because of the async flag, this gets passed to netfs_extract_user_iter() which throws a warning and fails because it only handles IOVEC and UBUF iterators. This can be triggered through a combination of cifs and a loopback blockdev with something like: mount //my/cifs/share /foo dd if=/dev/zero of=/foo/m0 bs=4K count=1K losetup --sector-size 4096 --direct-io=on /dev/loop2046 /foo/m0 echo hello >/dev/loop2046 This causes the following to appear in syslog: WARNING: CPU: 2 PID: 109 at fs/netfs/iterator.c:50 netfs_extract_user_iter+0x170/0x250 [netfs] and the write to fail. Fix this by removing the check in netfs_unbuffered_write_iter_locked() that causes async kernel DIO writes to be handled as userspace writes. Note that this change relies on the kernel caller maintaining the existence of the bio_vec array (or kvec[] or folio_queue) until the op is complete.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21646 In the Linux kernel, the following vulnerability has been resolved: afs: Fix the maximum cell name length The kafs filesystem limits the maximum length of a cell to 256 bytes, but a problem occurs if someone actually does that: kafs tries to create a directory under /proc/net/afs/ with the name of the cell, but that fails with a warning: WARNING: CPU: 0 PID: 9 at fs/proc/generic.c:405 because procfs limits the maximum filename length to 255. However, the DNS limits the maximum lookup length and, by extension, the maximum cell name, to 255 less two (length count and trailing NUL). Fix this by limiting the maximum acceptable cellname length to 253. This also allows us to be sure we can create the "/afs/.<cell>/" mountpoint too. Further, split the YFS VL record cell name maximum to be the 256 allowed by the protocol and ignore the record retrieved by YFSVL.GetCellName if it exceeds 253.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21647 In the Linux kernel, the following vulnerability has been resolved: sched: sch_cake: add bounds checks to host bulk flow fairness counts Even though we fixed a logic error in the commit cited below, syzbot still managed to trigger an underflow of the per-host bulk flow counters, leading to an out of bounds memory access. To avoid any such logic errors causing out of bounds memory accesses, this commit factors out all accesses to the per-host bulk flow counters to a series of helpers that perform bounds-checking before any increments and decrements. This also has the benefit of improving readability by moving the conditional checks for the flow mode into these helpers, instead of having them spread out throughout the code (which was the cause of the original logic error). As part of this change, the flow quantum calculation is consolidated into a helper function, which means that the dithering applied to the ost load scaling is now applied both in the DRR rotation and when a sparse flow's quantum is first initiated. The only user-visible effect of this is that the maximum packet size that can be sent while a flow stays sparse will now vary with +/- one byte in some cases. This should not make a noticeable difference in practice, and thus it's not worth complicating the code to preserve the old behaviour.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21650 In the Linux kernel, the following vulnerability has been resolved: net: hns3: fixed hclge_fetch_pf_reg accesses bar space out of bounds issue The TQP BAR space is divided into two segments. TQPs 0-1023 and TQPs 1024-1279 are in different BAR space addresses. However, hclge_fetch_pf_reg does not distinguish the tqp space information when reading the tqp space information. When the number of TQPs is greater than 1024, access bar space overwriting occurs. The problem of different segments has been considered during the initialization of tqp.io_base. Therefore, tqp.io_base is directly used when the queue is read in hclge_fetch_pf_reg. The error message: Unable to handle kernel paging request at virtual address ffff800037200000 pc : hclge_fetch_pf_reg+0x138/0x250 [hclge] lr : hclge_get_regs+0x84/0x1d0 [hclge] Call trace: hclge_fetch_pf_reg+0x138/0x250 [hclge] hclge_get_regs+0x84/0x1d0 [hclge] hns3_get_regs+0x2c/0x50 [hns3] ethtool_get_regs+0xf4/0x270 dev_ethtool+0x674/0x8a0 dev_ioctl+0x270/0x36c sock_do_ioctl+0x110/0x2a0 sock_ioctl+0x2ac/0x530 __arm64_sys_ioctl+0xa8/0x100 invoke_syscall+0x4c/0x124 el0_svc_common.constprop.0+0x140/0x15c do_el0_svc+0x30/0xd0 el0_svc+0x1c/0x2c el0_sync_handler+0xb0/0xb4 el0_sync+0x168/0x180

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21652 In the Linux kernel, the following vulnerability has been resolved: ipvlan: Fix use-after-free in ipvlan_get_iflink(). syzbot presented an use-after-free report [0] regarding ipvlan and linkwatch. ipvlan does not hold a refcnt of the lower device unlike vlan and macvlan. If the linkwatch work is triggered for the ipvlan dev, the lower dev might have already been freed, resulting in UAF of ipvlan->phy_dev in ipvlan_get_iflink(). We can delay the lower dev unregistration like vlan and macvlan by holding the lower dev's refcnt in dev->netdev_ops->ndo_init() and releasing it in dev->priv_destructor(). Jakub pointed out calling .ndo_XXX after unregister_netdevice() has returned is error prone and suggested [1] addressing this UAF in the core by taking commit 750e51603395 ("net: avoid potential UAF in default_operstate()") further. Let's assume unregistering devices DOWN and use RCU protection in default_operstate() not to race with the device unregistration. [0]: BUG: KASAN: slab-use-after-free in ipvlan_get_iflink+0x84/0x88 drivers/net/ipvlan/ipvlan_main.c:353 Read of size 4 at addr ffff0000d768c0e0 by task kworker/u8:35/6944 CPU: 0 UID: 0 PID: 6944 Comm: kworker/u8:35 Not tainted 6.13.0-rc2-g9bc5c9515b48 #12 4c3cb9e8b4565456f6a355f312ff91f4f29b3c47 Hardware name: linux,dummy-virt (DT) Workqueue: events_unbound linkwatch_event Call trace: show_stack+0x38/0x50 arch/arm64/kernel/stacktrace.c:484 (C) __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0xbc/0x108 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x16c/0x6f0 mm/kasan/report.c:489 kasan_report+0xc0/0x120 mm/kasan/report.c:602 __asan_report_load4_noabort+0x20/0x30 mm/kasan/report_generic.c:380 ipvlan_get_iflink+0x84/0x88 drivers/net/ipvlan/ipvlan_main.c:353 dev_get_iflink+0x7c/0xd8 net/core/dev.c:674 default_operstate net/core/link_watch.c:45 [inline] rfc2863_policy+0x144/0x360 net/core/link_watch.c:72 linkwatch_do_dev+0x60/0x228 net/core/link_watch.c:175 __linkwatch_run_queue+0x2f4/0x5b8 net/core/link_watch.c:239 linkwatch_event+0x64/0xa8 net/core/link_watch.c:282 process_one_work+0x700/0x1398 kernel/workqueue.c:3229 process_scheduled_works kernel/workqueue.c:3310 [inline] worker_thread+0x8c4/0xe10 kernel/workqueue.c:3391 kthread+0x2b0/0x360 kernel/kthread.c:389 ret_from_fork+0x10/0x20 arch/arm64/kernel/entry.S:862 Allocated by task 9303: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x30/0x68 mm/kasan/common.c:68 kasan_save_alloc_info+0x44/0x58 mm/kasan/generic.c:568 poison_kmalloc_redzone mm/kasan/common.c:377 [inline] __kasan_kmalloc+0x84/0xa0 mm/kasan/common.c:394 kasan_kmalloc include/linux/kasan.h:260 [inline] __do_kmalloc_node mm/slub.c:4283 [inline] __kmalloc_node_noprof+0x2a0/0x560 mm/slub.c:4289 __kvmalloc_node_noprof+0x9c/0x230 mm/util.c:650 alloc_netdev_mqs+0xb4/0x1118 net/core/dev.c:11209 rtnl_create_link+0x2b8/0xb60 net/core/rtnetlink.c:3595 rtnl_newlink_create+0x19c/0x868 net/core/rtnetlink.c:3771 __rtnl_newlink net/core/rtnetlink.c:3896 [inline] rtnl_newlink+0x122c/0x15c0 net/core/rtnetlink.c:4011 rtnetlink_rcv_msg+0x61c/0x918 net/core/rtnetlink.c:6901 netlink_rcv_skb+0x1dc/0x398 net/netlink/af_netlink.c:2542 rtnetlink_rcv+0x34/0x50 net/core/rtnetlink.c:6928 netlink_unicast_kernel net/netlink/af_netlink.c:1321 [inline] netlink_unicast+0x618/0x838 net/netlink/af_netlink.c:1347 netlink_sendmsg+0x5fc/0x8b0 net/netlink/af_netlink.c:1891 sock_sendmsg_nosec net/socket.c:711 [inline] __sock_sendmsg net/socket.c:726 [inline] __sys_sendto+0x2ec/0x438 net/socket.c:2197 __do_sys_sendto net/socket.c:2204 [inline] __se_sys_sendto net/socket.c:2200 [inline] __arm64_sys_sendto+0xe4/0x110 net/socket.c:2200 __invoke_syscall arch/arm64/kernel/syscall.c:35 [inline] invoke_syscall+0x90/0x278 arch/arm64/kernel/syscall.c:49 el0_svc_common+0x13c/0x250 arch/arm64/kernel/syscall.c:132 do_el0_svc+0x54/0x70 arch/arm64/kernel/syscall.c:151 el ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21654 In the Linux kernel, the following vulnerability has been resolved: ovl: support encoding fid from inode with no alias Dmitry Safonov reported that a WARN_ON() assertion can be trigered by userspace when calling inotify_show_fdinfo() for an overlayfs watched inode, whose dentry aliases were discarded with drop_caches. The WARN_ON() assertion in inotify_show_fdinfo() was removed, because it is possible for encoding file handle to fail for other reason, but the impact of failing to encode an overlayfs file handle goes beyond this assertion. As shown in the LTP test case mentioned in the link below, failure to encode an overlayfs file handle from a non-aliased inode also leads to failure to report an fid with FAN_DELETE_SELF fanotify events. As Dmitry notes in his analyzis of the problem, ovl_encode_fh() fails if it cannot find an alias for the inode, but this failure can be fixed. ovl_encode_fh() seldom uses the alias and in the case of non-decodable file handles, as is often the case with fanotify fid info, ovl_encode_fh() never needs to use the alias to encode a file handle. Defer finding an alias until it is actually needed so ovl_encode_fh() will not fail in the common case of FAN_DELETE_SELF fanotify events.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21655 In the Linux kernel, the following vulnerability has been resolved: io_uring/eventfd: ensure io_eventfd_signal() defers another RCU period io_eventfd_do_signal() is invoked from an RCU callback, but when dropping the reference to the io_ev_fd, it calls io_eventfd_free() directly if the refcount drops to zero. This isn't correct, as any potential freeing of the io_ev_fd should be deferred another RCU grace period. Just call io_eventfd_put() rather than open-code the dec-and-test and free, which will correctly defer it another RCU grace period.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21659 In the Linux kernel, the following vulnerability has been resolved: netdev: prevent accessing NAPI instances from another namespace The NAPI IDs were not fully exposed to user space prior to the netlink API, so they were never namespaced. The netlink API must ensure that at the very least NAPI instance belongs to the same netns as the owner of the genl sock. napi_by_id() can become static now, but it needs to move because of dev_get_by_napi_id().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21660 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix unexpectedly changed path in ksmbd_vfs_kern_path_locked When `ksmbd_vfs_kern_path_locked` met an error and it is not the last entry, it will exit without restoring changed path buffer. But later this buffer may be used as the filename for creation.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21662 In the Linux kernel, the following vulnerability has been resolved: net/mlx5: Fix variable not being completed when function returns When cmd_alloc_index(), fails cmd_work_handler() needs to complete ent->slotted before returning early. Otherwise the task which issued the command may hang: mlx5_core 0000:01:00.0: cmd_work_handler:877:(pid 3880418): failed to allocate command entry INFO: task kworker/13:2:4055883 blocked for more than 120 seconds. Not tainted 4.19.90-25.44.v2101.ky10.aarch64 #1 "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. kworker/13:2 D 0 4055883 2 0x00000228 Workqueue: events mlx5e_tx_dim_work [mlx5_core] Call trace: __switch_to+0xe8/0x150 __schedule+0x2a8/0x9b8 schedule+0x2c/0x88 schedule_timeout+0x204/0x478 wait_for_common+0x154/0x250 wait_for_completion+0x28/0x38 cmd_exec+0x7a0/0xa00 [mlx5_core] mlx5_cmd_exec+0x54/0x80 [mlx5_core] mlx5_core_modify_cq+0x6c/0x80 [mlx5_core] mlx5_core_modify_cq_moderation+0xa0/0xb8 [mlx5_core] mlx5e_tx_dim_work+0x54/0x68 [mlx5_core] process_one_work+0x1b0/0x448 worker_thread+0x54/0x468 kthread+0x134/0x138 ret_from_fork+0x10/0x18

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21663 In the Linux kernel, the following vulnerability has been resolved: net: stmmac: dwmac-tegra: Read iommu stream id from device tree Nvidia's Tegra MGBE controllers require the IOMMU "Stream ID" (SID) to be written to the MGBE_WRAP_AXI_ASID0_CTRL register. The current driver is hard coded to use MGBE0's SID for all controllers. This causes softirq time outs and kernel panics when using controllers other than MGBE0. Example dmesg errors when an ethernet cable is connected to MGBE1: [ 116.133290] tegra-mgbe 6910000.ethernet eth1: Link is Up - 1Gbps/Full - flow control rx/tx [ 121.851283] tegra-mgbe 6910000.ethernet eth1: NETDEV WATCHDOG: CPU: 5: transmit queue 0 timed out 5690 ms [ 121.851782] tegra-mgbe 6910000.ethernet eth1: Reset adapter. [ 121.892464] tegra-mgbe 6910000.ethernet eth1: Register MEM_TYPE_PAGE_POOL RxQ-0 [ 121.905920] tegra-mgbe 6910000.ethernet eth1: PHY [stmmac-1:00] driver [Aquantia AQR113] (irq=171) [ 121.907356] tegra-mgbe 6910000.ethernet eth1: Enabling Safety Features [ 121.907578] tegra-mgbe 6910000.ethernet eth1: IEEE 1588-2008 Advanced Timestamp supported [ 121.908399] tegra-mgbe 6910000.ethernet eth1: registered PTP clock [ 121.908582] tegra-mgbe 6910000.ethernet eth1: configuring for phy/10gbase-r link mode [ 125.961292] tegra-mgbe 6910000.ethernet eth1: Link is Up - 1Gbps/Full - flow control rx/tx [ 181.921198] rcu: INFO: rcu_preempt detected stalls on CPUs/tasks: [ 181.921404] rcu: 7-....: (1 GPs behind) idle=540c/1/0x4000000000000002 softirq=1748/1749 fqs=2337 [ 181.921684] rcu: (detected by 4, t=6002 jiffies, g=1357, q=1254 ncpus=8) [ 181.921878] Sending NMI from CPU 4 to CPUs 7: [ 181.921886] NMI backtrace for cpu 7 [ 181.922131] CPU: 7 UID: 0 PID: 0 Comm: swapper/7 Kdump: loaded Not tainted 6.13.0-rc3+ #6 [ 181.922390] Hardware name: NVIDIA CTI Forge + Orin AGX/Jetson, BIOS 202402.1-Unknown 10/28/2024 [ 181.922658] pstate: 40400009 (nZcv daif +PAN -UAO -TCO -DIT -SSBS BTYPE=--) [ 181.922847] pc : handle_softirqs+0x98/0x368 [ 181.922978] lr : __do_softirq+0x18/0x20 [ 181.923095] sp : ffff80008003bf50 [ 181.923189] x29: ffff80008003bf50 x28: 0000000000000008 x27: 0000000000000000 [ 181.923379] x26: ffffce78ea277000 x25: 0000000000000000 x24: 0000001c61befda0 [ 181.924486] x23: 0000000060400009 x22: ffffce78e99918bc x21: ffff80008018bd70 [ 181.925568] x20: ffffce78e8bb00d8 x19: ffff80008018bc20 x18: 0000000000000000 [ 181.926655] x17: ffff318ebe7d3000 x16: ffff800080038000 x15: 0000000000000000 [ 181.931455] x14: ffff000080816680 x13: ffff318ebe7d3000 x12: 000000003464d91d [ 181.938628] x11: 0000000000000040 x10: ffff000080165a70 x9 : ffffce78e8bb0160 [ 181.945804] x8 : ffff8000827b3160 x7 : f9157b241586f343 x6 : eeb6502a01c81c74 [ 181.953068] x5 : a4acfcdd2e8096bb x4 : ffffce78ea277340 x3 : 00000000ffffd1e1 [ 181.960329] x2 : 0000000000000101 x1 : ffffce78ea277340 x0 : ffff318ebe7d3000 [ 181.967591] Call trace: [ 181.970043] handle_softirqs+0x98/0x368 (P) [ 181.974240] __do_softirq+0x18/0x20 [ 181.977743] ____do_softirq+0x14/0x28 [ 181.981415] call_on_irq_stack+0x24/0x30 [ 181.985180] do_softirq_own_stack+0x20/0x30 [ 181.989379] __irq_exit_rcu+0x114/0x140 [ 181.993142] irq_exit_rcu+0x14/0x28 [ 181.996816] el1_interrupt+0x44/0xb8 [ 182.000316] el1h_64_irq_handler+0x14/0x20 [ 182.004343] el1h_64_irq+0x80/0x88 [ 182.007755] cpuidle_enter_state+0xc4/0x4a8 (P) [ 182.012305] cpuidle_enter+0x3c/0x58 [ 182.015980] cpuidle_idle_call+0x128/0x1c0 [ 182.020005] do_idle+0xe0/0xf0 [ 182.023155] cpu_startup_entry+0x3c/0x48 [ 182.026917] secondary_start_kernel+0xdc/0x120 [ 182.031379] __secondary_switched+0x74/0x78 [ 212.971162] rcu: INFO: rcu_preempt detected expedited stalls on CPUs/tasks: { 7-.... } 6103 jiffies s: 417 root: 0x80/. [ 212.985935] rcu: blocking rcu_node structures (internal RCU debug): [ 212.992758] Sending NMI from CPU 0 to CPUs 7: [ 212.998539] NMI backtrace for cpu 7 [ 213.004304] CPU: 7 UID: 0 PI ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21665 In the Linux kernel, the following vulnerability has been resolved: filemap: avoid truncating 64-bit offset to 32 bits On 32-bit kernels, folio_seek_hole_data() was inadvertently truncating a 64-bit value to 32 bits, leading to a possible infinite loop when writing to an xfs filesystem.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21668 In the Linux kernel, the following vulnerability has been resolved: pmdomain: imx8mp-blk-ctrl: add missing loop break condition Currently imx8mp_blk_ctrl_remove() will continue the for loop until an out-of-bounds exception occurs. pstate: 60000005 (nZCv daif -PAN -UAO -TCO -DIT -SSBS BTYPE=--) pc : dev_pm_domain_detach+0x8/0x48 lr : imx8mp_blk_ctrl_shutdown+0x58/0x90 sp : ffffffc084f8bbf0 x29: ffffffc084f8bbf0 x28: ffffff80daf32ac0 x27: 0000000000000000 x26: ffffffc081658d78 x25: 0000000000000001 x24: ffffffc08201b028 x23: ffffff80d0db9490 x22: ffffffc082340a78 x21: 00000000000005b0 x20: ffffff80d19bc180 x19: 000000000000000a x18: ffffffffffffffff x17: ffffffc080a39e08 x16: ffffffc080a39c98 x15: 4f435f464f006c72 x14: 0000000000000004 x13: ffffff80d0172110 x12: 0000000000000000 x11: ffffff80d0537740 x10: ffffff80d05376c0 x9 : ffffffc0808ed2d8 x8 : ffffffc084f8bab0 x7 : 0000000000000000 x6 : 0000000000000000 x5 : ffffff80d19b9420 x4 : fffffffe03466e60 x3 : 0000000080800077 x2 : 0000000000000000 x1 : 0000000000000001 x0 : 0000000000000000 Call trace: dev_pm_domain_detach+0x8/0x48 platform_shutdown+0x2c/0x48 device_shutdown+0x158/0x268 kernel_restart_prepare+0x40/0x58 kernel_kexec+0x58/0xe8 __do_sys_reboot+0x198/0x258 __arm64_sys_reboot+0x2c/0x40 invoke_syscall+0x5c/0x138 el0_svc_common.constprop.0+0x48/0xf0 do_el0_svc+0x24/0x38 el0_svc+0x38/0xc8 el0t_64_sync_handler+0x120/0x130 el0t_64_sync+0x190/0x198 Code: 8128c2d0 ffffffc0 aa1e03e9 d503201f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21670 In the Linux kernel, the following vulnerability has been resolved: vsock/bpf: return early if transport is not assigned Some of the core functions can only be called if the transport has been assigned. As Michal reported, a socket might have the transport at NULL, for example after a failed connect(), causing the following trace: BUG: kernel NULL pointer dereference, address: 00000000000000a0 #PF: supervisor read access in kernel mode #PF: error_code(0x0000) - not-present page PGD 12faf8067 P4D 12faf8067 PUD 113670067 PMD 0 Oops: Oops: 0000 [#1] PREEMPT SMP NOPTI CPU: 15 UID: 0 PID: 1198 Comm: a.out Not tainted 6.13.0-rc2+ RIP: 0010:vsock_connectible_has_data+0x1f/0x40 Call Trace: vsock_bpf_recvmsg+0xca/0x5e0 sock_recvmsg+0xb9/0xc0 __sys_recvfrom+0xb3/0x130 __x64_sys_recvfrom+0x20/0x30 do_syscall_64+0x93/0x180 entry_SYSCALL_64_after_hwframe+0x76/0x7e So we need to check the `vsk->transport` in vsock_bpf_recvmsg(), especially for connected sockets (stream/seqpacket) as we already do in __vsock_connectible_recvmsg().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21674 In the Linux kernel, the following vulnerability has been resolved: net/mlx5e: Fix inversion dependency warning while enabling IPsec tunnel Attempt to enable IPsec packet offload in tunnel mode in debug kernel generates the following kernel panic, which is happening due to two issues: 1. In SA add section, the should be _bh() variant when marking SA mode. 2. There is not needed flush_workqueue in SA delete routine. It is not needed as at this stage as it is removed from SADB and the running work will be canceled later in SA free. ===================================================== WARNING: SOFTIRQ-safe -> SOFTIRQ-unsafe lock order detected 6.12.0+ #4 Not tainted ----------------------------------------------------- charon/1337 [HC0[0]:SC0[4]:HE1:SE0] is trying to acquire: ffff88810f365020 (&xa->xa_lock#24){+.+.}-{3:3}, at: mlx5e_xfrm_del_state+0xca/0x1e0 [mlx5_core] and this task is already holding: ffff88813e0f0d48 (&x->lock){+.-.}-{3:3}, at: xfrm_state_delete+0x16/0x30 which would create a new lock dependency: (&x->lock){+.-.}-{3:3} -> (&xa->xa_lock#24){+.+.}-{3:3} but this new dependency connects a SOFTIRQ-irq-safe lock: (&x->lock){+.-.}-{3:3} ... which became SOFTIRQ-irq-safe at: lock_acquire+0x1be/0x520 _raw_spin_lock_bh+0x34/0x40 xfrm_timer_handler+0x91/0xd70 __hrtimer_run_queues+0x1dd/0xa60 hrtimer_run_softirq+0x146/0x2e0 handle_softirqs+0x266/0x860 irq_exit_rcu+0x115/0x1a0 sysvec_apic_timer_interrupt+0x6e/0x90 asm_sysvec_apic_timer_interrupt+0x16/0x20 default_idle+0x13/0x20 default_idle_call+0x67/0xa0 do_idle+0x2da/0x320 cpu_startup_entry+0x50/0x60 start_secondary+0x213/0x2a0 common_startup_64+0x129/0x138 to a SOFTIRQ-irq-unsafe lock: (&xa->xa_lock#24){+.+.}-{3:3} ... which became SOFTIRQ-irq-unsafe at: ... lock_acquire+0x1be/0x520 _raw_spin_lock+0x2c/0x40 xa_set_mark+0x70/0x110 mlx5e_xfrm_add_state+0xe48/0x2290 [mlx5_core] xfrm_dev_state_add+0x3bb/0xd70 xfrm_add_sa+0x2451/0x4a90 xfrm_user_rcv_msg+0x493/0x880 netlink_rcv_skb+0x12e/0x380 xfrm_netlink_rcv+0x6d/0x90 netlink_unicast+0x42f/0x740 netlink_sendmsg+0x745/0xbe0 __sock_sendmsg+0xc5/0x190 __sys_sendto+0x1fe/0x2c0 __x64_sys_sendto+0xdc/0x1b0 do_syscall_64+0x6d/0x140 entry_SYSCALL_64_after_hwframe+0x4b/0x53 other info that might help us debug this: Possible interrupt unsafe locking scenario: CPU0 CPU1 ---- ---- lock(&xa->xa_lock#24); local_irq_disable(); lock(&x->lock); lock(&xa->xa_lock#24); <Interrupt> lock(&x->lock); *** DEADLOCK *** 2 locks held by charon/1337: #0: ffffffff87f8f858 (&net->xfrm.xfrm_cfg_mutex){+.+.}-{4:4}, at: xfrm_netlink_rcv+0x5e/0x90 #1: ffff88813e0f0d48 (&x->lock){+.-.}-{3:3}, at: xfrm_state_delete+0x16/0x30 the dependencies between SOFTIRQ-irq-safe lock and the holding lock: -> (&x->lock){+.-.}-{3:3} ops: 29 { HARDIRQ-ON-W at: lock_acquire+0x1be/0x520 _raw_spin_lock_bh+0x34/0x40 xfrm_alloc_spi+0xc0/0xe60 xfrm_alloc_userspi+0x5f6/0xbc0 xfrm_user_rcv_msg+0x493/0x880 netlink_rcv_skb+0x12e/0x380 xfrm_netlink_rcv+0x6d/0x90 netlink_unicast+0x42f/0x740 netlink_sendmsg+0x745/0xbe0 __sock_sendmsg+0xc5/0x190 __sys_sendto+0x1fe/0x2c0 __x64_sys_sendto+0xdc/0x1b0 do_syscall_64+0x6d/0x140 entry_SYSCALL_64_after_hwframe+0x4b/0x53 IN-SOFTIRQ-W at: lock_acquire+0x1be/0x520 _raw_spin_lock_bh+0x34/0x40 xfrm_timer_handler+0x91/0xd70 __hrtimer_run_queues+0x1dd/0xa60 ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21675 In the Linux kernel, the following vulnerability has been resolved: net/mlx5: Clear port select structure when fail to create Clear the port select structure on error so no stale values left after definers are destroyed. That's because the mlx5_lag_destroy_definers() always try to destroy all lag definers in the tt_map, so in the flow below lag definers get double-destroyed and cause kernel crash: mlx5_lag_port_sel_create() mlx5_lag_create_definers() mlx5_lag_create_definer() <- Failed on tt 1 mlx5_lag_destroy_definers() <- definers[tt=0] gets destroyed mlx5_lag_port_sel_create() mlx5_lag_create_definers() mlx5_lag_create_definer() <- Failed on tt 0 mlx5_lag_destroy_definers() <- definers[tt=0] gets double-destroyed Unable to handle kernel NULL pointer dereference at virtual address 0000000000000008 Mem abort info: ESR = 0x0000000096000005 EC = 0x25: DABT (current EL), IL = 32 bits SET = 0, FnV = 0 EA = 0, S1PTW = 0 FSC = 0x05: level 1 translation fault Data abort info: ISV = 0, ISS = 0x00000005, ISS2 = 0x00000000 CM = 0, WnR = 0, TnD = 0, TagAccess = 0 GCS = 0, Overlay = 0, DirtyBit = 0, Xs = 0 user pgtable: 64k pages, 48-bit VAs, pgdp=0000000112ce2e00 [0000000000000008] pgd=0000000000000000, p4d=0000000000000000, pud=0000000000000000 Internal error: Oops: 0000000096000005 [#1] PREEMPT SMP Modules linked in: iptable_raw bonding ip_gre ip6_gre gre ip6_tunnel tunnel6 geneve ip6_udp_tunnel udp_tunnel ipip tunnel4 ip_tunnel rdma_ucm(OE) rdma_cm(OE) iw_cm(OE) ib_ipoib(OE) ib_cm(OE) ib_umad(OE) mlx5_ib(OE) ib_uverbs(OE) mlx5_fwctl(OE) fwctl(OE) mlx5_core(OE) mlxdevm(OE) ib_core(OE) mlxfw(OE) memtrack(OE) mlx_compat(OE) openvswitch nsh nf_conncount psample xt_conntrack xt_MASQUERADE nf_conntrack_netlink nfnetlink xfrm_user xfrm_algo xt_addrtype iptable_filter iptable_nat nf_nat nf_conntrack nf_defrag_ipv6 nf_defrag_ipv4 br_netfilter bridge stp llc netconsole overlay efi_pstore sch_fq_codel zram ip_tables crct10dif_ce qemu_fw_cfg fuse ipv6 crc_ccitt [last unloaded: mlx_compat(OE)] CPU: 3 UID: 0 PID: 217 Comm: kworker/u53:2 Tainted: G OE 6.11.0+ #2 Tainted: [O]=OOT_MODULE, [E]=UNSIGNED_MODULE Hardware name: QEMU KVM Virtual Machine, BIOS 0.0.0 02/06/2015 Workqueue: mlx5_lag mlx5_do_bond_work [mlx5_core] pstate: 60400005 (nZCv daif +PAN -UAO -TCO -DIT -SSBS BTYPE=--) pc : mlx5_del_flow_rules+0x24/0x2c0 [mlx5_core] lr : mlx5_lag_destroy_definer+0x54/0x100 [mlx5_core] sp : ffff800085fafb00 x29: ffff800085fafb00 x28: ffff0000da0c8000 x27: 0000000000000000 x26: ffff0000da0c8000 x25: ffff0000da0c8000 x24: ffff0000da0c8000 x23: ffff0000c31f81a0 x22: 0400000000000000 x21: ffff0000da0c8000 x20: 0000000000000000 x19: 0000000000000001 x18: 0000000000000000 x17: 0000000000000000 x16: 0000000000000000 x15: 0000ffff8b0c9350 x14: 0000000000000000 x13: ffff800081390d18 x12: ffff800081dc3cc0 x11: 0000000000000001 x10: 0000000000000b10 x9 : ffff80007ab7304c x8 : ffff0000d00711f0 x7 : 0000000000000004 x6 : 0000000000000190 x5 : ffff00027edb3010 x4 : 0000000000000000 x3 : 0000000000000000 x2 : ffff0000d39b8000 x1 : ffff0000d39b8000 x0 : 0400000000000000 Call trace: mlx5_del_flow_rules+0x24/0x2c0 [mlx5_core] mlx5_lag_destroy_definer+0x54/0x100 [mlx5_core] mlx5_lag_destroy_definers+0xa0/0x108 [mlx5_core] mlx5_lag_port_sel_create+0x2d4/0x6f8 [mlx5_core] mlx5_activate_lag+0x60c/0x6f8 [mlx5_core] mlx5_do_bond_work+0x284/0x5c8 [mlx5_core] process_one_work+0x170/0x3e0 worker_thread+0x2d8/0x3e0 kthread+0x11c/0x128 ret_from_fork+0x10/0x20 Code: a9025bf5 aa0003f6 a90363f7 f90023f9 (f9400400) ---[ end trace 0000000000000000 ]---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21676 In the Linux kernel, the following vulnerability has been resolved: net: fec: handle page_pool_dev_alloc_pages error The fec_enet_update_cbd function calls page_pool_dev_alloc_pages but did not handle the case when it returned NULL. There was a WARN_ON(!new_page) but it would still proceed to use the NULL pointer and then crash. This case does seem somewhat rare but when the system is under memory pressure it can happen. One case where I can duplicate this with some frequency is when writing over a smbd share to a SATA HDD attached to an imx6q. Setting /proc/sys/vm/min_free_kbytes to higher values also seems to solve the problem for my test case. But it still seems wrong that the fec driver ignores the memory allocation error and can crash. This commit handles the allocation error by dropping the current packet.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21678 In the Linux kernel, the following vulnerability has been resolved: gtp: Destroy device along with udp socket's netns dismantle. gtp_newlink() links the device to a list in dev_net(dev) instead of src_net, where a udp tunnel socket is created. Even when src_net is removed, the device stays alive on dev_net(dev). Then, removing src_net triggers the splat below. [0] In this example, gtp0 is created in ns2, and the udp socket is created in ns1. ip netns add ns1 ip netns add ns2 ip -n ns1 link add netns ns2 name gtp0 type gtp role sgsn ip netns del ns1 Let's link the device to the socket's netns instead. Now, gtp_net_exit_batch_rtnl() needs another netdev iteration to remove all gtp devices in the netns. [0]: ref_tracker: net notrefcnt@000000003d6e7d05 has 1/2 users at sk_alloc (./include/net/net_namespace.h:345 net/core/sock.c:2236) inet_create (net/ipv4/af_inet.c:326 net/ipv4/af_inet.c:252) __sock_create (net/socket.c:1558) udp_sock_create4 (net/ipv4/udp_tunnel_core.c:18) gtp_create_sock (./include/net/udp_tunnel.h:59 drivers/net/gtp.c:1423) gtp_create_sockets (drivers/net/gtp.c:1447) gtp_newlink (drivers/net/gtp.c:1507) rtnl_newlink (net/core/rtnetlink.c:3786 net/core/rtnetlink.c:3897 net/core/rtnetlink.c:4012) rtnetlink_rcv_msg (net/core/rtnetlink.c:6922) netlink_rcv_skb (net/netlink/af_netlink.c:2542) netlink_unicast (net/netlink/af_netlink.c:1321 net/netlink/af_netlink.c:1347) netlink_sendmsg (net/netlink/af_netlink.c:1891) ____sys_sendmsg (net/socket.c:711 net/socket.c:726 net/socket.c:2583) ___sys_sendmsg (net/socket.c:2639) __sys_sendmsg (net/socket.c:2669) do_syscall_64 (arch/x86/entry/common.c:52 arch/x86/entry/common.c:83) WARNING: CPU: 1 PID: 60 at lib/ref_tracker.c:179 ref_tracker_dir_exit (lib/ref_tracker.c:179) Modules linked in: CPU: 1 UID: 0 PID: 60 Comm: kworker/u16:2 Not tainted 6.13.0-rc5-00147-g4c1224501e9d #5 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS rel-1.16.0-0-gd239552ce722-prebuilt.qemu.org 04/01/2014 Workqueue: netns cleanup_net RIP: 0010:ref_tracker_dir_exit (lib/ref_tracker.c:179) Code: 00 00 00 fc ff df 4d 8b 26 49 bd 00 01 00 00 00 00 ad de 4c 39 f5 0f 85 df 00 00 00 48 8b 74 24 08 48 89 df e8 a5 cc 12 02 90 <0f> 0b 90 48 8d 6b 44 be 04 00 00 00 48 89 ef e8 80 de 67 ff 48 89 RSP: 0018:ff11000009a07b60 EFLAGS: 00010286 RAX: 0000000000002bd3 RBX: ff1100000f4e1aa0 RCX: 1ffffffff0e40ac6 RDX: 0000000000000000 RSI: 0000000000000000 RDI: ffffffff8423ee3c RBP: ff1100000f4e1af0 R08: 0000000000000001 R09: fffffbfff0e395ae R10: 0000000000000001 R11: 0000000000036001 R12: ff1100000f4e1af0 R13: dead000000000100 R14: ff1100000f4e1af0 R15: dffffc0000000000 FS: 0000000000000000(0000) GS:ff1100006ce80000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f9b2464bd98 CR3: 0000000005286005 CR4: 0000000000771ef0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe07f0 DR7: 0000000000000400 PKRU: 55555554 Call Trace: <TASK> ? __warn (kernel/panic.c:748) ? ref_tracker_dir_exit (lib/ref_tracker.c:179) ? report_bug (lib/bug.c:201 lib/bug.c:219) ? handle_bug (arch/x86/kernel/traps.c:285) ? exc_invalid_op (arch/x86/kernel/traps.c:309 (discriminator 1)) ? asm_exc_invalid_op (./arch/x86/include/asm/idtentry.h:621) ? _raw_spin_unlock_irqrestore (./arch/x86/include/asm/irqflags.h:42 ./arch/x86/include/asm/irqflags.h:97 ./arch/x86/include/asm/irqflags.h:155 ./include/linux/spinlock_api_smp.h:151 kernel/locking/spinlock.c:194) ? ref_tracker_dir_exit (lib/ref_tracker.c:179) ? __pfx_ref_tracker_dir_exit (lib/ref_tracker.c:158) ? kfree (mm/slub.c:4613 mm/slub.c:4761) net_free (net/core/net_namespace.c:476 net/core/net_namespace.c:467) cleanup_net (net/core/net_namespace.c:664 (discriminator 3)) process_one_work (kernel/workqueue.c:3229) worker_thread (kernel/workqueue.c:3304 kernel/workqueue.c:3391 ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21680 In the Linux kernel, the following vulnerability has been resolved: pktgen: Avoid out-of-bounds access in get_imix_entries Passing a sufficient amount of imix entries leads to invalid access to the pkt_dev->imix_entries array because of the incorrect boundary check. UBSAN: array-index-out-of-bounds in net/core/pktgen.c:874:24 index 20 is out of range for type 'imix_pkt [20]' CPU: 2 PID: 1210 Comm: bash Not tainted 6.10.0-rc1 #121 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996) Call Trace: <TASK> dump_stack_lvl lib/dump_stack.c:117 __ubsan_handle_out_of_bounds lib/ubsan.c:429 get_imix_entries net/core/pktgen.c:874 pktgen_if_write net/core/pktgen.c:1063 pde_write fs/proc/inode.c:334 proc_reg_write fs/proc/inode.c:346 vfs_write fs/read_write.c:593 ksys_write fs/read_write.c:644 do_syscall_64 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe arch/x86/entry/entry_64.S:130 Found by Linux Verification Center (linuxtesting.org) with SVACE. [ fp: allow to fill the array completely; minor changelog cleanup ]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21684 In the Linux kernel, the following vulnerability has been resolved: gpio: xilinx: Convert gpio_lock to raw spinlock irq_chip functions may be called in raw spinlock context. Therefore, we must also use a raw spinlock for our own internal locking. This fixes the following lockdep splat: [ 5.349336] ============================= [ 5.353349] [ BUG: Invalid wait context ] [ 5.357361] 6.13.0-rc5+ #69 Tainted: G W [ 5.363031] ----------------------------- [ 5.367045] kworker/u17:1/44 is trying to lock: [ 5.371587] ffffff88018b02c0 (&chip->gpio_lock){....}-{3:3}, at: xgpio_irq_unmask (drivers/gpio/gpio-xilinx.c:433 (discriminator 8)) [ 5.380079] other info that might help us debug this: [ 5.385138] context-{5:5} [ 5.387762] 5 locks held by kworker/u17:1/44: [ 5.392123] #0: ffffff8800014958 ((wq_completion)events_unbound){+.+.}-{0:0}, at: process_one_work (kernel/workqueue.c:3204) [ 5.402260] #1: ffffffc082fcbdd8 (deferred_probe_work){+.+.}-{0:0}, at: process_one_work (kernel/workqueue.c:3205) [ 5.411528] #2: ffffff880172c900 (&dev->mutex){....}-{4:4}, at: __device_attach (drivers/base/dd.c:1006) [ 5.419929] #3: ffffff88039c8268 (request_class#2){+.+.}-{4:4}, at: __setup_irq (kernel/irq/internals.h:156 kernel/irq/manage.c:1596) [ 5.428331] #4: ffffff88039c80c8 (lock_class#2){....}-{2:2}, at: __setup_irq (kernel/irq/manage.c:1614) [ 5.436472] stack backtrace: [ 5.439359] CPU: 2 UID: 0 PID: 44 Comm: kworker/u17:1 Tainted: G W 6.13.0-rc5+ #69 [ 5.448690] Tainted: [W]=WARN [ 5.451656] Hardware name: xlnx,zynqmp (DT) [ 5.455845] Workqueue: events_unbound deferred_probe_work_func [ 5.461699] Call trace: [ 5.464147] show_stack+0x18/0x24 C [ 5.467821] dump_stack_lvl (lib/dump_stack.c:123) [ 5.471501] dump_stack (lib/dump_stack.c:130) [ 5.474824] __lock_acquire (kernel/locking/lockdep.c:4828 kernel/locking/lockdep.c:4898 kernel/locking/lockdep.c:5176) [ 5.478758] lock_acquire (arch/arm64/include/asm/percpu.h:40 kernel/locking/lockdep.c:467 kernel/locking/lockdep.c:5851 kernel/locking/lockdep.c:5814) [ 5.482429] _raw_spin_lock_irqsave (include/linux/spinlock_api_smp.h:111 kernel/locking/spinlock.c:162) [ 5.486797] xgpio_irq_unmask (drivers/gpio/gpio-xilinx.c:433 (discriminator 8)) [ 5.490737] irq_enable (kernel/irq/internals.h:236 kernel/irq/chip.c:170 kernel/irq/chip.c:439 kernel/irq/chip.c:432 kernel/irq/chip.c:345) [ 5.494060] __irq_startup (kernel/irq/internals.h:241 kernel/irq/chip.c:180 kernel/irq/chip.c:250) [ 5.497645] irq_startup (kernel/irq/chip.c:270) [ 5.501143] __setup_irq (kernel/irq/manage.c:1807) [ 5.504728] request_threaded_irq (kernel/irq/manage.c:2208)

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21687 In the Linux kernel, the following vulnerability has been resolved: vfio/platform: check the bounds of read/write syscalls count and offset are passed from user space and not checked, only offset is capped to 40 bits, which can be used to read/write out of bounds of the device.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2025-21691 In the Linux kernel, the following vulnerability has been resolved: cachestat: fix page cache statistics permission checking When the 'cachestat()' system call was added in commit cf264e1329fb ("cachestat: implement cachestat syscall"), it was meant to be a much more convenient (and performant) version of mincore() that didn't need mapping things into the user virtual address space in order to work. But it ended up missing the "check for writability or ownership" fix for mincore(), done in commit 134fca9063ad ("mm/mincore.c: make mincore() more conservative"). This just adds equivalent logic to 'cachestat()', modified for the file context (rather than vma).

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21697 In the Linux kernel, the following vulnerability has been resolved: drm/v3d: Ensure job pointer is set to NULL after job completion After a job completes, the corresponding pointer in the device must be set to NULL. Failing to do so triggers a warning when unloading the driver, as it appears the job is still active. To prevent this, assign the job pointer to NULL after completing the job, indicating the job has finished.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21705 In the Linux kernel, the following vulnerability has been resolved: mptcp: handle fastopen disconnect correctly Syzbot was able to trigger a data stream corruption: WARNING: CPU: 0 PID: 9846 at net/mptcp/protocol.c:1024 __mptcp_clean_una+0xddb/0xff0 net/mptcp/protocol.c:1024 Modules linked in: CPU: 0 UID: 0 PID: 9846 Comm: syz-executor351 Not tainted 6.13.0-rc2-syzkaller-00059-g00a5acdbf398 #0 Hardware name: Google Compute Engine/Google Compute Engine, BIOS Google 11/25/2024 RIP: 0010:__mptcp_clean_una+0xddb/0xff0 net/mptcp/protocol.c:1024 Code: fa ff ff 48 8b 4c 24 18 80 e1 07 fe c1 38 c1 0f 8c 8e fa ff ff 48 8b 7c 24 18 e8 e0 db 54 f6 e9 7f fa ff ff e8 e6 80 ee f5 90 <0f> 0b 90 4c 8b 6c 24 40 4d 89 f4 e9 04 f5 ff ff 44 89 f1 80 e1 07 RSP: 0018:ffffc9000c0cf400 EFLAGS: 00010293 RAX: ffffffff8bb0dd5a RBX: ffff888033f5d230 RCX: ffff888059ce8000 RDX: 0000000000000000 RSI: 0000000000000000 RDI: 0000000000000000 RBP: ffffc9000c0cf518 R08: ffffffff8bb0d1dd R09: 1ffff110170c8928 R10: dffffc0000000000 R11: ffffed10170c8929 R12: 0000000000000000 R13: ffff888033f5d220 R14: dffffc0000000000 R15: ffff8880592b8000 FS: 00007f6e866496c0(0000) GS:ffff8880b8600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f6e86f491a0 CR3: 00000000310e6000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> __mptcp_clean_una_wakeup+0x7f/0x2d0 net/mptcp/protocol.c:1074 mptcp_release_cb+0x7cb/0xb30 net/mptcp/protocol.c:3493 release_sock+0x1aa/0x1f0 net/core/sock.c:3640 inet_wait_for_connect net/ipv4/af_inet.c:609 [inline] __inet_stream_connect+0x8bd/0xf30 net/ipv4/af_inet.c:703 mptcp_sendmsg_fastopen+0x2a2/0x530 net/mptcp/protocol.c:1755 mptcp_sendmsg+0x1884/0x1b10 net/mptcp/protocol.c:1830 sock_sendmsg_nosec net/socket.c:711 [inline] __sock_sendmsg+0x1a6/0x270 net/socket.c:726 ____sys_sendmsg+0x52a/0x7e0 net/socket.c:2583 ___sys_sendmsg net/socket.c:2637 [inline] __sys_sendmsg+0x269/0x350 net/socket.c:2669 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7f6e86ebfe69 Code: 28 00 00 00 75 05 48 83 c4 28 c3 e8 b1 1f 00 00 90 48 89 f8 48 89 f7 48 89 d6 48 89 ca 4d 89 c2 4d 89 c8 4c 8b 4c 24 08 0f 05 <48> 3d 01 f0 ff ff 73 01 c3 48 c7 c1 b0 ff ff ff f7 d8 64 89 01 48 RSP: 002b:00007f6e86649168 EFLAGS: 00000246 ORIG_RAX: 000000000000002e RAX: ffffffffffffffda RBX: 00007f6e86f491b8 RCX: 00007f6e86ebfe69 RDX: 0000000030004001 RSI: 0000000020000080 RDI: 0000000000000003 RBP: 00007f6e86f491b0 R08: 00007f6e866496c0 R09: 0000000000000000 R10: 0000000000000000 R11: 0000000000000246 R12: 00007f6e86f491bc R13: 000000000000006e R14: 00007ffe445d9420 R15: 00007ffe445d9508 </TASK> The root cause is the bad handling of disconnect() generated internally by the MPTCP protocol in case of connect FASTOPEN errors. Address the issue increasing the socket disconnect counter even on such a case, to allow other threads waiting on the same socket lock to properly error out.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21706 In the Linux kernel, the following vulnerability has been resolved: mptcp: pm: only set fullmesh for subflow endp With the in-kernel path-manager, it is possible to change the 'fullmesh' flag. The code in mptcp_pm_nl_fullmesh() expects to change it only on 'subflow' endpoints, to recreate more or less subflows using the linked address. Unfortunately, the set_flags() hook was a bit more permissive, and allowed 'implicit' endpoints to get the 'fullmesh' flag while it is not allowed before. That's what syzbot found, triggering the following warning: WARNING: CPU: 0 PID: 6499 at net/mptcp/pm_netlink.c:1496 __mark_subflow_endp_available net/mptcp/pm_netlink.c:1496 [inline] WARNING: CPU: 0 PID: 6499 at net/mptcp/pm_netlink.c:1496 mptcp_pm_nl_fullmesh net/mptcp/pm_netlink.c:1980 [inline] WARNING: CPU: 0 PID: 6499 at net/mptcp/pm_netlink.c:1496 mptcp_nl_set_flags net/mptcp/pm_netlink.c:2003 [inline] WARNING: CPU: 0 PID: 6499 at net/mptcp/pm_netlink.c:1496 mptcp_pm_nl_set_flags+0x974/0xdc0 net/mptcp/pm_netlink.c:2064 Modules linked in: CPU: 0 UID: 0 PID: 6499 Comm: syz.1.413 Not tainted 6.13.0-rc5-syzkaller-00172-gd1bf27c4e176 #0 Hardware name: Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 RIP: 0010:__mark_subflow_endp_available net/mptcp/pm_netlink.c:1496 [inline] RIP: 0010:mptcp_pm_nl_fullmesh net/mptcp/pm_netlink.c:1980 [inline] RIP: 0010:mptcp_nl_set_flags net/mptcp/pm_netlink.c:2003 [inline] RIP: 0010:mptcp_pm_nl_set_flags+0x974/0xdc0 net/mptcp/pm_netlink.c:2064 Code: 01 00 00 49 89 c5 e8 fb 45 e8 f5 e9 b8 fc ff ff e8 f1 45 e8 f5 4c 89 f7 be 03 00 00 00 e8 44 1d 0b f9 eb a0 e8 dd 45 e8 f5 90 <0f> 0b 90 e9 17 ff ff ff 89 d9 80 e1 07 38 c1 0f 8c c9 fc ff ff 48 RSP: 0018:ffffc9000d307240 EFLAGS: 00010293 RAX: ffffffff8bb72e03 RBX: 0000000000000000 RCX: ffff88807da88000 RDX: 0000000000000000 RSI: 0000000000000000 RDI: 0000000000000000 RBP: ffffc9000d307430 R08: ffffffff8bb72cf0 R09: 1ffff1100b842a5e R10: dffffc0000000000 R11: ffffed100b842a5f R12: ffff88801e2e5ac0 R13: ffff88805c214800 R14: ffff88805c2152e8 R15: 1ffff1100b842a5d FS: 00005555619f6500(0000) GS:ffff8880b8600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000000020002840 CR3: 00000000247e6000 CR4: 00000000003526f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> genl_family_rcv_msg_doit net/netlink/genetlink.c:1115 [inline] genl_family_rcv_msg net/netlink/genetlink.c:1195 [inline] genl_rcv_msg+0xb14/0xec0 net/netlink/genetlink.c:1210 netlink_rcv_skb+0x1e3/0x430 net/netlink/af_netlink.c:2542 genl_rcv+0x28/0x40 net/netlink/genetlink.c:1219 netlink_unicast_kernel net/netlink/af_netlink.c:1321 [inline] netlink_unicast+0x7f6/0x990 net/netlink/af_netlink.c:1347 netlink_sendmsg+0x8e4/0xcb0 net/netlink/af_netlink.c:1891 sock_sendmsg_nosec net/socket.c:711 [inline] __sock_sendmsg+0x221/0x270 net/socket.c:726 ____sys_sendmsg+0x52a/0x7e0 net/socket.c:2583 ___sys_sendmsg net/socket.c:2637 [inline] __sys_sendmsg+0x269/0x350 net/socket.c:2669 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7f5fe8785d29 Code: ff ff c3 66 2e 0f 1f 84 00 00 00 00 00 0f 1f 40 00 48 89 f8 48 89 f7 48 89 d6 48 89 ca 4d 89 c2 4d 89 c8 4c 8b 4c 24 08 0f 05 <48> 3d 01 f0 ff ff 73 01 c3 48 c7 c1 a8 ff ff ff f7 d8 64 89 01 48 RSP: 002b:00007fff571f5558 EFLAGS: 00000246 ORIG_RAX: 000000000000002e RAX: ffffffffffffffda RBX: 00007f5fe8975fa0 RCX: 00007f5fe8785d29 RDX: 0000000000000000 RSI: 0000000020000480 RDI: 0000000000000007 RBP: 00007f5fe8801b08 R08: 0000000000000000 R09: 0000000000000000 R10: 0000000000000000 R11: 0000000000000246 R12: 0000000000000000 R13: 00007f5fe8975fa0 R14: 00007f5fe8975fa0 R15: 000000 ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21710 In the Linux kernel, the following vulnerability has been resolved: tcp: correct handling of extreme memory squeeze Testing with iperf3 using the "pasta" protocol splicer has revealed a problem in the way tcp handles window advertising in extreme memory squeeze situations. Under memory pressure, a socket endpoint may temporarily advertise a zero-sized window, but this is not stored as part of the socket data. The reasoning behind this is that it is considered a temporary setting which shouldn't influence any further calculations. However, if we happen to stall at an unfortunate value of the current window size, the algorithm selecting a new value will consistently fail to advertise a non-zero window once we have freed up enough memory. This means that this side's notion of the current window size is different from the one last advertised to the peer, causing the latter to not send any data to resolve the sitution. The problem occurs on the iperf3 server side, and the socket in question is a completely regular socket with the default settings for the fedora40 kernel. We do not use SO_PEEK or SO_RCVBUF on the socket. The following excerpt of a logging session, with own comments added, shows more in detail what is happening: // tcp_v4_rcv(->) // tcp_rcv_established(->) [5201<->39222]: ==== Activating log @ net/ipv4/tcp_input.c/tcp_data_queue()/5257 ==== [5201<->39222]: tcp_data_queue(->) [5201<->39222]: DROPPING skb [265600160..265665640], reason: SKB_DROP_REASON_PROTO_MEM [rcv_nxt 265600160, rcv_wnd 262144, snt_ack 265469200, win_now 131184] [copied_seq 259909392->260034360 (124968), unread 5565800, qlen 85, ofoq 0] [OFO queue: gap: 65480, len: 0] [5201<->39222]: tcp_data_queue(<-) [5201<->39222]: __tcp_transmit_skb(->) [tp->rcv_wup: 265469200, tp->rcv_wnd: 262144, tp->rcv_nxt 265600160] [5201<->39222]: tcp_select_window(->) [5201<->39222]: (inet_csk(sk)->icsk_ack.pending & ICSK_ACK_NOMEM) ? --> TRUE [tp->rcv_wup: 265469200, tp->rcv_wnd: 262144, tp->rcv_nxt 265600160] returning 0 [5201<->39222]: tcp_select_window(<-) [5201<->39222]: ADVERTISING WIN 0, ACK_SEQ: 265600160 [5201<->39222]: [__tcp_transmit_skb(<-) [5201<->39222]: tcp_rcv_established(<-) [5201<->39222]: tcp_v4_rcv(<-) // Receive queue is at 85 buffers and we are out of memory. // We drop the incoming buffer, although it is in sequence, and decide // to send an advertisement with a window of zero. // We don't update tp->rcv_wnd and tp->rcv_wup accordingly, which means // we unconditionally shrink the window. [5201<->39222]: tcp_recvmsg_locked(->) [5201<->39222]: __tcp_cleanup_rbuf(->) tp->rcv_wup: 265469200, tp->rcv_wnd: 262144, tp->rcv_nxt 265600160 [5201<->39222]: [new_win = 0, win_now = 131184, 2 * win_now = 262368] [5201<->39222]: [new_win >= (2 * win_now) ? --> time_to_ack = 0] [5201<->39222]: NOT calling tcp_send_ack() [tp->rcv_wup: 265469200, tp->rcv_wnd: 262144, tp->rcv_nxt 265600160] [5201<->39222]: __tcp_cleanup_rbuf(<-) [rcv_nxt 265600160, rcv_wnd 262144, snt_ack 265469200, win_now 131184] [copied_seq 260040464->260040464 (0), unread 5559696, qlen 85, ofoq 0] returning 6104 bytes [5201<->39222]: tcp_recvmsg_locked(<-) // After each read, the algorithm for calculating the new receive // window in __tcp_cleanup_rbuf() finds it is too small to advertise // or to update tp->rcv_wnd. // Meanwhile, the peer thinks the window is zero, and will not send // any more data to trigger an update from the interrupt mode side. [5201<->39222]: tcp_recvmsg_locked(->) [5201<->39222]: __tcp_cleanup_rbuf(->) tp->rcv_wup: 265469200, tp->rcv_wnd: 262144, tp->rcv_nxt 265600160 [5201<->39222]: [new_win = 262144, win_now = 131184, 2 * win_n ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21711 In the Linux kernel, the following vulnerability has been resolved: net/rose: prevent integer overflows in rose_setsockopt() In case of possible unpredictably large arguments passed to rose_setsockopt() and multiplied by extra values on top of that, integer overflows may occur. Do the safest minimum and fix these issues by checking the contents of 'opt' and returning -EINVAL if they are too large. Also, switch to unsigned int and remove useless check for negative 'opt' in ROSE_IDLE case.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21715 In the Linux kernel, the following vulnerability has been resolved: net: davicom: fix UAF in dm9000_drv_remove dm is netdev private data and it cannot be used after free_netdev() call. Using dm after free_netdev() can cause UAF bug. Fix it by moving free_netdev() at the end of the function. This is similar to the issue fixed in commit ad297cd2db89 ("net: qcom/emac: fix UAF in emac_remove"). This bug is detected by our static analysis tool.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21716 In the Linux kernel, the following vulnerability has been resolved: vxlan: Fix uninit-value in vxlan_vnifilter_dump() KMSAN reported an uninit-value access in vxlan_vnifilter_dump() [1]. If the length of the netlink message payload is less than sizeof(struct tunnel_msg), vxlan_vnifilter_dump() accesses bytes beyond the message. This can lead to uninit-value access. Fix this by returning an error in such situations. [1] BUG: KMSAN: uninit-value in vxlan_vnifilter_dump+0x328/0x920 drivers/net/vxlan/vxlan_vnifilter.c:422 vxlan_vnifilter_dump+0x328/0x920 drivers/net/vxlan/vxlan_vnifilter.c:422 rtnl_dumpit+0xd5/0x2f0 net/core/rtnetlink.c:6786 netlink_dump+0x93e/0x15f0 net/netlink/af_netlink.c:2317 __netlink_dump_start+0x716/0xd60 net/netlink/af_netlink.c:2432 netlink_dump_start include/linux/netlink.h:340 [inline] rtnetlink_dump_start net/core/rtnetlink.c:6815 [inline] rtnetlink_rcv_msg+0x1256/0x14a0 net/core/rtnetlink.c:6882 netlink_rcv_skb+0x467/0x660 net/netlink/af_netlink.c:2542 rtnetlink_rcv+0x35/0x40 net/core/rtnetlink.c:6944 netlink_unicast_kernel net/netlink/af_netlink.c:1321 [inline] netlink_unicast+0xed6/0x1290 net/netlink/af_netlink.c:1347 netlink_sendmsg+0x1092/0x1230 net/netlink/af_netlink.c:1891 sock_sendmsg_nosec net/socket.c:711 [inline] __sock_sendmsg+0x330/0x3d0 net/socket.c:726 ____sys_sendmsg+0x7f4/0xb50 net/socket.c:2583 ___sys_sendmsg+0x271/0x3b0 net/socket.c:2637 __sys_sendmsg net/socket.c:2669 [inline] __do_sys_sendmsg net/socket.c:2674 [inline] __se_sys_sendmsg net/socket.c:2672 [inline] __x64_sys_sendmsg+0x211/0x3e0 net/socket.c:2672 x64_sys_call+0x3878/0x3d90 arch/x86/include/generated/asm/syscalls_64.h:47 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xd9/0x1d0 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f Uninit was created at: slab_post_alloc_hook mm/slub.c:4110 [inline] slab_alloc_node mm/slub.c:4153 [inline] kmem_cache_alloc_node_noprof+0x800/0xe80 mm/slub.c:4205 kmalloc_reserve+0x13b/0x4b0 net/core/skbuff.c:587 __alloc_skb+0x347/0x7d0 net/core/skbuff.c:678 alloc_skb include/linux/skbuff.h:1323 [inline] netlink_alloc_large_skb+0xa5/0x280 net/netlink/af_netlink.c:1196 netlink_sendmsg+0xac9/0x1230 net/netlink/af_netlink.c:1866 sock_sendmsg_nosec net/socket.c:711 [inline] __sock_sendmsg+0x330/0x3d0 net/socket.c:726 ____sys_sendmsg+0x7f4/0xb50 net/socket.c:2583 ___sys_sendmsg+0x271/0x3b0 net/socket.c:2637 __sys_sendmsg net/socket.c:2669 [inline] __do_sys_sendmsg net/socket.c:2674 [inline] __se_sys_sendmsg net/socket.c:2672 [inline] __x64_sys_sendmsg+0x211/0x3e0 net/socket.c:2672 x64_sys_call+0x3878/0x3d90 arch/x86/include/generated/asm/syscalls_64.h:47 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xd9/0x1d0 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f CPU: 0 UID: 0 PID: 30991 Comm: syz.4.10630 Not tainted 6.12.0-10694-gc44daa7e3c73 #29 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.16.3-3.fc41 04/01/2014

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21718 In the Linux kernel, the following vulnerability has been resolved: net: rose: fix timer races against user threads Rose timers only acquire the socket spinlock, without checking if the socket is owned by one user thread. Add a check and rearm the timers if needed. BUG: KASAN: slab-use-after-free in rose_timer_expiry+0x31d/0x360 net/rose/rose_timer.c:174 Read of size 2 at addr ffff88802f09b82a by task swapper/0/0 CPU: 0 UID: 0 PID: 0 Comm: swapper/0 Not tainted 6.13.0-rc5-syzkaller-00172-gd1bf27c4e176 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 Call Trace: <IRQ> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x241/0x360 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x169/0x550 mm/kasan/report.c:489 kasan_report+0x143/0x180 mm/kasan/report.c:602 rose_timer_expiry+0x31d/0x360 net/rose/rose_timer.c:174 call_timer_fn+0x187/0x650 kernel/time/timer.c:1793 expire_timers kernel/time/timer.c:1844 [inline] __run_timers kernel/time/timer.c:2418 [inline] __run_timer_base+0x66a/0x8e0 kernel/time/timer.c:2430 run_timer_base kernel/time/timer.c:2439 [inline] run_timer_softirq+0xb7/0x170 kernel/time/timer.c:2449 handle_softirqs+0x2d4/0x9b0 kernel/softirq.c:561 __do_softirq kernel/softirq.c:595 [inline] invoke_softirq kernel/softirq.c:435 [inline] __irq_exit_rcu+0xf7/0x220 kernel/softirq.c:662 irq_exit_rcu+0x9/0x30 kernel/softirq.c:678 instr_sysvec_apic_timer_interrupt arch/x86/kernel/apic/apic.c:1049 [inline] sysvec_apic_timer_interrupt+0xa6/0xc0 arch/x86/kernel/apic/apic.c:1049 </IRQ>

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21720 In the Linux kernel, the following vulnerability has been resolved: xfrm: delete intermediate secpath entry in packet offload mode Packets handled by hardware have added secpath as a way to inform XFRM core code that this path was already handled. That secpath is not needed at all after policy is checked and it is removed later in the stack. However, in the case of IP forwarding is enabled (/proc/sys/net/ipv4/ip_forward), that secpath is not removed and packets which already were handled are reentered to the driver TX path with xfrm_offload set. The following kernel panic is observed in mlx5 in such case: mlx5_core 0000:04:00.0 enp4s0f0np0: Link up mlx5_core 0000:04:00.1 enp4s0f1np1: Link up Initializing XFRM netlink socket IPsec XFRM device driver BUG: kernel NULL pointer dereference, address: 0000000000000000 #PF: supervisor instruction fetch in kernel mode #PF: error_code(0x0010) - not-present page PGD 0 P4D 0 Oops: Oops: 0010 [#1] PREEMPT SMP CPU: 0 UID: 0 PID: 0 Comm: swapper/0 Not tainted 6.13.0-rc1-alex #3 Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.13.0-1ubuntu1.1 04/01/2014 RIP: 0010:0x0 Code: Unable to access opcode bytes at 0xffffffffffffffd6. RSP: 0018:ffffb87380003800 EFLAGS: 00010206 RAX: ffff8df004e02600 RBX: ffffb873800038d8 RCX: 00000000ffff98cf RDX: ffff8df00733e108 RSI: ffff8df00521fb80 RDI: ffff8df001661f00 RBP: ffffb87380003850 R08: ffff8df013980000 R09: 0000000000000010 R10: 0000000000000002 R11: 0000000000000002 R12: ffff8df001661f00 R13: ffff8df00521fb80 R14: ffff8df00733e108 R15: ffff8df011faf04e FS: 0000000000000000(0000) GS:ffff8df46b800000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: ffffffffffffffd6 CR3: 0000000106384000 CR4: 0000000000350ef0 Call Trace: <IRQ> ? show_regs+0x63/0x70 ? __die_body+0x20/0x60 ? __die+0x2b/0x40 ? page_fault_oops+0x15c/0x550 ? do_user_addr_fault+0x3ed/0x870 ? exc_page_fault+0x7f/0x190 ? asm_exc_page_fault+0x27/0x30 mlx5e_ipsec_handle_tx_skb+0xe7/0x2f0 [mlx5_core] mlx5e_xmit+0x58e/0x1980 [mlx5_core] ? __fib_lookup+0x6a/0xb0 dev_hard_start_xmit+0x82/0x1d0 sch_direct_xmit+0xfe/0x390 __dev_queue_xmit+0x6d8/0xee0 ? __fib_lookup+0x6a/0xb0 ? internal_add_timer+0x48/0x70 ? mod_timer+0xe2/0x2b0 neigh_resolve_output+0x115/0x1b0 __neigh_update+0x26a/0xc50 neigh_update+0x14/0x20 arp_process+0x2cb/0x8e0 ? __napi_build_skb+0x5e/0x70 arp_rcv+0x11e/0x1c0 ? dev_gro_receive+0x574/0x820 __netif_receive_skb_list_core+0x1cf/0x1f0 netif_receive_skb_list_internal+0x183/0x2a0 napi_complete_done+0x76/0x1c0 mlx5e_napi_poll+0x234/0x7a0 [mlx5_core] __napi_poll+0x2d/0x1f0 net_rx_action+0x1a6/0x370 ? atomic_notifier_call_chain+0x3b/0x50 ? irq_int_handler+0x15/0x20 [mlx5_core] handle_softirqs+0xb9/0x2f0 ? handle_irq_event+0x44/0x60 irq_exit_rcu+0xdb/0x100 common_interrupt+0x98/0xc0 </IRQ> <TASK> asm_common_interrupt+0x27/0x40 RIP: 0010:pv_native_safe_halt+0xb/0x10 Code: 09 c3 66 66 2e 0f 1f 84 00 00 00 00 00 66 90 0f 22 0f 1f 84 00 00 00 00 00 90 eb 07 0f 00 2d 7f e9 36 00 fb 40 00 83 ff 07 77 21 89 ff ff 24 fd 88 3d a1 bd 0f 21 f8 RSP: 0018:ffffffffbe603de8 EFLAGS: 00000202 RAX: 0000000000000000 RBX: 0000000000000000 RCX: 0000000f92f46680 RDX: 0000000000000037 RSI: 00000000ffffffff RDI: 00000000000518d4 RBP: ffffffffbe603df0 R08: 000000cd42e4dffb R09: ffffffffbe603d70 R10: 0000004d80d62680 R11: 0000000000000001 R12: ffffffffbe60bf40 R13: 0000000000000000 R14: 0000000000000000 R15: ffffffffbe60aff8 ? default_idle+0x9/0x20 arch_cpu_idle+0x9/0x10 default_idle_call+0x29/0xf0 do_idle+0x1f2/0x240 cpu_startup_entry+0x2c/0x30 rest_init+0xe7/0x100 start_kernel+0x76b/0xb90 x86_64_start_reservations+0x18/0x30 x86_64_start_kernel+0xc0/0x110 ? setup_ghcb+0xe/0x130 common_startup_64+0x13e/0x141 </TASK> Modules linked in: esp4_offload esp4 xfrm_interface xfrm6_tunnel tunnel4 tunnel6 xfrm_user xfrm_algo binf ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21721 In the Linux kernel, the following vulnerability has been resolved: nilfs2: handle errors that nilfs_prepare_chunk() may return Patch series "nilfs2: fix issues with rename operations". This series fixes BUG_ON check failures reported by syzbot around rename operations, and a minor behavioral issue where the mtime of a child directory changes when it is renamed instead of moved. This patch (of 2): The directory manipulation routines nilfs_set_link() and nilfs_delete_entry() rewrite the directory entry in the folio/page previously read by nilfs_find_entry(), so error handling is omitted on the assumption that nilfs_prepare_chunk(), which prepares the buffer for rewriting, will always succeed for these. And if an error is returned, it triggers the legacy BUG_ON() checks in each routine. This assumption is wrong, as proven by syzbot: the buffer layer called by nilfs_prepare_chunk() may call nilfs_get_block() if necessary, which may fail due to metadata corruption or other reasons. This has been there all along, but improved sanity checks and error handling may have made it more reproducible in fuzzing tests. Fix this issue by adding missing error paths in nilfs_set_link(), nilfs_delete_entry(), and their caller nilfs_rename().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21722 In the Linux kernel, the following vulnerability has been resolved: nilfs2: do not force clear folio if buffer is referenced Patch series "nilfs2: protect busy buffer heads from being force-cleared". This series fixes the buffer head state inconsistency issues reported by syzbot that occurs when the filesystem is corrupted and falls back to read-only, and the associated buffer head use-after-free issue. This patch (of 2): Syzbot has reported that after nilfs2 detects filesystem corruption and falls back to read-only, inconsistencies in the buffer state may occur. One of the inconsistencies is that when nilfs2 calls mark_buffer_dirty() to set a data or metadata buffer as dirty, but it detects that the buffer is not in the uptodate state: WARNING: CPU: 0 PID: 6049 at fs/buffer.c:1177 mark_buffer_dirty+0x2e5/0x520 fs/buffer.c:1177 ... Call Trace: <TASK> nilfs_palloc_commit_alloc_entry+0x4b/0x160 fs/nilfs2/alloc.c:598 nilfs_ifile_create_inode+0x1dd/0x3a0 fs/nilfs2/ifile.c:73 nilfs_new_inode+0x254/0x830 fs/nilfs2/inode.c:344 nilfs_mkdir+0x10d/0x340 fs/nilfs2/namei.c:218 vfs_mkdir+0x2f9/0x4f0 fs/namei.c:4257 do_mkdirat+0x264/0x3a0 fs/namei.c:4280 __do_sys_mkdirat fs/namei.c:4295 [inline] __se_sys_mkdirat fs/namei.c:4293 [inline] __x64_sys_mkdirat+0x87/0xa0 fs/namei.c:4293 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f The other is when nilfs_btree_propagate(), which propagates the dirty state to the ancestor nodes of a b-tree that point to a dirty buffer, detects that the origin buffer is not dirty, even though it should be: WARNING: CPU: 0 PID: 5245 at fs/nilfs2/btree.c:2089 nilfs_btree_propagate+0xc79/0xdf0 fs/nilfs2/btree.c:2089 ... Call Trace: <TASK> nilfs_bmap_propagate+0x75/0x120 fs/nilfs2/bmap.c:345 nilfs_collect_file_data+0x4d/0xd0 fs/nilfs2/segment.c:587 nilfs_segctor_apply_buffers+0x184/0x340 fs/nilfs2/segment.c:1006 nilfs_segctor_scan_file+0x28c/0xa50 fs/nilfs2/segment.c:1045 nilfs_segctor_collect_blocks fs/nilfs2/segment.c:1216 [inline] nilfs_segctor_collect fs/nilfs2/segment.c:1540 [inline] nilfs_segctor_do_construct+0x1c28/0x6b90 fs/nilfs2/segment.c:2115 nilfs_segctor_construct+0x181/0x6b0 fs/nilfs2/segment.c:2479 nilfs_segctor_thread_construct fs/nilfs2/segment.c:2587 [inline] nilfs_segctor_thread+0x69e/0xe80 fs/nilfs2/segment.c:2701 kthread+0x2f0/0x390 kernel/kthread.c:389 ret_from_fork+0x4b/0x80 arch/x86/kernel/process.c:147 ret_from_fork_asm+0x1a/0x30 arch/x86/entry/entry_64.S:244 </TASK> Both of these issues are caused by the callbacks that handle the page/folio write requests, forcibly clear various states, including the working state of the buffers they hold, at unexpected times when they detect read-only fallback. Fix these issues by checking if the buffer is referenced before clearing the page/folio state, and skipping the clear if it is.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21724 In the Linux kernel, the following vulnerability has been resolved: iommufd/iova_bitmap: Fix shift-out-of-bounds in iova_bitmap_offset_to_index() Resolve a UBSAN shift-out-of-bounds issue in iova_bitmap_offset_to_index() where shifting the constant "1" (of type int) by bitmap->mapped.pgshift (an unsigned long value) could result in undefined behavior. The constant "1" defaults to a 32-bit "int", and when "pgshift" exceeds 31 (e.g., pgshift = 63) the shift operation overflows, as the result cannot be represented in a 32-bit type. To resolve this, the constant is updated to "1UL", promoting it to an unsigned long type to match the operand's type.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21725 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix oops due to unset link speed It isn't guaranteed that NETWORK_INTERFACE_INFO::LinkSpeed will always be set by the server, so the client must handle any values and then prevent oopses like below from happening: Oops: divide error: 0000 [#1] PREEMPT SMP KASAN NOPTI CPU: 0 UID: 0 PID: 1323 Comm: cat Not tainted 6.13.0-rc7 #2 Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.16.3-3.fc41 04/01/2014 RIP: 0010:cifs_debug_data_proc_show+0xa45/0x1460 [cifs] Code: 00 00 48 89 df e8 3b cd 1b c1 41 f6 44 24 2c 04 0f 84 50 01 00 00 48 89 ef e8 e7 d0 1b c1 49 8b 44 24 18 31 d2 49 8d 7c 24 28 <48> f7 74 24 18 48 89 c3 e8 6e cf 1b c1 41 8b 6c 24 28 49 8d 7c 24 RSP: 0018:ffffc90001817be0 EFLAGS: 00010246 RAX: 0000000000000000 RBX: ffff88811230022c RCX: ffffffffc041bd99 RDX: 0000000000000000 RSI: 0000000000000567 RDI: ffff888112300228 RBP: ffff888112300218 R08: fffff52000302f5f R09: ffffed1022fa58ac R10: ffff888117d2c566 R11: 00000000fffffffe R12: ffff888112300200 R13: 000000012a15343f R14: 0000000000000001 R15: ffff888113f2db58 FS: 00007fe27119e740(0000) GS:ffff888148600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007fe2633c5000 CR3: 0000000124da0000 CR4: 0000000000750ef0 PKRU: 55555554 Call Trace: <TASK> ? __die_body.cold+0x19/0x27 ? die+0x2e/0x50 ? do_trap+0x159/0x1b0 ? cifs_debug_data_proc_show+0xa45/0x1460 [cifs] ? do_error_trap+0x90/0x130 ? cifs_debug_data_proc_show+0xa45/0x1460 [cifs] ? exc_divide_error+0x39/0x50 ? cifs_debug_data_proc_show+0xa45/0x1460 [cifs] ? asm_exc_divide_error+0x1a/0x20 ? cifs_debug_data_proc_show+0xa39/0x1460 [cifs] ? cifs_debug_data_proc_show+0xa45/0x1460 [cifs] ? seq_read_iter+0x42e/0x790 seq_read_iter+0x19a/0x790 proc_reg_read_iter+0xbe/0x110 ? __pfx_proc_reg_read_iter+0x10/0x10 vfs_read+0x469/0x570 ? do_user_addr_fault+0x398/0x760 ? __pfx_vfs_read+0x10/0x10 ? find_held_lock+0x8a/0xa0 ? __pfx_lock_release+0x10/0x10 ksys_read+0xd3/0x170 ? __pfx_ksys_read+0x10/0x10 ? __rcu_read_unlock+0x50/0x270 ? mark_held_locks+0x1a/0x90 do_syscall_64+0xbb/0x1d0 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7fe271288911 Code: 00 48 8b 15 01 25 10 00 f7 d8 64 89 02 b8 ff ff ff ff eb bd e8 20 ad 01 00 f3 0f 1e fa 80 3d b5 a7 10 00 00 74 13 31 c0 0f 05 <48> 3d 00 f0 ff ff 77 4f c3 66 0f 1f 44 00 00 55 48 89 e5 48 83 ec RSP: 002b:00007ffe87c079d8 EFLAGS: 00000246 ORIG_RAX: 0000000000000000 RAX: ffffffffffffffda RBX: 0000000000040000 RCX: 00007fe271288911 RDX: 0000000000040000 RSI: 00007fe2633c6000 RDI: 0000000000000003 RBP: 00007ffe87c07a00 R08: 0000000000000000 R09: 00007fe2713e6380 R10: 0000000000000022 R11: 0000000000000246 R12: 0000000000040000 R13: 00007fe2633c6000 R14: 0000000000000003 R15: 0000000000000000 </TASK> Fix this by setting cifs_server_iface::speed to a sane value (1Gbps) by default when link speed is unset.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21727 In the Linux kernel, the following vulnerability has been resolved: padata: fix UAF in padata_reorder A bug was found when run ltp test: BUG: KASAN: slab-use-after-free in padata_find_next+0x29/0x1a0 Read of size 4 at addr ffff88bbfe003524 by task kworker/u113:2/3039206 CPU: 0 PID: 3039206 Comm: kworker/u113:2 Kdump: loaded Not tainted 6.6.0+ Workqueue: pdecrypt_parallel padata_parallel_worker Call Trace: <TASK> dump_stack_lvl+0x32/0x50 print_address_description.constprop.0+0x6b/0x3d0 print_report+0xdd/0x2c0 kasan_report+0xa5/0xd0 padata_find_next+0x29/0x1a0 padata_reorder+0x131/0x220 padata_parallel_worker+0x3d/0xc0 process_one_work+0x2ec/0x5a0 If 'mdelay(10)' is added before calling 'padata_find_next' in the 'padata_reorder' function, this issue could be reproduced easily with ltp test (pcrypt_aead01). This can be explained as bellow: pcrypt_aead_encrypt ... padata_do_parallel refcount_inc(&pd->refcnt); // add refcnt ... padata_do_serial padata_reorder // pd while (1) { padata_find_next(pd, true); // using pd queue_work_on ... padata_serial_worker crypto_del_alg padata_put_pd_cnt // sub refcnt padata_free_shell padata_put_pd(ps->pd); // pd is freed // loop again, but pd is freed // call padata_find_next, UAF } In the padata_reorder function, when it loops in 'while', if the alg is deleted, the refcnt may be decreased to 0 before entering 'padata_find_next', which leads to UAF. As mentioned in [1], do_serial is supposed to be called with BHs disabled and always happen under RCU protection, to address this issue, add synchronize_rcu() in 'padata_free_shell' wait for all _do_serial calls to finish. [1] https://lore.kernel.org/all/20221028160401.cccypv4euxikusiq@parnassus.localdomain/ [2] https://lore.kernel.org/linux-kernel/jfjz5d7zwbytztackem7ibzalm5lnxldi2eofeiczqmqs2m7o6@fq426cwnjtkm/

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21735 In the Linux kernel, the following vulnerability has been resolved: NFC: nci: Add bounds checking in nci_hci_create_pipe() The "pipe" variable is a u8 which comes from the network. If it's more than 127, then it results in memory corruption in the caller, nci_hci_connect_gate().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21736 In the Linux kernel, the following vulnerability has been resolved: nilfs2: fix possible int overflows in nilfs_fiemap() Since nilfs_bmap_lookup_contig() in nilfs_fiemap() calculates its result by being prepared to go through potentially maxblocks == INT_MAX blocks, the value in n may experience an overflow caused by left shift of blkbits. While it is extremely unlikely to occur, play it safe and cast right hand expression to wider type to mitigate the issue. Found by Linux Verification Center (linuxtesting.org) with static analysis tool SVACE.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21741 In the Linux kernel, the following vulnerability has been resolved: usbnet: ipheth: fix DPE OoB read Fix an out-of-bounds DPE read, limit the number of processed DPEs to the amount that fits into the fixed-size NDP16 header.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21742 In the Linux kernel, the following vulnerability has been resolved: usbnet: ipheth: use static NDP16 location in URB Original code allowed for the start of NDP16 to be anywhere within the URB based on the `wNdpIndex` value in NTH16. Only the start position of NDP16 was checked, so it was possible for even the fixed-length part of NDP16 to extend past the end of URB, leading to an out-of-bounds read. On iOS devices, the NDP16 header always directly follows NTH16. Rely on and check for this specific format. This, along with NCM-specific minimal URB length check that already exists, will ensure that the fixed-length part of NDP16 plus a set amount of DPEs fit within the URB. Note that this commit alone does not fully address the OoB read. The limit on the amount of DPEs needs to be enforced separately.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21743 In the Linux kernel, the following vulnerability has been resolved: usbnet: ipheth: fix possible overflow in DPE length check Originally, it was possible for the DPE length check to overflow if wDatagramIndex + wDatagramLength > U16_MAX. This could lead to an OoB read. Move the wDatagramIndex term to the other side of the inequality. An existing condition ensures that wDatagramIndex < urb->actual_length.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21746 In the Linux kernel, the following vulnerability has been resolved: Input: synaptics - fix crash when enabling pass-through port When enabling a pass-through port an interrupt might come before psmouse driver binds to the pass-through port. However synaptics sub-driver tries to access psmouse instance presumably associated with the pass-through port to figure out if only 1 byte of response or entire protocol packet needs to be forwarded to the pass-through port and may crash if psmouse instance has not been attached to the port yet. Fix the crash by introducing open() and close() methods for the port and check if the port is open before trying to access psmouse instance. Because psmouse calls serio_open() only after attaching psmouse instance to serio port instance this prevents the potential crash.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21748 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix integer overflows on 32 bit systems On 32bit systems the addition operations in ipc_msg_alloc() can potentially overflow leading to memory corruption. Add bounds checking using KSMBD_IPC_MAX_PAYLOAD to avoid overflow.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21749 In the Linux kernel, the following vulnerability has been resolved: net: rose: lock the socket in rose_bind() syzbot reported a soft lockup in rose_loopback_timer(), with a repro calling bind() from multiple threads. rose_bind() must lock the socket to avoid this issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21751 In the Linux kernel, the following vulnerability has been resolved: net/mlx5: HWS, change error flow on matcher disconnect Currently, when firmware failure occurs during matcher disconnect flow, the error flow of the function reconnects the matcher back and returns an error, which continues running the calling function and eventually frees the matcher that is being disconnected. This leads to a case where we have a freed matcher on the matchers list, which in turn leads to use-after-free and eventual crash. This patch fixes that by not trying to reconnect the matcher back when some FW command fails during disconnect. Note that we're dealing here with FW error. We can't overcome this problem. This might lead to bad steering state (e.g. wrong connection between matchers), and will also lead to resource leakage, as it is the case with any other error handling during resource destruction. However, the goal here is to allow the driver to continue and not crash the machine with use-after-free error.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-21753 In the Linux kernel, the following vulnerability has been resolved: btrfs: fix use-after-free when attempting to join an aborted transaction When we are trying to join the current transaction and if it's aborted, we read its 'aborted' field after unlocking fs_info->trans_lock and without holding any extra reference count on it. This means that a concurrent task that is aborting the transaction may free the transaction before we read its 'aborted' field, leading to a use-after-free. Fix this by reading the 'aborted' field while holding fs_info->trans_lock since any freeing task must first acquire that lock and set fs_info->running_transaction to NULL before freeing the transaction. This was reported by syzbot and Dmitry with the following stack traces from KASAN: ================================================================== BUG: KASAN: slab-use-after-free in join_transaction+0xd9b/0xda0 fs/btrfs/transaction.c:278 Read of size 4 at addr ffff888011839024 by task kworker/u4:9/1128 CPU: 0 UID: 0 PID: 1128 Comm: kworker/u4:9 Not tainted 6.13.0-rc7-syzkaller-00019-gc45323b7560e #0 Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.16.3-debian-1.16.3-2~bpo12+1 04/01/2014 Workqueue: events_unbound btrfs_async_reclaim_data_space Call Trace: <TASK> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x241/0x360 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x169/0x550 mm/kasan/report.c:489 kasan_report+0x143/0x180 mm/kasan/report.c:602 join_transaction+0xd9b/0xda0 fs/btrfs/transaction.c:278 start_transaction+0xaf8/0x1670 fs/btrfs/transaction.c:697 flush_space+0x448/0xcf0 fs/btrfs/space-info.c:803 btrfs_async_reclaim_data_space+0x159/0x510 fs/btrfs/space-info.c:1321 process_one_work kernel/workqueue.c:3236 [inline] process_scheduled_works+0xa66/0x1840 kernel/workqueue.c:3317 worker_thread+0x870/0xd30 kernel/workqueue.c:3398 kthread+0x2f0/0x390 kernel/kthread.c:389 ret_from_fork+0x4b/0x80 arch/x86/kernel/process.c:147 ret_from_fork_asm+0x1a/0x30 arch/x86/entry/entry_64.S:244 </TASK> Allocated by task 5315: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x3f/0x80 mm/kasan/common.c:68 poison_kmalloc_redzone mm/kasan/common.c:377 [inline] __kasan_kmalloc+0x98/0xb0 mm/kasan/common.c:394 kasan_kmalloc include/linux/kasan.h:260 [inline] __kmalloc_cache_noprof+0x243/0x390 mm/slub.c:4329 kmalloc_noprof include/linux/slab.h:901 [inline] join_transaction+0x144/0xda0 fs/btrfs/transaction.c:308 start_transaction+0xaf8/0x1670 fs/btrfs/transaction.c:697 btrfs_create_common+0x1b2/0x2e0 fs/btrfs/inode.c:6572 lookup_open fs/namei.c:3649 [inline] open_last_lookups fs/namei.c:3748 [inline] path_openat+0x1c03/0x3590 fs/namei.c:3984 do_filp_open+0x27f/0x4e0 fs/namei.c:4014 do_sys_openat2+0x13e/0x1d0 fs/open.c:1402 do_sys_open fs/open.c:1417 [inline] __do_sys_creat fs/open.c:1495 [inline] __se_sys_creat fs/open.c:1489 [inline] __x64_sys_creat+0x123/0x170 fs/open.c:1489 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f Freed by task 5336: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x3f/0x80 mm/kasan/common.c:68 kasan_save_free_info+0x40/0x50 mm/kasan/generic.c:582 poison_slab_object mm/kasan/common.c:247 [inline] __kasan_slab_free+0x59/0x70 mm/kasan/common.c:264 kasan_slab_free include/linux/kasan.h:233 [inline] slab_free_hook mm/slub.c:2353 [inline] slab_free mm/slub.c:4613 [inline] kfree+0x196/0x430 mm/slub.c:4761 cleanup_transaction fs/btrfs/transaction.c:2063 [inline] btrfs_commit_transaction+0x2c97/0x3720 fs/btrfs/transaction.c:2598 insert_balance_item+0x1284/0x20b0 fs/btrfs/volumes.c:3757 btrfs_balance+0x992/ ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21754 In the Linux kernel, the following vulnerability has been resolved: btrfs: fix assertion failure when splitting ordered extent after transaction abort If while we are doing a direct IO write a transaction abort happens, we mark all existing ordered extents with the BTRFS_ORDERED_IOERR flag (done at btrfs_destroy_ordered_extents()), and then after that if we enter btrfs_split_ordered_extent() and the ordered extent has bytes left (meaning we have a bio that doesn't cover the whole ordered extent, see details at btrfs_extract_ordered_extent()), we will fail on the following assertion at btrfs_split_ordered_extent(): ASSERT(!(flags & ~BTRFS_ORDERED_TYPE_FLAGS)); because the BTRFS_ORDERED_IOERR flag is set and the definition of BTRFS_ORDERED_TYPE_FLAGS is just the union of all flags that identify the type of write (regular, nocow, prealloc, compressed, direct IO, encoded). Fix this by returning an error from btrfs_extract_ordered_extent() if we find the BTRFS_ORDERED_IOERR flag in the ordered extent. The error will be the error that resulted in the transaction abort or -EIO if no transaction abort happened. This was recently reported by syzbot with the following trace: FAULT_INJECTION: forcing a failure. name failslab, interval 1, probability 0, space 0, times 1 CPU: 0 UID: 0 PID: 5321 Comm: syz.0.0 Not tainted 6.13.0-rc5-syzkaller #0 Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.16.3-debian-1.16.3-2~bpo12+1 04/01/2014 Call Trace: <TASK> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x241/0x360 lib/dump_stack.c:120 fail_dump lib/fault-inject.c:53 [inline] should_fail_ex+0x3b0/0x4e0 lib/fault-inject.c:154 should_failslab+0xac/0x100 mm/failslab.c:46 slab_pre_alloc_hook mm/slub.c:4072 [inline] slab_alloc_node mm/slub.c:4148 [inline] __do_kmalloc_node mm/slub.c:4297 [inline] __kmalloc_noprof+0xdd/0x4c0 mm/slub.c:4310 kmalloc_noprof include/linux/slab.h:905 [inline] kzalloc_noprof include/linux/slab.h:1037 [inline] btrfs_chunk_alloc_add_chunk_item+0x244/0x1100 fs/btrfs/volumes.c:5742 reserve_chunk_space+0x1ca/0x2c0 fs/btrfs/block-group.c:4292 check_system_chunk fs/btrfs/block-group.c:4319 [inline] do_chunk_alloc fs/btrfs/block-group.c:3891 [inline] btrfs_chunk_alloc+0x77b/0xf80 fs/btrfs/block-group.c:4187 find_free_extent_update_loop fs/btrfs/extent-tree.c:4166 [inline] find_free_extent+0x42d1/0x5810 fs/btrfs/extent-tree.c:4579 btrfs_reserve_extent+0x422/0x810 fs/btrfs/extent-tree.c:4672 btrfs_new_extent_direct fs/btrfs/direct-io.c:186 [inline] btrfs_get_blocks_direct_write+0x706/0xfa0 fs/btrfs/direct-io.c:321 btrfs_dio_iomap_begin+0xbb7/0x1180 fs/btrfs/direct-io.c:525 iomap_iter+0x697/0xf60 fs/iomap/iter.c:90 __iomap_dio_rw+0xeb9/0x25b0 fs/iomap/direct-io.c:702 btrfs_dio_write fs/btrfs/direct-io.c:775 [inline] btrfs_direct_write+0x610/0xa30 fs/btrfs/direct-io.c:880 btrfs_do_write_iter+0x2a0/0x760 fs/btrfs/file.c:1397 do_iter_readv_writev+0x600/0x880 vfs_writev+0x376/0xba0 fs/read_write.c:1050 do_pwritev fs/read_write.c:1146 [inline] __do_sys_pwritev2 fs/read_write.c:1204 [inline] __se_sys_pwritev2+0x196/0x2b0 fs/read_write.c:1195 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7f1281f85d29 RSP: 002b:00007f12819fe038 EFLAGS: 00000246 ORIG_RAX: 0000000000000148 RAX: ffffffffffffffda RBX: 00007f1282176080 RCX: 00007f1281f85d29 RDX: 0000000000000001 RSI: 0000000020000240 RDI: 0000000000000005 RBP: 00007f12819fe090 R08: 0000000000000000 R09: 0000000000000003 R10: 0000000000007000 R11: 0000000000000246 R12: 0000000000000002 R13: 0000000000000000 R14: 00007f1282176080 R15: 00007ffcb9e23328 </TASK> BTRFS error (device loop0 state A): Transaction aborted (error -12) BTRFS: error (device loop0 state A ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21758 In the Linux kernel, the following vulnerability has been resolved: ipv6: mcast: add RCU protection to mld_newpack() mld_newpack() can be called without RTNL or RCU being held. Note that we no longer can use sock_alloc_send_skb() because ipv6.igmp_sk uses GFP_KERNEL allocations which can sleep. Instead use alloc_skb() and charge the net->ipv6.igmp_sk socket under RCU protection.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21759 In the Linux kernel, the following vulnerability has been resolved: ipv6: mcast: extend RCU protection in igmp6_send() igmp6_send() can be called without RTNL or RCU being held. Extend RCU protection so that we can safely fetch the net pointer and avoid a potential UAF. Note that we no longer can use sock_alloc_send_skb() because ipv6.igmp_sk uses GFP_KERNEL allocations which can sleep. Instead use alloc_skb() and charge the net->ipv6.igmp_sk socket under RCU protection.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2025-21764 In the Linux kernel, the following vulnerability has been resolved: ndisc: use RCU protection in ndisc_alloc_skb() ndisc_alloc_skb() can be called without RTNL or RCU being held. Add RCU protection to avoid possible UAF.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21767 In the Linux kernel, the following vulnerability has been resolved: clocksource: Use migrate_disable() to avoid calling get_random_u32() in atomic context The following bug report happened with a PREEMPT_RT kernel: BUG: sleeping function called from invalid context at kernel/locking/spinlock_rt.c:48 in_atomic(): 1, irqs_disabled(): 0, non_block: 0, pid: 2012, name: kwatchdog preempt_count: 1, expected: 0 RCU nest depth: 0, expected: 0 get_random_u32+0x4f/0x110 clocksource_verify_choose_cpus+0xab/0x1a0 clocksource_verify_percpu.part.0+0x6b/0x330 clocksource_watchdog_kthread+0x193/0x1a0 It is due to the fact that clocksource_verify_choose_cpus() is invoked with preemption disabled. This function invokes get_random_u32() to obtain random numbers for choosing CPUs. The batched_entropy_32 local lock and/or the base_crng.lock spinlock in driver/char/random.c will be acquired during the call. In PREEMPT_RT kernel, they are both sleeping locks and so cannot be acquired in atomic context. Fix this problem by using migrate_disable() to allow smp_processor_id() to be reliably used without introducing atomic context. preempt_disable() is then called after clocksource_verify_choose_cpus() but before the clocksource measurement is being run to avoid introducing unexpected latency.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21773 In the Linux kernel, the following vulnerability has been resolved: can: etas_es58x: fix potential NULL pointer dereference on udev->serial The driver assumed that es58x_dev->udev->serial could never be NULL. While this is true on commercially available devices, an attacker could spoof the device identity providing a NULL USB serial number. That would trigger a NULL pointer dereference. Add a check on es58x_dev->udev->serial before accessing it.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21775 In the Linux kernel, the following vulnerability has been resolved: can: ctucanfd: handle skb allocation failure If skb allocation fails, the pointer to struct can_frame is NULL. This is actually handled everywhere inside ctucan_err_interrupt() except for the only place. Add the missed NULL check. Found by Linux Verification Center (linuxtesting.org) with SVACE static analysis tool.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21781 In the Linux kernel, the following vulnerability has been resolved: batman-adv: fix panic during interface removal Reference counting is used to ensure that batadv_hardif_neigh_node and batadv_hard_iface are not freed before/during batadv_v_elp_throughput_metric_update work is finished. But there isn't a guarantee that the hard if will remain associated with a soft interface up until the work is finished. This fixes a crash triggered by reboot that looks like this: Call trace: batadv_v_mesh_free+0xd0/0x4dc [batman_adv] batadv_v_elp_throughput_metric_update+0x1c/0xa4 process_one_work+0x178/0x398 worker_thread+0x2e8/0x4d0 kthread+0xd8/0xdc ret_from_fork+0x10/0x20 (the batadv_v_mesh_free call is misleading, and does not actually happen) I was able to make the issue happen more reliably by changing hardif_neigh->bat_v.metric_work work to be delayed work. This allowed me to track down and confirm the fix. [sven@narfation.org: prevent entering batadv_v_elp_get_throughput without soft_iface]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21782 In the Linux kernel, the following vulnerability has been resolved: orangefs: fix a oob in orangefs_debug_write I got a syzbot report: slab-out-of-bounds Read in orangefs_debug_write... several people suggested fixes, I tested Al Viro's suggestion and made this patch.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21783 In the Linux kernel, the following vulnerability has been resolved: gpiolib: Fix crash on error in gpiochip_get_ngpios() The gpiochip_get_ngpios() uses chip_*() macros to print messages. However these macros rely on gpiodev to be initialised and set, which is not the case when called via bgpio_init(). In such a case the printing messages will crash on NULL pointer dereference. Replace chip_*() macros by the respective dev_*() ones to avoid such crash.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21784 In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: bail out when failed to load fw in psp_init_cap_microcode() In function psp_init_cap_microcode(), it should bail out when failed to load firmware, otherwise it may cause invalid memory access.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21785 In the Linux kernel, the following vulnerability has been resolved: arm64: cacheinfo: Avoid out-of-bounds write to cacheinfo array The loop that detects/populates cache information already has a bounds check on the array size but does not account for cache levels with separate data/instructions cache. Fix this by incrementing the index for any populated leaf (instead of any populated level).

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21787 In the Linux kernel, the following vulnerability has been resolved: team: better TEAM_OPTION_TYPE_STRING validation syzbot reported following splat [1] Make sure user-provided data contains one nul byte. [1] BUG: KMSAN: uninit-value in string_nocheck lib/vsprintf.c:633 [inline] BUG: KMSAN: uninit-value in string+0x3ec/0x5f0 lib/vsprintf.c:714 string_nocheck lib/vsprintf.c:633 [inline] string+0x3ec/0x5f0 lib/vsprintf.c:714 vsnprintf+0xa5d/0x1960 lib/vsprintf.c:2843 __request_module+0x252/0x9f0 kernel/module/kmod.c:149 team_mode_get drivers/net/team/team_core.c:480 [inline] team_change_mode drivers/net/team/team_core.c:607 [inline] team_mode_option_set+0x437/0x970 drivers/net/team/team_core.c:1401 team_option_set drivers/net/team/team_core.c:375 [inline] team_nl_options_set_doit+0x1339/0x1f90 drivers/net/team/team_core.c:2662 genl_family_rcv_msg_doit net/netlink/genetlink.c:1115 [inline] genl_family_rcv_msg net/netlink/genetlink.c:1195 [inline] genl_rcv_msg+0x1214/0x12c0 net/netlink/genetlink.c:1210 netlink_rcv_skb+0x375/0x650 net/netlink/af_netlink.c:2543 genl_rcv+0x40/0x60 net/netlink/genetlink.c:1219 netlink_unicast_kernel net/netlink/af_netlink.c:1322 [inline] netlink_unicast+0xf52/0x1260 net/netlink/af_netlink.c:1348 netlink_sendmsg+0x10da/0x11e0 net/netlink/af_netlink.c:1892 sock_sendmsg_nosec net/socket.c:718 [inline] __sock_sendmsg+0x30f/0x380 net/socket.c:733 ____sys_sendmsg+0x877/0xb60 net/socket.c:2573 ___sys_sendmsg+0x28d/0x3c0 net/socket.c:2627 __sys_sendmsg net/socket.c:2659 [inline] __do_sys_sendmsg net/socket.c:2664 [inline] __se_sys_sendmsg net/socket.c:2662 [inline] __x64_sys_sendmsg+0x212/0x3c0 net/socket.c:2662 x64_sys_call+0x2ed6/0x3c30 arch/x86/include/generated/asm/syscalls_64.h:47 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xcd/0x1e0 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21790 In the Linux kernel, the following vulnerability has been resolved: vxlan: check vxlan_vnigroup_init() return value vxlan_init() must check vxlan_vnigroup_init() success otherwise a crash happens later, spotted by syzbot. Oops: general protection fault, probably for non-canonical address 0xdffffc000000002c: 0000 [#1] PREEMPT SMP KASAN NOPTI KASAN: null-ptr-deref in range [0x0000000000000160-0x0000000000000167] CPU: 0 UID: 0 PID: 7313 Comm: syz-executor147 Not tainted 6.14.0-rc1-syzkaller-00276-g69b54314c975 #0 Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.16.3-debian-1.16.3-2~bpo12+1 04/01/2014 RIP: 0010:vxlan_vnigroup_uninit+0x89/0x500 drivers/net/vxlan/vxlan_vnifilter.c:912 Code: 00 48 8b 44 24 08 4c 8b b0 98 41 00 00 49 8d 86 60 01 00 00 48 89 c2 48 89 44 24 10 48 b8 00 00 00 00 00 fc ff df 48 c1 ea 03 <80> 3c 02 00 0f 85 4d 04 00 00 49 8b 86 60 01 00 00 48 ba 00 00 00 RSP: 0018:ffffc9000cc1eea8 EFLAGS: 00010202 RAX: dffffc0000000000 RBX: 0000000000000001 RCX: ffffffff8672effb RDX: 000000000000002c RSI: ffffffff8672ecb9 RDI: ffff8880461b4f18 RBP: ffff8880461b4ef4 R08: 0000000000000001 R09: 0000000000000000 R10: 0000000000000001 R11: 0000000000000000 R12: 0000000000020000 R13: ffff8880461b0d80 R14: 0000000000000000 R15: dffffc0000000000 FS: 00007fecfa95d6c0(0000) GS:ffff88806a600000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007fecfa95cfb8 CR3: 000000004472c000 CR4: 0000000000352ef0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> vxlan_uninit+0x1ab/0x200 drivers/net/vxlan/vxlan_core.c:2942 unregister_netdevice_many_notify+0x12d6/0x1f30 net/core/dev.c:11824 unregister_netdevice_many net/core/dev.c:11866 [inline] unregister_netdevice_queue+0x307/0x3f0 net/core/dev.c:11736 register_netdevice+0x1829/0x1eb0 net/core/dev.c:10901 __vxlan_dev_create+0x7c6/0xa30 drivers/net/vxlan/vxlan_core.c:3981 vxlan_newlink+0xd1/0x130 drivers/net/vxlan/vxlan_core.c:4407 rtnl_newlink_create net/core/rtnetlink.c:3795 [inline] __rtnl_newlink net/core/rtnetlink.c:3906 [inline]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21793 In the Linux kernel, the following vulnerability has been resolved: spi: sn-f-ospi: Fix division by zero When there is no dummy cycle in the spi-nor commands, both dummy bus cycle bytes and width are zero. Because of the cpu's warning when divided by zero, the warning should be avoided. Return just zero to avoid such calculations.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21795 In the Linux kernel, the following vulnerability has been resolved: NFSD: fix hang in nfsd4_shutdown_callback If nfs4_client is in courtesy state then there is no point to send the callback. This causes nfsd4_shutdown_callback to hang since cl_cb_inflight is not 0. This hang lasts about 15 minutes until TCP notifies NFSD that the connection was dropped. This patch modifies nfsd4_run_cb_work to skip the RPC call if nfs4_client is in courtesy state.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21798 In the Linux kernel, the following vulnerability has been resolved: firewire: test: Fix potential null dereference in firewire kunit test kunit_kzalloc() may return a NULL pointer, dereferencing it without NULL check may lead to NULL dereference. Add a NULL check for test_state.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21799 In the Linux kernel, the following vulnerability has been resolved: net: ethernet: ti: am65-cpsw: fix freeing IRQ in am65_cpsw_nuss_remove_tx_chns() When getting the IRQ we use k3_udma_glue_tx_get_irq() which returns negative error value on error. So not NULL check is not sufficient to deteremine if IRQ is valid. Check that IRQ is greater then zero to ensure it is valid. There is no issue at probe time but at runtime user can invoke .set_channels which results in the following call chain. am65_cpsw_set_channels() am65_cpsw_nuss_update_tx_rx_chns() am65_cpsw_nuss_remove_tx_chns() am65_cpsw_nuss_init_tx_chns() At this point if am65_cpsw_nuss_init_tx_chns() fails due to k3_udma_glue_tx_get_irq() then tx_chn->irq will be set to a negative value. Then, at subsequent .set_channels with higher channel count we will attempt to free an invalid IRQ in am65_cpsw_nuss_remove_tx_chns() leading to a kernel warning. The issue is present in the original commit that introduced this driver, although there, am65_cpsw_nuss_update_tx_rx_chns() existed as am65_cpsw_nuss_update_tx_chns().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21801 In the Linux kernel, the following vulnerability has been resolved: net: ravb: Fix missing rtnl lock in suspend/resume path Fix the suspend/resume path by ensuring the rtnl lock is held where required. Calls to ravb_open, ravb_close and wol operations must be performed under the rtnl lock to prevent conflicts with ongoing ndo operations. Without this fix, the following warning is triggered: [ 39.032969] ============================= [ 39.032983] WARNING: suspicious RCU usage [ 39.033019] ----------------------------- [ 39.033033] drivers/net/phy/phy_device.c:2004 suspicious rcu_dereference_protected() usage! ... [ 39.033597] stack backtrace: [ 39.033613] CPU: 0 UID: 0 PID: 174 Comm: python3 Not tainted 6.13.0-rc7-next-20250116-arm64-renesas-00002-g35245dfdc62c #7 [ 39.033623] Hardware name: Renesas SMARC EVK version 2 based on r9a08g045s33 (DT) [ 39.033628] Call trace: [ 39.033633] show_stack+0x14/0x1c (C) [ 39.033652] dump_stack_lvl+0xb4/0xc4 [ 39.033664] dump_stack+0x14/0x1c [ 39.033671] lockdep_rcu_suspicious+0x16c/0x22c [ 39.033682] phy_detach+0x160/0x190 [ 39.033694] phy_disconnect+0x40/0x54 [ 39.033703] ravb_close+0x6c/0x1cc [ 39.033714] ravb_suspend+0x48/0x120 [ 39.033721] dpm_run_callback+0x4c/0x14c [ 39.033731] device_suspend+0x11c/0x4dc [ 39.033740] dpm_suspend+0xdc/0x214 [ 39.033748] dpm_suspend_start+0x48/0x60 [ 39.033758] suspend_devices_and_enter+0x124/0x574 [ 39.033769] pm_suspend+0x1ac/0x274 [ 39.033778] state_store+0x88/0x124 [ 39.033788] kobj_attr_store+0x14/0x24 [ 39.033798] sysfs_kf_write+0x48/0x6c [ 39.033808] kernfs_fop_write_iter+0x118/0x1a8 [ 39.033817] vfs_write+0x27c/0x378 [ 39.033825] ksys_write+0x64/0xf4 [ 39.033833] __arm64_sys_write+0x18/0x20 [ 39.033841] invoke_syscall+0x44/0x104 [ 39.033852] el0_svc_common.constprop.0+0xb4/0xd4 [ 39.033862] do_el0_svc+0x18/0x20 [ 39.033870] el0_svc+0x3c/0xf0 [ 39.033880] el0t_64_sync_handler+0xc0/0xc4 [ 39.033888] el0t_64_sync+0x154/0x158 [ 39.041274] ravb 11c30000.ethernet eth0: Link is Down

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2025-21802 In the Linux kernel, the following vulnerability has been resolved: net: hns3: fix oops when unload drivers paralleling When unload hclge driver, it tries to disable sriov first for each ae_dev node from hnae3_ae_dev_list. If user unloads hns3 driver at the time, because it removes all the ae_dev nodes, and it may cause oops. But we can't simply use hnae3_common_lock for this. Because in the process flow of pci_disable_sriov(), it will trigger the remove flow of VF, which will also take hnae3_common_lock. To fixes it, introduce a new mutex to protect the unload process.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21804 In the Linux kernel, the following vulnerability has been resolved: PCI: rcar-ep: Fix incorrect variable used when calling devm_request_mem_region() The rcar_pcie_parse_outbound_ranges() uses the devm_request_mem_region() macro to request a needed resource. A string variable that lives on the stack is then used to store a dynamically computed resource name, which is then passed on as one of the macro arguments. This can lead to undefined behavior. Depending on the current contents of the memory, the manifestations of errors may vary. One possible output may be as follows: $ cat /proc/iomem 30000000-37ffffff : 38000000-3fffffff : Sometimes, garbage may appear after the colon. In very rare cases, if no NULL-terminator is found in memory, the system might crash because the string iterator will overrun which can lead to access of unmapped memory above the stack. Thus, fix this by replacing outbound_name with the name of the previously requested resource. With the changes applied, the output will be as follows: $ cat /proc/iomem 30000000-37ffffff : memory2 38000000-3fffffff : memory3 [kwilczynski: commit log]

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21808 In the Linux kernel, the following vulnerability has been resolved: net: xdp: Disallow attaching device-bound programs in generic mode Device-bound programs are used to support RX metadata kfuncs. These kfuncs are driver-specific and rely on the driver context to read the metadata. This means they can't work in generic XDP mode. However, there is no check to disallow such programs from being attached in generic mode, in which case the metadata kfuncs will be called in an invalid context, leading to crashes. Fix this by adding a check to disallow attaching device-bound programs in generic mode.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21809 In the Linux kernel, the following vulnerability has been resolved: rxrpc, afs: Fix peer hash locking vs RCU callback In its address list, afs now retains pointers to and refs on one or more rxrpc_peer objects. The address list is freed under RCU and at this time, it puts the refs on those peers. Now, when an rxrpc_peer object runs out of refs, it gets removed from the peer hash table and, for that, rxrpc has to take a spinlock. However, it is now being called from afs's RCU cleanup, which takes place in BH context - but it is just taking an ordinary spinlock. The put may also be called from non-BH context, and so there exists the possibility of deadlock if the BH-based RCU cleanup happens whilst the hash spinlock is held. This led to the attached lockdep complaint. Fix this by changing spinlocks of rxnet->peer_hash_lock back to BH-disabling locks. ================================ WARNING: inconsistent lock state 6.13.0-rc5-build2+ #1223 Tainted: G E -------------------------------- inconsistent {SOFTIRQ-ON-W} -> {IN-SOFTIRQ-W} usage. swapper/1/0 [HC0[0]:SC1[1]:HE1:SE0] takes: ffff88810babe228 (&rxnet->peer_hash_lock){+.?.}-{3:3}, at: rxrpc_put_peer+0xcb/0x180 {SOFTIRQ-ON-W} state was registered at: mark_usage+0x164/0x180 __lock_acquire+0x544/0x990 lock_acquire.part.0+0x103/0x280 _raw_spin_lock+0x2f/0x40 rxrpc_peer_keepalive_worker+0x144/0x440 process_one_work+0x486/0x7c0 process_scheduled_works+0x73/0x90 worker_thread+0x1c8/0x2a0 kthread+0x19b/0x1b0 ret_from_fork+0x24/0x40 ret_from_fork_asm+0x1a/0x30 irq event stamp: 972402 hardirqs last enabled at (972402): [<ffffffff8244360e>] _raw_spin_unlock_irqrestore+0x2e/0x50 hardirqs last disabled at (972401): [<ffffffff82443328>] _raw_spin_lock_irqsave+0x18/0x60 softirqs last enabled at (972300): [<ffffffff810ffbbe>] handle_softirqs+0x3ee/0x430 softirqs last disabled at (972313): [<ffffffff810ffc54>] __irq_exit_rcu+0x44/0x110 other info that might help us debug this: Possible unsafe locking scenario: CPU0 ---- lock(&rxnet->peer_hash_lock); <Interrupt> lock(&rxnet->peer_hash_lock); *** DEADLOCK *** 1 lock held by swapper/1/0: #0: ffffffff83576be0 (rcu_callback){....}-{0:0}, at: rcu_lock_acquire+0x7/0x30 stack backtrace: CPU: 1 UID: 0 PID: 0 Comm: swapper/1 Tainted: G E 6.13.0-rc5-build2+ #1223 Tainted: [E]=UNSIGNED_MODULE Hardware name: ASUS All Series/H97-PLUS, BIOS 2306 10/09/2014 Call Trace: <IRQ> dump_stack_lvl+0x57/0x80 print_usage_bug.part.0+0x227/0x240 valid_state+0x53/0x70 mark_lock_irq+0xa5/0x2f0 mark_lock+0xf7/0x170 mark_usage+0xe1/0x180 __lock_acquire+0x544/0x990 lock_acquire.part.0+0x103/0x280 _raw_spin_lock+0x2f/0x40 rxrpc_put_peer+0xcb/0x180 afs_free_addrlist+0x46/0x90 [kafs] rcu_do_batch+0x2d2/0x640 rcu_core+0x2f7/0x350 handle_softirqs+0x1ee/0x430 __irq_exit_rcu+0x44/0x110 irq_exit_rcu+0xa/0x30 sysvec_apic_timer_interrupt+0x7f/0xa0 </IRQ>

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21810 In the Linux kernel, the following vulnerability has been resolved: driver core: class: Fix wild pointer dereferences in API class_dev_iter_next() There are a potential wild pointer dereferences issue regarding APIs class_dev_iter_(init|next|exit)(), as explained by below typical usage: // All members of @iter are wild pointers. struct class_dev_iter iter; // class_dev_iter_init(@iter, @class, ...) checks parameter @class for // potential class_to_subsys() error, and it returns void type and does // not initialize its output parameter @iter, so caller can not detect // the error and continues to invoke class_dev_iter_next(@iter) even if // @iter still contains wild pointers. class_dev_iter_init(&iter, ...); // Dereference these wild pointers in @iter here once suffer the error. while (dev = class_dev_iter_next(&iter)) { ... }; // Also dereference these wild pointers here. class_dev_iter_exit(&iter); Actually, all callers of these APIs have such usage pattern in kernel tree. Fix by: - Initialize output parameter @iter by memset() in class_dev_iter_init() and give callers prompt by pr_crit() for the error. - Check if @iter is valid in class_dev_iter_next().

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21811 In the Linux kernel, the following vulnerability has been resolved: nilfs2: protect access to buffers with no active references nilfs_lookup_dirty_data_buffers(), which iterates through the buffers attached to dirty data folios/pages, accesses the attached buffers without locking the folios/pages. For data cache, nilfs_clear_folio_dirty() may be called asynchronously when the file system degenerates to read only, so nilfs_lookup_dirty_data_buffers() still has the potential to cause use after free issues when buffers lose the protection of their dirty state midway due to this asynchronous clearing and are unintentionally freed by try_to_free_buffers(). Eliminate this race issue by adjusting the lock section in this function.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21812 In the Linux kernel, the following vulnerability has been resolved: ax25: rcu protect dev->ax25_ptr syzbot found a lockdep issue [1]. We should remove ax25 RTNL dependency in ax25_setsockopt() This should also fix a variety of possible UAF in ax25. [1] WARNING: possible circular locking dependency detected 6.13.0-rc3-syzkaller-00762-g9268abe611b0 #0 Not tainted ------------------------------------------------------ syz.5.1818/12806 is trying to acquire lock: ffffffff8fcb3988 (rtnl_mutex){+.+.}-{4:4}, at: ax25_setsockopt+0xa55/0xe90 net/ax25/af_ax25.c:680 but task is already holding lock: ffff8880617ac258 (sk_lock-AF_AX25){+.+.}-{0:0}, at: lock_sock include/net/sock.h:1618 [inline] ffff8880617ac258 (sk_lock-AF_AX25){+.+.}-{0:0}, at: ax25_setsockopt+0x209/0xe90 net/ax25/af_ax25.c:574 which lock already depends on the new lock. the existing dependency chain (in reverse order) is: -> #1 (sk_lock-AF_AX25){+.+.}-{0:0}: lock_acquire+0x1ed/0x550 kernel/locking/lockdep.c:5849 lock_sock_nested+0x48/0x100 net/core/sock.c:3642 lock_sock include/net/sock.h:1618 [inline] ax25_kill_by_device net/ax25/af_ax25.c:101 [inline] ax25_device_event+0x24d/0x580 net/ax25/af_ax25.c:146 notifier_call_chain+0x1a5/0x3f0 kernel/notifier.c:85 __dev_notify_flags+0x207/0x400 dev_change_flags+0xf0/0x1a0 net/core/dev.c:9026 dev_ifsioc+0x7c8/0xe70 net/core/dev_ioctl.c:563 dev_ioctl+0x719/0x1340 net/core/dev_ioctl.c:820 sock_do_ioctl+0x240/0x460 net/socket.c:1234 sock_ioctl+0x626/0x8e0 net/socket.c:1339 vfs_ioctl fs/ioctl.c:51 [inline] __do_sys_ioctl fs/ioctl.c:906 [inline] __se_sys_ioctl+0xf5/0x170 fs/ioctl.c:892 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f -> #0 (rtnl_mutex){+.+.}-{4:4}: check_prev_add kernel/locking/lockdep.c:3161 [inline] check_prevs_add kernel/locking/lockdep.c:3280 [inline] validate_chain+0x18ef/0x5920 kernel/locking/lockdep.c:3904 __lock_acquire+0x1397/0x2100 kernel/locking/lockdep.c:5226 lock_acquire+0x1ed/0x550 kernel/locking/lockdep.c:5849 __mutex_lock_common kernel/locking/mutex.c:585 [inline] __mutex_lock+0x1ac/0xee0 kernel/locking/mutex.c:735 ax25_setsockopt+0xa55/0xe90 net/ax25/af_ax25.c:680 do_sock_setsockopt+0x3af/0x720 net/socket.c:2324 __sys_setsockopt net/socket.c:2349 [inline] __do_sys_setsockopt net/socket.c:2355 [inline] __se_sys_setsockopt net/socket.c:2352 [inline] __x64_sys_setsockopt+0x1ee/0x280 net/socket.c:2352 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf3/0x230 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f other info that might help us debug this: Possible unsafe locking scenario: CPU0 CPU1 ---- ---- lock(sk_lock-AF_AX25); lock(rtnl_mutex); lock(sk_lock-AF_AX25); lock(rtnl_mutex); *** DEADLOCK *** 1 lock held by syz.5.1818/12806: #0: ffff8880617ac258 (sk_lock-AF_AX25){+.+.}-{0:0}, at: lock_sock include/net/sock.h:1618 [inline] #0: ffff8880617ac258 (sk_lock-AF_AX25){+.+.}-{0:0}, at: ax25_setsockopt+0x209/0xe90 net/ax25/af_ax25.c:574 stack backtrace: CPU: 1 UID: 0 PID: 12806 Comm: syz.5.1818 Not tainted 6.13.0-rc3-syzkaller-00762-g9268abe611b0 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 09/13/2024 Call Trace: <TASK> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x241/0x360 lib/dump_stack.c:120 print_circular_bug+0x13a/0x1b0 kernel/locking/lockdep.c:2074 check_noncircular+0x36a/0x4a0 kernel/locking/lockdep.c:2206 check_prev_add kernel/locking/lockdep.c:3161 [inline] check_prevs_add kernel/lockin ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2025-21815 In the Linux kernel, the following vulnerability has been resolved: mm/compaction: fix UBSAN shift-out-of-bounds warning syzkaller reported a UBSAN shift-out-of-bounds warning of (1UL << order) in isolate_freepages_block(). The bogus compound_order can be any value because it is union with flags. Add back the MAX_PAGE_ORDER check to fix the warning.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21820 In the Linux kernel, the following vulnerability has been resolved: tty: xilinx_uartps: split sysrq handling lockdep detects the following circular locking dependency: CPU 0 CPU 1 ========================== ============================ cdns_uart_isr() printk() uart_port_lock(port) console_lock() cdns_uart_console_write() if (!port->sysrq) uart_port_lock(port) uart_handle_break() port->sysrq = ... uart_handle_sysrq_char() printk() console_lock() The fixed commit attempts to avoid this situation by only taking the port lock in cdns_uart_console_write if port->sysrq unset. However, if (as shown above) cdns_uart_console_write runs before port->sysrq is set, then it will try to take the port lock anyway. This may result in a deadlock. Fix this by splitting sysrq handling into two parts. We use the prepare helper under the port lock and defer handling until we release the lock.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21823 In the Linux kernel, the following vulnerability has been resolved: batman-adv: Drop unmanaged ELP metric worker The ELP worker needs to calculate new metric values for all neighbors "reachable" over an interface. Some of the used metric sources require locks which might need to sleep. This sleep is incompatible with the RCU list iterator used for the recorded neighbors. The initial approach to work around of this problem was to queue another work item per neighbor and then run this in a new context. Even when this solved the RCU vs might_sleep() conflict, it has a major problems: Nothing was stopping the work item in case it is not needed anymore - for example because one of the related interfaces was removed or the batman-adv module was unloaded - resulting in potential invalid memory accesses. Directly canceling the metric worker also has various problems: * cancel_work_sync for a to-be-deactivated interface is called with rtnl_lock held. But the code in the ELP metric worker also tries to use rtnl_lock() - which will never return in this case. This also means that cancel_work_sync would never return because it is waiting for the worker to finish. * iterating over the neighbor list for the to-be-deactivated interface is currently done using the RCU specific methods. Which means that it is possible to miss items when iterating over it without the associated spinlock - a behaviour which is acceptable for a periodic metric check but not for a cleanup routine (which must "stop" all still running workers) The better approch is to get rid of the per interface neighbor metric worker and handle everything in the interface worker. The original problems are solved by: * creating a list of neighbors which require new metric information inside the RCU protected context, gathering the metric according to the new list outside the RCU protected context * only use rcu_trylock inside metric gathering code to avoid a deadlock when the cancel_delayed_work_sync is called in the interface removal code (which is called with the rtnl_lock held)

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21826 In the Linux kernel, the following vulnerability has been resolved: netfilter: nf_tables: reject mismatching sum of field_len with set key length The field length description provides the length of each separated key field in the concatenation, each field gets rounded up to 32-bits to calculate the pipapo rule width from pipapo_init(). The set key length provides the total size of the key aligned to 32-bits. Register-based arithmetics still allows for combining mismatching set key length and field length description, eg. set key length 10 and field description [ 5, 4 ] leading to pipapo width of 12.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21830 In the Linux kernel, the following vulnerability has been resolved: landlock: Handle weird files A corrupted filesystem (e.g. bcachefs) might return weird files. Instead of throwing a warning and allowing access to such file, treat them as regular files.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-21832 In the Linux kernel, the following vulnerability has been resolved: block: don't revert iter for -EIOCBQUEUED blkdev_read_iter() has a few odd checks, like gating the position and count adjustment on whether or not the result is bigger-than-or-equal to zero (where bigger than makes more sense), and not checking the return value of blkdev_direct_IO() before doing an iov_iter_revert(). The latter can lead to attempting to revert with a negative value, which when passed to iov_iter_revert() as an unsigned value will lead to throwing a WARN_ON() because unroll is bigger than MAX_RW_COUNT. Be sane and don't revert for -EIOCBQUEUED, like what is done in other spots.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2025-21835 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: f_midi: fix MIDI Streaming descriptor lengths While the MIDI jacks are configured correctly, and the MIDIStreaming endpoint descriptors are filled with the correct information, bNumEmbMIDIJack and bLength are set incorrectly in these descriptors. This does not matter when the numbers of in and out ports are equal, but when they differ the host will receive broken descriptors with uninitialized stack memory leaking into the descriptor for whichever value is smaller. The precise meaning of "in" and "out" in the port counts is not clearly defined and can be confusing. But elsewhere the driver consistently uses this to match the USB meaning of IN and OUT viewed from the host, so that "in" ports send data to the host and "out" ports receive data from it.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21836 In the Linux kernel, the following vulnerability has been resolved: io_uring/kbuf: reallocate buf lists on upgrade IORING_REGISTER_PBUF_RING can reuse an old struct io_buffer_list if it was created for legacy selected buffer and has been emptied. It violates the requirement that most of the field should stay stable after publish. Always reallocate it instead.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21848 In the Linux kernel, the following vulnerability has been resolved: nfp: bpf: Add check for nfp_app_ctrl_msg_alloc() Add check for the return value of nfp_app_ctrl_msg_alloc() in nfp_bpf_cmsg_alloc() to prevent null pointer dereference.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21854 In the Linux kernel, the following vulnerability has been resolved: sockmap, vsock: For connectible sockets allow only connected sockmap expects all vsocks to have a transport assigned, which is expressed in vsock_proto::psock_update_sk_prot(). However, there is an edge case where an unconnected (connectible) socket may lose its previously assigned transport. This is handled with a NULL check in the vsock/BPF recv path. Another design detail is that listening vsocks are not supposed to have any transport assigned at all. Which implies they are not supported by the sockmap. But this is complicated by the fact that a socket, before switching to TCP_LISTEN, may have had some transport assigned during a failed connect() attempt. Hence, we may end up with a listening vsock in a sockmap, which blows up quickly: KASAN: null-ptr-deref in range [0x0000000000000120-0x0000000000000127] CPU: 7 UID: 0 PID: 56 Comm: kworker/7:0 Not tainted 6.14.0-rc1+ Workqueue: vsock-loopback vsock_loopback_work RIP: 0010:vsock_read_skb+0x4b/0x90 Call Trace: sk_psock_verdict_data_ready+0xa4/0x2e0 virtio_transport_recv_pkt+0x1ca8/0x2acc vsock_loopback_work+0x27d/0x3f0 process_one_work+0x846/0x1420 worker_thread+0x5b3/0xf80 kthread+0x35a/0x700 ret_from_fork+0x2d/0x70 ret_from_fork_asm+0x1a/0x30 For connectible sockets, instead of relying solely on the state of vsk->transport, tell sockmap to only allow those representing established connections. This aligns with the behaviour for AF_INET and AF_UNIX.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21856 In the Linux kernel, the following vulnerability has been resolved: s390/ism: add release function for struct device According to device_release() in /drivers/base/core.c, a device without a release function is a broken device and must be fixed. The current code directly frees the device after calling device_add() without waiting for other kernel parts to release their references. Thus, a reference could still be held to a struct device, e.g., by sysfs, leading to potential use-after-free issues if a proper release function is not set.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21858 In the Linux kernel, the following vulnerability has been resolved: geneve: Fix use-after-free in geneve_find_dev(). syzkaller reported a use-after-free in geneve_find_dev() [0] without repro. geneve_configure() links struct geneve_dev.next to net_generic(net, geneve_net_id)->geneve_list. The net here could differ from dev_net(dev) if IFLA_NET_NS_PID, IFLA_NET_NS_FD, or IFLA_TARGET_NETNSID is set. When dev_net(dev) is dismantled, geneve_exit_batch_rtnl() finally calls unregister_netdevice_queue() for each dev in the netns, and later the dev is freed. However, its geneve_dev.next is still linked to the backend UDP socket netns. Then, use-after-free will occur when another geneve dev is created in the netns. Let's call geneve_dellink() instead in geneve_destroy_tunnels(). [0]: BUG: KASAN: slab-use-after-free in geneve_find_dev drivers/net/geneve.c:1295 [inline] BUG: KASAN: slab-use-after-free in geneve_configure+0x234/0x858 drivers/net/geneve.c:1343 Read of size 2 at addr ffff000054d6ee24 by task syz.1.4029/13441 CPU: 1 UID: 0 PID: 13441 Comm: syz.1.4029 Not tainted 6.13.0-g0ad9617c78ac #24 dc35ca22c79fb82e8e7bc5c9c9adafea898b1e3d Hardware name: linux,dummy-virt (DT) Call trace: show_stack+0x38/0x50 arch/arm64/kernel/stacktrace.c:466 (C) __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0xbc/0x108 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x16c/0x6f0 mm/kasan/report.c:489 kasan_report+0xc0/0x120 mm/kasan/report.c:602 __asan_report_load2_noabort+0x20/0x30 mm/kasan/report_generic.c:379 geneve_find_dev drivers/net/geneve.c:1295 [inline] geneve_configure+0x234/0x858 drivers/net/geneve.c:1343 geneve_newlink+0xb8/0x128 drivers/net/geneve.c:1634 rtnl_newlink_create+0x23c/0x868 net/core/rtnetlink.c:3795 __rtnl_newlink net/core/rtnetlink.c:3906 [inline] rtnl_newlink+0x1054/0x1630 net/core/rtnetlink.c:4021 rtnetlink_rcv_msg+0x61c/0x918 net/core/rtnetlink.c:6911 netlink_rcv_skb+0x1dc/0x398 net/netlink/af_netlink.c:2543 rtnetlink_rcv+0x34/0x50 net/core/rtnetlink.c:6938 netlink_unicast_kernel net/netlink/af_netlink.c:1322 [inline] netlink_unicast+0x618/0x838 net/netlink/af_netlink.c:1348 netlink_sendmsg+0x5fc/0x8b0 net/netlink/af_netlink.c:1892 sock_sendmsg_nosec net/socket.c:713 [inline] __sock_sendmsg net/socket.c:728 [inline] ____sys_sendmsg+0x410/0x6f8 net/socket.c:2568 ___sys_sendmsg+0x178/0x1d8 net/socket.c:2622 __sys_sendmsg net/socket.c:2654 [inline] __do_sys_sendmsg net/socket.c:2659 [inline] __se_sys_sendmsg net/socket.c:2657 [inline] __arm64_sys_sendmsg+0x12c/0x1c8 net/socket.c:2657 __invoke_syscall arch/arm64/kernel/syscall.c:35 [inline] invoke_syscall+0x90/0x278 arch/arm64/kernel/syscall.c:49 el0_svc_common+0x13c/0x250 arch/arm64/kernel/syscall.c:132 do_el0_svc+0x54/0x70 arch/arm64/kernel/syscall.c:151 el0_svc+0x4c/0xa8 arch/arm64/kernel/entry-common.c:744 el0t_64_sync_handler+0x78/0x108 arch/arm64/kernel/entry-common.c:762 el0t_64_sync+0x198/0x1a0 arch/arm64/kernel/entry.S:600 Allocated by task 13247: kasan_save_stack mm/kasan/common.c:47 [inline] kasan_save_track+0x30/0x68 mm/kasan/common.c:68 kasan_save_alloc_info+0x44/0x58 mm/kasan/generic.c:568 poison_kmalloc_redzone mm/kasan/common.c:377 [inline] __kasan_kmalloc+0x84/0xa0 mm/kasan/common.c:394 kasan_kmalloc include/linux/kasan.h:260 [inline] __do_kmalloc_node mm/slub.c:4298 [inline] __kmalloc_node_noprof+0x2a0/0x560 mm/slub.c:4304 __kvmalloc_node_noprof+0x9c/0x230 mm/util.c:645 alloc_netdev_mqs+0xb8/0x11a0 net/core/dev.c:11470 rtnl_create_link+0x2b8/0xb50 net/core/rtnetlink.c:3604 rtnl_newlink_create+0x19c/0x868 net/core/rtnetlink.c:3780 __rtnl_newlink net/core/rtnetlink.c:3906 [inline] rtnl_newlink+0x1054/0x1630 net/core/rtnetlink.c:4021 rtnetlink_rcv_msg+0x61c/0x918 net/core/rtnetlink.c:6911 netlink_rcv_skb+0x1dc/0x398 net/netlink/af_netlink.c:2543 rtnetlink_rcv+0x34/0x50 net/core/rtnetlink.c:6938 netlink_unicast_kernel net/netlink/af_n ---truncated---

dex-runtime-python-builder-7.1.9.1078-compat
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21859 In the Linux kernel, the following vulnerability has been resolved: USB: gadget: f_midi: f_midi_complete to call queue_work When using USB MIDI, a lock is attempted to be acquired twice through a re-entrant call to f_midi_transmit, causing a deadlock. Fix it by using queue_work() to schedule the inner f_midi_transmit() via a high priority work queue from the completion handler.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21864 In the Linux kernel, the following vulnerability has been resolved: tcp: drop secpath at the same time as we currently drop dst Xiumei reported hitting the WARN in xfrm6_tunnel_net_exit while running tests that boil down to: - create a pair of netns - run a basic TCP test over ipcomp6 - delete the pair of netns The xfrm_state found on spi_byaddr was not deleted at the time we delete the netns, because we still have a reference on it. This lingering reference comes from a secpath (which holds a ref on the xfrm_state), which is still attached to an skb. This skb is not leaked, it ends up on sk_receive_queue and then gets defer-free'd by skb_attempt_defer_free. The problem happens when we defer freeing an skb (push it on one CPU's defer_list), and don't flush that list before the netns is deleted. In that case, we still have a reference on the xfrm_state that we don't expect at this point. We already drop the skb's dst in the TCP receive path when it's no longer needed, so let's also drop the secpath. At this point, tcp_filter has already called into the LSM hooks that may require the secpath, so it should not be needed anymore. However, in some of those places, the MPTCP extension has just been attached to the skb, so we cannot simply drop all extensions.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21867 In the Linux kernel, the following vulnerability has been resolved: bpf, test_run: Fix use-after-free issue in eth_skb_pkt_type() KMSAN reported a use-after-free issue in eth_skb_pkt_type()[1]. The cause of the issue was that eth_skb_pkt_type() accessed skb's data that didn't contain an Ethernet header. This occurs when bpf_prog_test_run_xdp() passes an invalid value as the user_data argument to bpf_test_init(). Fix this by returning an error when user_data is less than ETH_HLEN in bpf_test_init(). Additionally, remove the check for "if (user_size > size)" as it is unnecessary. [1] BUG: KMSAN: use-after-free in eth_skb_pkt_type include/linux/etherdevice.h:627 [inline] BUG: KMSAN: use-after-free in eth_type_trans+0x4ee/0x980 net/ethernet/eth.c:165 eth_skb_pkt_type include/linux/etherdevice.h:627 [inline] eth_type_trans+0x4ee/0x980 net/ethernet/eth.c:165 __xdp_build_skb_from_frame+0x5a8/0xa50 net/core/xdp.c:635 xdp_recv_frames net/bpf/test_run.c:272 [inline] xdp_test_run_batch net/bpf/test_run.c:361 [inline] bpf_test_run_xdp_live+0x2954/0x3330 net/bpf/test_run.c:390 bpf_prog_test_run_xdp+0x148e/0x1b10 net/bpf/test_run.c:1318 bpf_prog_test_run+0x5b7/0xa30 kernel/bpf/syscall.c:4371 __sys_bpf+0x6a6/0xe20 kernel/bpf/syscall.c:5777 __do_sys_bpf kernel/bpf/syscall.c:5866 [inline] __se_sys_bpf kernel/bpf/syscall.c:5864 [inline] __x64_sys_bpf+0xa4/0xf0 kernel/bpf/syscall.c:5864 x64_sys_call+0x2ea0/0x3d90 arch/x86/include/generated/asm/syscalls_64.h:322 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xd9/0x1d0 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f Uninit was created at: free_pages_prepare mm/page_alloc.c:1056 [inline] free_unref_page+0x156/0x1320 mm/page_alloc.c:2657 __free_pages+0xa3/0x1b0 mm/page_alloc.c:4838 bpf_ringbuf_free kernel/bpf/ringbuf.c:226 [inline] ringbuf_map_free+0xff/0x1e0 kernel/bpf/ringbuf.c:235 bpf_map_free kernel/bpf/syscall.c:838 [inline] bpf_map_free_deferred+0x17c/0x310 kernel/bpf/syscall.c:862 process_one_work kernel/workqueue.c:3229 [inline] process_scheduled_works+0xa2b/0x1b60 kernel/workqueue.c:3310 worker_thread+0xedf/0x1550 kernel/workqueue.c:3391 kthread+0x535/0x6b0 kernel/kthread.c:389 ret_from_fork+0x6e/0x90 arch/x86/kernel/process.c:147 ret_from_fork_asm+0x1a/0x30 arch/x86/entry/entry_64.S:244 CPU: 1 UID: 0 PID: 17276 Comm: syz.1.16450 Not tainted 6.12.0-05490-g9bb88c659673 #8 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.16.3-3.fc41 04/01/2014

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21868 In the Linux kernel, the following vulnerability has been resolved: net: allow small head cache usage with large MAX_SKB_FRAGS values Sabrina reported the following splat: WARNING: CPU: 0 PID: 1 at net/core/dev.c:6935 netif_napi_add_weight_locked+0x8f2/0xba0 Modules linked in: CPU: 0 UID: 0 PID: 1 Comm: swapper/0 Not tainted 6.14.0-rc1-net-00092-g011b03359038 #996 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS Arch Linux 1.16.3-1-1 04/01/2014 RIP: 0010:netif_napi_add_weight_locked+0x8f2/0xba0 Code: e8 c3 e6 6a fe 48 83 c4 28 5b 5d 41 5c 41 5d 41 5e 41 5f c3 cc cc cc cc c7 44 24 10 ff ff ff ff e9 8f fb ff ff e8 9e e6 6a fe <0f> 0b e9 d3 fe ff ff e8 92 e6 6a fe 48 8b 04 24 be ff ff ff ff 48 RSP: 0000:ffffc9000001fc60 EFLAGS: 00010293 RAX: 0000000000000000 RBX: ffff88806ce48128 RCX: 1ffff11001664b9e RDX: ffff888008f00040 RSI: ffffffff8317ca42 RDI: ffff88800b325cb6 RBP: ffff88800b325c40 R08: 0000000000000001 R09: ffffed100167502c R10: ffff88800b3a8163 R11: 0000000000000000 R12: ffff88800ac1c168 R13: ffff88800ac1c168 R14: ffff88800ac1c168 R15: 0000000000000007 FS: 0000000000000000(0000) GS:ffff88806ce00000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: ffff888008201000 CR3: 0000000004c94001 CR4: 0000000000370ef0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: <TASK> gro_cells_init+0x1ba/0x270 xfrm_input_init+0x4b/0x2a0 xfrm_init+0x38/0x50 ip_rt_init+0x2d7/0x350 ip_init+0xf/0x20 inet_init+0x406/0x590 do_one_initcall+0x9d/0x2e0 do_initcalls+0x23b/0x280 kernel_init_freeable+0x445/0x490 kernel_init+0x20/0x1d0 ret_from_fork+0x46/0x80 ret_from_fork_asm+0x1a/0x30 </TASK> irq event stamp: 584330 hardirqs last enabled at (584338): [<ffffffff8168bf87>] __up_console_sem+0x77/0xb0 hardirqs last disabled at (584345): [<ffffffff8168bf6c>] __up_console_sem+0x5c/0xb0 softirqs last enabled at (583242): [<ffffffff833ee96d>] netlink_insert+0x14d/0x470 softirqs last disabled at (583754): [<ffffffff8317c8cd>] netif_napi_add_weight_locked+0x77d/0xba0 on kernel built with MAX_SKB_FRAGS=45, where SKB_WITH_OVERHEAD(1024) is smaller than GRO_MAX_HEAD. Such built additionally contains the revert of the single page frag cache so that napi_get_frags() ends up using the page frag allocator, triggering the splat. Note that the underlying issue is independent from the mentioned revert; address it ensuring that the small head cache will fit either TCP and GRO allocation and updating napi_alloc_skb() and __netdev_alloc_skb() to select kmalloc() usage for any allocation fitting such cache.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21869 In the Linux kernel, the following vulnerability has been resolved: powerpc/code-patching: Disable KASAN report during patching via temporary mm Erhard reports the following KASAN hit on Talos II (power9) with kernel 6.13: [ 12.028126] ================================================================== [ 12.028198] BUG: KASAN: user-memory-access in copy_to_kernel_nofault+0x8c/0x1a0 [ 12.028260] Write of size 8 at addr 0000187e458f2000 by task systemd/1 [ 12.028346] CPU: 87 UID: 0 PID: 1 Comm: systemd Tainted: G T 6.13.0-P9-dirty #3 [ 12.028408] Tainted: [T]=RANDSTRUCT [ 12.028446] Hardware name: T2P9D01 REV 1.01 POWER9 0x4e1202 opal:skiboot-bc106a0 PowerNV [ 12.028500] Call Trace: [ 12.028536] [c000000008dbf3b0] [c000000001656a48] dump_stack_lvl+0xbc/0x110 (unreliable) [ 12.028609] [c000000008dbf3f0] [c0000000006e2fc8] print_report+0x6b0/0x708 [ 12.028666] [c000000008dbf4e0] [c0000000006e2454] kasan_report+0x164/0x300 [ 12.028725] [c000000008dbf600] [c0000000006e54d4] kasan_check_range+0x314/0x370 [ 12.028784] [c000000008dbf640] [c0000000006e6310] __kasan_check_write+0x20/0x40 [ 12.028842] [c000000008dbf660] [c000000000578e8c] copy_to_kernel_nofault+0x8c/0x1a0 [ 12.028902] [c000000008dbf6a0] [c0000000000acfe4] __patch_instructions+0x194/0x210 [ 12.028965] [c000000008dbf6e0] [c0000000000ade80] patch_instructions+0x150/0x590 [ 12.029026] [c000000008dbf7c0] [c0000000001159bc] bpf_arch_text_copy+0x6c/0xe0 [ 12.029085] [c000000008dbf800] [c000000000424250] bpf_jit_binary_pack_finalize+0x40/0xc0 [ 12.029147] [c000000008dbf830] [c000000000115dec] bpf_int_jit_compile+0x3bc/0x930 [ 12.029206] [c000000008dbf990] [c000000000423720] bpf_prog_select_runtime+0x1f0/0x280 [ 12.029266] [c000000008dbfa00] [c000000000434b18] bpf_prog_load+0xbb8/0x1370 [ 12.029324] [c000000008dbfb70] [c000000000436ebc] __sys_bpf+0x5ac/0x2e00 [ 12.029379] [c000000008dbfd00] [c00000000043a228] sys_bpf+0x28/0x40 [ 12.029435] [c000000008dbfd20] [c000000000038eb4] system_call_exception+0x334/0x610 [ 12.029497] [c000000008dbfe50] [c00000000000c270] system_call_vectored_common+0xf0/0x280 [ 12.029561] --- interrupt: 3000 at 0x3fff82f5cfa8 [ 12.029608] NIP: 00003fff82f5cfa8 LR: 00003fff82f5cfa8 CTR: 0000000000000000 [ 12.029660] REGS: c000000008dbfe80 TRAP: 3000 Tainted: G T (6.13.0-P9-dirty) [ 12.029735] MSR: 900000000280f032 <SF,HV,VEC,VSX,EE,PR,FP,ME,IR,DR,RI> CR: 42004848 XER: 00000000 [ 12.029855] IRQMASK: 0 GPR00: 0000000000000169 00003fffdcf789a0 00003fff83067100 0000000000000005 GPR04: 00003fffdcf78a98 0000000000000090 0000000000000000 0000000000000008 GPR08: 0000000000000000 0000000000000000 0000000000000000 0000000000000000 GPR12: 0000000000000000 00003fff836ff7e0 c000000000010678 0000000000000000 GPR16: 0000000000000000 0000000000000000 00003fffdcf78f28 00003fffdcf78f90 GPR20: 0000000000000000 0000000000000000 0000000000000000 00003fffdcf78f80 GPR24: 00003fffdcf78f70 00003fffdcf78d10 00003fff835c7239 00003fffdcf78bd8 GPR28: 00003fffdcf78a98 0000000000000000 0000000000000000 000000011f547580 [ 12.030316] NIP [00003fff82f5cfa8] 0x3fff82f5cfa8 [ 12.030361] LR [00003fff82f5cfa8] 0x3fff82f5cfa8 [ 12.030405] --- interrupt: 3000 [ 12.030444] ================================================================== Commit c28c15b6d28a ("powerpc/code-patching: Use temporary mm for Radix MMU") is inspired from x86 but unlike x86 is doesn't disable KASAN reports during patching. This wasn't a problem at the begining because __patch_mem() is not instrumented. Commit 465cabc97b42 ("powerpc/code-patching: introduce patch_instructions()") use copy_to_kernel_nofault() to copy several instructions at once. But when using temporary mm the destination is not regular kernel memory but a kind of kernel-like memory located in user address space. ---truncated---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21871 In the Linux kernel, the following vulnerability has been resolved: tee: optee: Fix supplicant wait loop OP-TEE supplicant is a user-space daemon and it's possible for it be hung or crashed or killed in the middle of processing an OP-TEE RPC call. It becomes more complicated when there is incorrect shutdown ordering of the supplicant process vs the OP-TEE client application which can eventually lead to system hang-up waiting for the closure of the client application. Allow the client process waiting in kernel for supplicant response to be killed rather than indefinitely waiting in an unkillable state. Also, a normal uninterruptible wait should not have resulted in the hung-task watchdog getting triggered, but the endless loop would. This fixes issues observed during system reboot/shutdown when supplicant got hung for some reason or gets crashed/killed which lead to client getting hung in an unkillable state. It in turn lead to system being in hung up state requiring hard power off/on to recover.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21887 In the Linux kernel, the following vulnerability has been resolved: ovl: fix UAF in ovl_dentry_update_reval by moving dput() in ovl_link_up The issue was caused by dput(upper) being called before ovl_dentry_update_reval(), while upper->d_flags was still accessed in ovl_dentry_remote(). Move dput(upper) after its last use to prevent use-after-free. BUG: KASAN: slab-use-after-free in ovl_dentry_remote fs/overlayfs/util.c:162 [inline] BUG: KASAN: slab-use-after-free in ovl_dentry_update_reval+0xd2/0xf0 fs/overlayfs/util.c:167 Call Trace: <TASK> __dump_stack lib/dump_stack.c:88 [inline] dump_stack_lvl+0x116/0x1f0 lib/dump_stack.c:114 print_address_description mm/kasan/report.c:377 [inline] print_report+0xc3/0x620 mm/kasan/report.c:488 kasan_report+0xd9/0x110 mm/kasan/report.c:601 ovl_dentry_remote fs/overlayfs/util.c:162 [inline] ovl_dentry_update_reval+0xd2/0xf0 fs/overlayfs/util.c:167 ovl_link_up fs/overlayfs/copy_up.c:610 [inline] ovl_copy_up_one+0x2105/0x3490 fs/overlayfs/copy_up.c:1170 ovl_copy_up_flags+0x18d/0x200 fs/overlayfs/copy_up.c:1223 ovl_rename+0x39e/0x18c0 fs/overlayfs/dir.c:1136 vfs_rename+0xf84/0x20a0 fs/namei.c:4893 ... </TASK>

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-21943 In the Linux kernel, the following vulnerability has been resolved: gpio: aggregator: protect driver attr handlers against module unload Both new_device_store and delete_device_store touch module global resources (e.g. gpio_aggregator_lock). To prevent race conditions with module unload, a reference needs to be held. Add try_module_get() in these handlers. For new_device_store, this eliminates what appears to be the most dangerous scenario: if an id is allocated from gpio_aggregator_idr but platform_device_register has not yet been called or completed, a concurrent module unload could fail to unregister/delete the device, leaving behind a dangling platform device/GPIO forwarder. This can result in various issues. The following simple reproducer demonstrates these problems: #!/bin/bash while :; do # note: whether 'gpiochip0 0' exists or not does not matter. echo 'gpiochip0 0' > /sys/bus/platform/drivers/gpio-aggregator/new_device done & while :; do modprobe gpio-aggregator modprobe -r gpio-aggregator done & wait Starting with the following warning, several kinds of warnings will appear and the system may become unstable: ------------[ cut here ]------------ list_del corruption, ffff888103e2e980->next is LIST_POISON1 (dead000000000100) WARNING: CPU: 1 PID: 1327 at lib/list_debug.c:56 __list_del_entry_valid_or_report+0xa3/0x120 [...] RIP: 0010:__list_del_entry_valid_or_report+0xa3/0x120 [...] Call Trace: <TASK> ? __list_del_entry_valid_or_report+0xa3/0x120 ? __warn.cold+0x93/0xf2 ? __list_del_entry_valid_or_report+0xa3/0x120 ? report_bug+0xe6/0x170 ? __irq_work_queue_local+0x39/0xe0 ? handle_bug+0x58/0x90 ? exc_invalid_op+0x13/0x60 ? asm_exc_invalid_op+0x16/0x20 ? __list_del_entry_valid_or_report+0xa3/0x120 gpiod_remove_lookup_table+0x22/0x60 new_device_store+0x315/0x350 [gpio_aggregator] kernfs_fop_write_iter+0x137/0x1f0 vfs_write+0x262/0x430 ksys_write+0x60/0xd0 do_syscall_64+0x6c/0x180 entry_SYSCALL_64_after_hwframe+0x76/0x7e [...] </TASK> ---[ end trace 0000000000000000 ]---

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-22088 In the Linux kernel, the following vulnerability has been resolved: RDMA/erdma: Prevent use-after-free in erdma_accept_newconn() After the erdma_cep_put(new_cep) being called, new_cep will be freed, and the following dereference will cause a UAF problem. Fix this issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-24357 vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-24528 In MIT Kerberos 5 (aka krb5) before 1.22 (with incremental propagation), there is an integer overflow for a large update size to resize() in kdb_log.c. An authenticated attacker can cause an out-of-bounds write and kadmind daemon crash.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-25183 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-27516 Jinja is an extensible templating engine. Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-28162 Buffer Overflow vulnerability in libpng 1.6.43-1.6.46 allows a local attacker to cause a denial of service via the pngimage with AddressSanitizer (ASan), the program leaks memory in various locations, eventually leading to high memory usage and causing the program to become unresponsive

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2025-29087 In SQLite 3.44.0 through 3.49.0 before 3.49.1, the concat_ws() SQL function can cause memory to be written beyond the end of a malloc-allocated buffer. If the separator argument is attacker-controlled and has a large string (e.g., 2MB or more), an integer overflow occurs in calculating the size of the result buffer, and thus malloc may not allocate enough memory.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-29088 In SQLite 3.49.0 before 3.49.1, certain argument values to sqlite3_db_config (in the C-language API) can cause a denial of service (application crash). An sz*nBig multiplication is not cast to a 64-bit integer, and consequently some memory allocations may be incorrect.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-29770 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. The outlines library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server. The affected code in vLLM is vllm/model_executor/guided_decoding/outlines_logits_processors.py, which unconditionally uses the cache from outlines. A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space. Note that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the guided_decoding_backend key of the extra_body field of the request. This issue applies only to the V0 engine and is fixed in 0.8.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-29783 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. When vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP on all network interfaces will allow attackers to execute remote code on distributed hosts. This is a remote code execution vulnerability impacting any deployments using Mooncake to distribute KV across distributed hosts. This vulnerability is fixed in 0.8.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-30165 vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-30202 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-30204 golang-jwt is a Go implementation of JSON Web Tokens. Starting in version 3.2.0 and prior to versions 5.2.2 and 4.5.2, the function parse.ParseUnverified splits (via a call to strings.Split) its argument (which is untrusted data) on periods. As a result, in the face of a malicious request whose Authorization header consists of Bearer followed by many period characters, a call to that function incurs allocations to the tune of O(n) bytes (where n stands for the length of the function's argument), with a constant factor of about 16. This issue is fixed in 5.2.2 and 4.5.2.

nim-deepseek-r1-v1.7.3
traefik

CVE-2025-30749 Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: 2D). Supported versions that are affected are Oracle Java SE: 8u451, 8u451-perf, 11.0.27, 17.0.15, 21.0.7, 24.0.1; Oracle GraalVM for JDK: 17.0.15, 21.0.7 and 24.0.1; Oracle GraalVM Enterprise Edition: 21.3.14. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in takeover of Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Note: This vulnerability applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. This vulnerability does not apply to Java deployments, typically in servers, that load and run only trusted code (e.g., code installed by an administrator). CVSS 3.1 Base Score 8.1 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-30754 Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: JSSE). Supported versions that are affected are Oracle Java SE: 8u451, 8u451-perf, 11.0.27, 17.0.15, 21.0.7, 24.0.1; Oracle GraalVM for JDK: 17.0.15, 21.0.7 and 24.0.1; Oracle GraalVM Enterprise Edition: 21.3.14. Difficult to exploit vulnerability allows unauthenticated attacker with network access via TLS to compromise Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized update, insert or delete access to some of Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition accessible data as well as unauthorized read access to a subset of Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. This vulnerability does not apply to Java deployments, typically in servers, that load and run only trusted code (e.g., code installed by an administrator). CVSS 3.1 Base Score 4.8 (Confidentiality and Integrity impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-30761 Vulnerability in the Oracle Java SE, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: Scripting). Supported versions that are affected are Oracle Java SE: 8u451, 8u451-perf and 11.0.27; Oracle GraalVM Enterprise Edition: 21.3.14. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized creation, deletion or modification access to critical data or all Oracle Java SE, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 5.9 (Integrity impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-32381 XGrammar is an open-source library for efficient, flexible, and portable structured generation. Prior to 0.1.18, Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. This vulnerability is fixed in 0.1.18.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-32434 PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5

CVE-2025-32444 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-32728 In sshd in OpenSSH before 10.0, the DisableForwarding directive does not adhere to the documentation stating that it disables X11 and agent forwarding.

nim-deepseek-r1-v1.7.3

CVE-2025-33245 NVIDIA NeMo Framework contains a vulnerability where malicious data could cause remote code execution. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-33253 NVIDIA NeMo Framework contains a vulnerability where an attacker could cause remote code execution by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-37750 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix UAF in decryption with multichannel After commit f7025d861694 ("smb: client: allocate crypto only for primary server") and commit b0abcd65ec54 ("smb: client: fix UAF in async decryption"), the channels started reusing AEAD TFM from primary channel to perform synchronous decryption, but that can't done as there could be multiple cifsd threads (one per channel) simultaneously accessing it to perform decryption. This fixes the following KASAN splat when running fstest generic/249 with 'vers=3.1.1,multichannel,max_channels=4,seal' against Windows Server 2022: BUG: KASAN: slab-use-after-free in gf128mul_4k_lle+0xba/0x110 Read of size 8 at addr ffff8881046c18a0 by task cifsd/986 CPU: 3 UID: 0 PID: 986 Comm: cifsd Not tainted 6.15.0-rc1 #1 PREEMPT(voluntary) Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.16.3-3.fc41 04/01/2014 Call Trace: <TASK> dump_stack_lvl+0x5d/0x80 print_report+0x156/0x528 ? gf128mul_4k_lle+0xba/0x110 ? __virt_addr_valid+0x145/0x300 ? __phys_addr+0x46/0x90 ? gf128mul_4k_lle+0xba/0x110 kasan_report+0xdf/0x1a0 ? gf128mul_4k_lle+0xba/0x110 gf128mul_4k_lle+0xba/0x110 ghash_update+0x189/0x210 shash_ahash_update+0x295/0x370 ? __pfx_shash_ahash_update+0x10/0x10 ? __pfx_shash_ahash_update+0x10/0x10 ? __pfx_extract_iter_to_sg+0x10/0x10 ? ___kmalloc_large_node+0x10e/0x180 ? __asan_memset+0x23/0x50 crypto_ahash_update+0x3c/0xc0 gcm_hash_assoc_remain_continue+0x93/0xc0 crypt_message+0xe09/0xec0 [cifs] ? __pfx_crypt_message+0x10/0x10 [cifs] ? _raw_spin_unlock+0x23/0x40 ? __pfx_cifs_readv_from_socket+0x10/0x10 [cifs] decrypt_raw_data+0x229/0x380 [cifs] ? __pfx_decrypt_raw_data+0x10/0x10 [cifs] ? __pfx_cifs_read_iter_from_socket+0x10/0x10 [cifs] smb3_receive_transform+0x837/0xc80 [cifs] ? __pfx_smb3_receive_transform+0x10/0x10 [cifs] ? __pfx___might_resched+0x10/0x10 ? __pfx_smb3_is_transform_hdr+0x10/0x10 [cifs] cifs_demultiplex_thread+0x692/0x1570 [cifs] ? __pfx_cifs_demultiplex_thread+0x10/0x10 [cifs] ? rcu_is_watching+0x20/0x50 ? rcu_lockdep_current_cpu_online+0x62/0xb0 ? find_held_lock+0x32/0x90 ? kvm_sched_clock_read+0x11/0x20 ? local_clock_noinstr+0xd/0xd0 ? trace_irq_enable.constprop.0+0xa8/0xe0 ? __pfx_cifs_demultiplex_thread+0x10/0x10 [cifs] kthread+0x1fe/0x380 ? kthread+0x10f/0x380 ? __pfx_kthread+0x10/0x10 ? local_clock_noinstr+0xd/0xd0 ? ret_from_fork+0x1b/0x60 ? local_clock+0x15/0x30 ? lock_release+0x29b/0x390 ? rcu_is_watching+0x20/0x50 ? __pfx_kthread+0x10/0x10 ret_from_fork+0x31/0x60 ? __pfx_kthread+0x10/0x10 ret_from_fork_asm+0x1a/0x30 </TASK>

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-37752 In the Linux kernel, the following vulnerability has been resolved: net_sched: sch_sfq: move the limit validation It is not sufficient to directly validate the limit on the data that the user passes as it can be updated based on how the other parameters are changed. Move the check at the end of the configuration update process to also catch scenarios where the limit is indirectly updated, for example with the following configurations: tc qdisc add dev dummy0 handle 1: root sfq limit 2 flows 1 depth 1 tc qdisc add dev dummy0 handle 1: root sfq limit 2 flows 1 divisor 1 This fixes the following syzkaller reported crash: ------------[ cut here ]------------ UBSAN: array-index-out-of-bounds in net/sched/sch_sfq.c:203:6 index 65535 is out of range for type 'struct sfq_head[128]' CPU: 1 UID: 0 PID: 3037 Comm: syz.2.16 Not tainted 6.14.0-rc2-syzkaller #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 12/27/2024 Call Trace: <TASK> __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0x201/0x300 lib/dump_stack.c:120 ubsan_epilogue lib/ubsan.c:231 [inline] __ubsan_handle_out_of_bounds+0xf5/0x120 lib/ubsan.c:429 sfq_link net/sched/sch_sfq.c:203 [inline] sfq_dec+0x53c/0x610 net/sched/sch_sfq.c:231 sfq_dequeue+0x34e/0x8c0 net/sched/sch_sfq.c:493 sfq_reset+0x17/0x60 net/sched/sch_sfq.c:518 qdisc_reset+0x12e/0x600 net/sched/sch_generic.c:1035 tbf_reset+0x41/0x110 net/sched/sch_tbf.c:339 qdisc_reset+0x12e/0x600 net/sched/sch_generic.c:1035 dev_reset_queue+0x100/0x1b0 net/sched/sch_generic.c:1311 netdev_for_each_tx_queue include/linux/netdevice.h:2590 [inline] dev_deactivate_many+0x7e5/0xe70 net/sched/sch_generic.c:1375

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-37797 In the Linux kernel, the following vulnerability has been resolved: net_sched: hfsc: Fix a UAF vulnerability in class handling This patch fixes a Use-After-Free vulnerability in the HFSC qdisc class handling. The issue occurs due to a time-of-check/time-of-use condition in hfsc_change_class() when working with certain child qdiscs like netem or codel. The vulnerability works as follows: 1. hfsc_change_class() checks if a class has packets (q.qlen != 0) 2. It then calls qdisc_peek_len(), which for certain qdiscs (e.g., codel, netem) might drop packets and empty the queue 3. The code continues assuming the queue is still non-empty, adding the class to vttree 4. This breaks HFSC scheduler assumptions that only non-empty classes are in vttree 5. Later, when the class is destroyed, this can lead to a Use-After-Free The fix adds a second queue length check after qdisc_peek_len() to verify the queue wasn't emptied.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-37890 In the Linux kernel, the following vulnerability has been resolved: net_sched: hfsc: Fix a UAF vulnerability in class with netem as child qdisc As described in Gerrard's report [1], we have a UAF case when an hfsc class has a netem child qdisc. The crux of the issue is that hfsc is assuming that checking for cl->qdisc->q.qlen == 0 guarantees that it hasn't inserted the class in the vttree or eltree (which is not true for the netem duplicate case). This patch checks the n_active class variable to make sure that the code won't insert the class in the vttree or eltree twice, catering for the reentrant case. [1] https://lore.kernel.org/netdev/CAHcdcOm+03OD2j6R0=YHKqmy=VgJ8xEOKuP6c7mSgnp-TEJJbw@mail.gmail.com/

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-38069 In the Linux kernel, the following vulnerability has been resolved: PCI: endpoint: pci-epf-test: Fix double free that causes kernel to oops Fix a kernel oops found while testing the stm32_pcie Endpoint driver with handling of PERST# deassertion: During EP initialization, pci_epf_test_alloc_space() allocates all BARs, which are further freed if epc_set_bar() fails (for instance, due to no free inbound window). However, when pci_epc_set_bar() fails, the error path: pci_epc_set_bar() -> pci_epf_free_space() does not clear the previous assignment to epf_test->reg[bar]. Then, if the host reboots, the PERST# deassertion restarts the BAR allocation sequence with the same allocation failure (no free inbound window), creating a double free situation since epf_test->reg[bar] was deallocated and is still non-NULL. Thus, make sure that pci_epf_alloc_space() and pci_epf_free_space() invocations are symmetric, and as such, set epf_test->reg[bar] to NULL when memory is freed. [kwilczynski: commit log]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38082 In the Linux kernel, the following vulnerability has been resolved: gpio: virtuser: fix potential out-of-bound write If the caller wrote more characters, count is truncated to the max available space in "simple_write_to_buffer". Check that the input size does not exceed the buffer size. Write a zero termination afterwards.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38091 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: check stream id dml21 wrapper to get plane_id [Why & How] Fix a false positive warning which occurs due to lack of correct checks when querying plane_id in DML21. This fixes the warning when performing a mode1 reset (cat /sys/kernel/debug/dri/1/amdgpu_gpu_recover): [ 35.751250] WARNING: CPU: 11 PID: 326 at /tmp/amd.PHpyAl7v/amd/amdgpu/../display/dc/dml2/dml2_dc_resource_mgmt.c:91 dml2_map_dc_pipes+0x243d/0x3f40 [amdgpu] [ 35.751434] Modules linked in: amdgpu(OE) amddrm_ttm_helper(OE) amdttm(OE) amddrm_buddy(OE) amdxcp(OE) amddrm_exec(OE) amd_sched(OE) amdkcl(OE) drm_suballoc_helper drm_ttm_helper ttm drm_display_helper cec rc_core i2c_algo_bit rfcomm qrtr cmac algif_hash algif_skcipher af_alg bnep amd_atl intel_rapl_msr intel_rapl_common snd_hda_codec_hdmi snd_hda_intel edac_mce_amd snd_intel_dspcfg snd_intel_sdw_acpi snd_hda_codec kvm_amd snd_hda_core snd_hwdep snd_pcm kvm snd_seq_midi snd_seq_midi_event snd_rawmidi crct10dif_pclmul polyval_clmulni polyval_generic btusb ghash_clmulni_intel sha256_ssse3 btrtl sha1_ssse3 snd_seq btintel aesni_intel btbcm btmtk snd_seq_device crypto_simd sunrpc cryptd bluetooth snd_timer ccp binfmt_misc rapl snd i2c_piix4 wmi_bmof gigabyte_wmi k10temp i2c_smbus soundcore gpio_amdpt mac_hid sch_fq_codel msr parport_pc ppdev lp parport efi_pstore nfnetlink dmi_sysfs ip_tables x_tables autofs4 hid_generic usbhid hid crc32_pclmul igc ahci xhci_pci libahci xhci_pci_renesas video wmi [ 35.751501] CPU: 11 UID: 0 PID: 326 Comm: kworker/u64:9 Tainted: G OE 6.11.0-21-generic #21~24.04.1-Ubuntu [ 35.751504] Tainted: [O]=OOT_MODULE, [E]=UNSIGNED_MODULE [ 35.751505] Hardware name: Gigabyte Technology Co., Ltd. X670E AORUS PRO X/X670E AORUS PRO X, BIOS F30 05/22/2024 [ 35.751506] Workqueue: amdgpu-reset-dev amdgpu_debugfs_reset_work [amdgpu] [ 35.751638] RIP: 0010:dml2_map_dc_pipes+0x243d/0x3f40 [amdgpu] [ 35.751794] Code: 6d 0c 00 00 8b 84 24 88 00 00 00 41 3b 44 9c 20 0f 84 fc 07 00 00 48 83 c3 01 48 83 fb 06 75 b3 4c 8b 64 24 68 4c 8b 6c 24 40 <0f> 0b b8 06 00 00 00 49 8b 94 24 a0 49 00 00 89 c3 83 f8 07 0f 87 [ 35.751796] RSP: 0018:ffffbfa3805d7680 EFLAGS: 00010246 [ 35.751798] RAX: 0000000000010000 RBX: 0000000000000006 RCX: 0000000000000000 [ 35.751799] RDX: 0000000000000000 RSI: 0000000000000005 RDI: 0000000000000000 [ 35.751800] RBP: ffffbfa3805d78f0 R08: 0000000000000000 R09: 0000000000000000 [ 35.751801] R10: 0000000000000000 R11: 0000000000000000 R12: ffffbfa383249000 [ 35.751802] R13: ffffa0e68f280000 R14: ffffbfa383249658 R15: 0000000000000000 [ 35.751803] FS: 0000000000000000(0000) GS:ffffa0edbe580000(0000) knlGS:0000000000000000 [ 35.751804] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 35.751805] CR2: 00005d847ef96c58 CR3: 000000041de3e000 CR4: 0000000000f50ef0 [ 35.751806] PKRU: 55555554 [ 35.751807] Call Trace: [ 35.751810] <TASK> [ 35.751816] ? show_regs+0x6c/0x80 [ 35.751820] ? __warn+0x88/0x140 [ 35.751822] ? dml2_map_dc_pipes+0x243d/0x3f40 [amdgpu] [ 35.751964] ? report_bug+0x182/0x1b0 [ 35.751969] ? handle_bug+0x6e/0xb0 [ 35.751972] ? exc_invalid_op+0x18/0x80 [ 35.751974] ? asm_exc_invalid_op+0x1b/0x20 [ 35.751978] ? dml2_map_dc_pipes+0x243d/0x3f40 [amdgpu] [ 35.752117] ? math_pow+0x48/0xa0 [amdgpu] [ 35.752256] ? srso_alias_return_thunk+0x5/0xfbef5 [ 35.752260] ? math_pow+0x48/0xa0 [amdgpu] [ 35.752400] ? srso_alias_return_thunk+0x5/0xfbef5 [ 35.752403] ? math_pow+0x11/0xa0 [amdgpu] [ 35.752524] ? srso_alias_return_thunk+0x5/0xfbef5 [ 35.752526] ? core_dcn4_mode_programming+0xe4d/0x20d0 [amdgpu] [ 35.752663] ? srso_alias_return_thunk+0x5/0xfbef5 [ 35.752669] dml21_validate+0x3d4/0x980 [amdgpu] (cherry picked from commit f8ad62c0a93e5dd94243e10f1b742232e4d6411e)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38092 In the Linux kernel, the following vulnerability has been resolved: ksmbd: use list_first_entry_or_null for opinfo_get_list() The list_first_entry() macro never returns NULL. If the list is empty then it returns an invalid pointer. Use list_first_entry_or_null() to check if the list is empty.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38187 In the Linux kernel, the following vulnerability has been resolved: drm/nouveau: fix a use-after-free in r535_gsp_rpc_push() The RPC container is released after being passed to r535_gsp_rpc_send(). When sending the initial fragment of a large RPC and passing the caller's RPC container, the container will be freed prematurely. Subsequent attempts to send remaining fragments will therefore result in a use-after-free. Allocate a temporary RPC container for holding the initial fragment of a large RPC when sending. Free the caller's container when all fragments are successfully sent. [ Rebase onto Blackwell changes. - Danilo ]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2025-38350 In the Linux kernel, the following vulnerability has been resolved: net/sched: Always pass notifications when child class becomes empty Certain classful qdiscs may invoke their classes' dequeue handler on an enqueue operation. This may unexpectedly empty the child qdisc and thus make an in-flight class passive via qlen_notify(). Most qdiscs do not expect such behaviour at this point in time and may re-activate the class eventually anyways which will lead to a use-after-free. The referenced fix commit attempted to fix this behavior for the HFSC case by moving the backlog accounting around, though this turned out to be incomplete since the parent's parent may run into the issue too. The following reproducer demonstrates this use-after-free: tc qdisc add dev lo root handle 1: drr tc filter add dev lo parent 1: basic classid 1:1 tc class add dev lo parent 1: classid 1:1 drr tc qdisc add dev lo parent 1:1 handle 2: hfsc def 1 tc class add dev lo parent 2: classid 2:1 hfsc rt m1 8 d 1 m2 0 tc qdisc add dev lo parent 2:1 handle 3: netem tc qdisc add dev lo parent 3:1 handle 4: blackhole echo 1 | socat -u STDIN UDP4-DATAGRAM:127.0.0.1:8888 tc class delete dev lo classid 1:1 echo 1 | socat -u STDIN UDP4-DATAGRAM:127.0.0.1:8888 Since backlog accounting issues leading to a use-after-frees on stale class pointers is a recurring pattern at this point, this patch takes a different approach. Instead of trying to fix the accounting, the patch ensures that qdisc_tree_reduce_backlog always calls qlen_notify when the child qdisc is empty. This solves the problem because deletion of qdiscs always involves a call to qdisc_reset() and / or qdisc_purge_queue() which ultimately resets its qlen to 0 thus causing the following qdisc_tree_reduce_backlog() to report to the parent. Note that this may call qlen_notify on passive classes multiple times. This is not a problem after the recent patch series that made all the classful qdiscs qlen_notify() handlers idempotent.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-38440 In the Linux kernel, the following vulnerability has been resolved: net/mlx5e: Fix race between DIM disable and net_dim() There's a race between disabling DIM and NAPI callbacks using the dim pointer on the RQ or SQ. If NAPI checks the DIM state bit and sees it still set, it assumes `rq->dim` or `sq->dim` is valid. But if DIM gets disabled right after that check, the pointer might already be set to NULL, leading to a NULL pointer dereference in net_dim(). Fix this by calling `synchronize_net()` before freeing the DIM context. This ensures all in-progress NAPI callbacks are finished before the pointer is cleared. Kernel log: BUG: kernel NULL pointer dereference, address: 0000000000000000 ... RIP: 0010:net_dim+0x23/0x190 ... Call Trace: <TASK> ? __die+0x20/0x60 ? page_fault_oops+0x150/0x3e0 ? common_interrupt+0xf/0xa0 ? sysvec_call_function_single+0xb/0x90 ? exc_page_fault+0x74/0x130 ? asm_exc_page_fault+0x22/0x30 ? net_dim+0x23/0x190 ? mlx5e_poll_ico_cq+0x41/0x6f0 [mlx5_core] ? sysvec_apic_timer_interrupt+0xb/0x90 mlx5e_handle_rx_dim+0x92/0xd0 [mlx5_core] mlx5e_napi_poll+0x2cd/0xac0 [mlx5_core] ? mlx5e_poll_ico_cq+0xe5/0x6f0 [mlx5_core] busy_poll_stop+0xa2/0x200 ? mlx5e_napi_poll+0x1d9/0xac0 [mlx5_core] ? mlx5e_trigger_irq+0x130/0x130 [mlx5_core] __napi_busy_loop+0x345/0x3b0 ? sysvec_call_function_single+0xb/0x90 ? asm_sysvec_call_function_single+0x16/0x20 ? sysvec_apic_timer_interrupt+0xb/0x90 ? pcpu_free_area+0x1e4/0x2e0 napi_busy_loop+0x11/0x20 xsk_recvmsg+0x10c/0x130 sock_recvmsg+0x44/0x70 __sys_recvfrom+0xbc/0x130 ? __schedule+0x398/0x890 __x64_sys_recvfrom+0x20/0x30 do_syscall_64+0x4c/0x100 entry_SYSCALL_64_after_hwframe+0x4b/0x53 ... ---[ end trace 0000000000000000 ]--- ... ---[ end Kernel panic - not syncing: Fatal exception in interrupt ]---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38486 In the Linux kernel, the following vulnerability has been resolved: soundwire: Revert "soundwire: qcom: Add set_channel_map api support" This reverts commit 7796c97df6b1b2206681a07f3c80f6023a6593d5. This patch broke Dragonboard 845c (sdm845). I see: Unexpected kernel BRK exception at EL1 Internal error: BRK handler: 00000000f20003e8 [#1] SMP pc : qcom_swrm_set_channel_map+0x7c/0x80 [soundwire_qcom] lr : snd_soc_dai_set_channel_map+0x34/0x78 Call trace: qcom_swrm_set_channel_map+0x7c/0x80 [soundwire_qcom] (P) sdm845_dai_init+0x18c/0x2e0 [snd_soc_sdm845] snd_soc_link_init+0x28/0x6c snd_soc_bind_card+0x5f4/0xb0c snd_soc_register_card+0x148/0x1a4 devm_snd_soc_register_card+0x50/0xb0 sdm845_snd_platform_probe+0x124/0x148 [snd_soc_sdm845] platform_probe+0x6c/0xd0 really_probe+0xc0/0x2a4 __driver_probe_device+0x7c/0x130 driver_probe_device+0x40/0x118 __device_attach_driver+0xc4/0x108 bus_for_each_drv+0x8c/0xf0 __device_attach+0xa4/0x198 device_initial_probe+0x18/0x28 bus_probe_device+0xb8/0xbc deferred_probe_work_func+0xac/0xfc process_one_work+0x244/0x658 worker_thread+0x1b4/0x360 kthread+0x148/0x228 ret_from_fork+0x10/0x20 Kernel panic - not syncing: BRK handler: Fatal exception Dan has also reported following issues with the original patch https://lore.kernel.org/all/33fe8fe7-719a-405a-9ed2-d9f816ce1d57@sabinyo.mountain/ Bug #1: The zeroeth element of ctrl->pconfig[] is supposed to be unused. We start counting at 1. However this code sets ctrl->pconfig[0].ch_mask = 128. Bug #2: There are SLIM_MAX_TX_PORTS (16) elements in tx_ch[] array but only QCOM_SDW_MAX_PORTS + 1 (15) in the ctrl->pconfig[] array so it corrupts memory like Yongqin Liu pointed out. Bug 3: Like Jie Gan pointed out, it erases all the tx information with the rx information.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38553 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

dex-runtime-python-builder-7.1.9.1078-compat
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-38669 In the Linux kernel, the following vulnerability has been resolved: Revert "drm/gem-shmem: Use dma_buf from GEM object instance" This reverts commit 1a148af06000e545e714fe3210af3d77ff903c11. The dma_buf field in struct drm_gem_object is not stable over the object instance's lifetime. The field becomes NULL when user space releases the final GEM handle on the buffer object. This resulted in a NULL-pointer deref. Workarounds in commit 5307dce878d4 ("drm/gem: Acquire references on GEM handles for framebuffers") and commit f6bfc9afc751 ("drm/framebuffer: Acquire internal references on GEM handles") only solved the problem partially. They especially don't work for buffer objects without a DRM framebuffer associated. Hence, this revert to going back to using .import_attach->dmabuf. v3: - cc stable

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38672 In the Linux kernel, the following vulnerability has been resolved: Revert "drm/gem-dma: Use dma_buf from GEM object instance" This reverts commit e8afa1557f4f963c9a511bd2c6074a941c308685. The dma_buf field in struct drm_gem_object is not stable over the object instance's lifetime. The field becomes NULL when user space releases the final GEM handle on the buffer object. This resulted in a NULL-pointer deref. Workarounds in commit 5307dce878d4 ("drm/gem: Acquire references on GEM handles for framebuffers") and commit f6bfc9afc751 ("drm/framebuffer: Acquire internal references on GEM handles") only solved the problem partially. They especially don't work for buffer objects without a DRM framebuffer associated. Hence, this revert to going back to using .import_attach->dmabuf. v3: - cc stable

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38673 In the Linux kernel, the following vulnerability has been resolved: Revert "drm/gem-framebuffer: Use dma_buf from GEM object instance" This reverts commit cce16fcd7446dcff7480cd9d2b6417075ed81065. The dma_buf field in struct drm_gem_object is not stable over the object instance's lifetime. The field becomes NULL when user space releases the final GEM handle on the buffer object. This resulted in a NULL-pointer deref. Workarounds in commit 5307dce878d4 ("drm/gem: Acquire references on GEM handles for framebuffers") and commit f6bfc9afc751 ("drm/framebuffer: Acquire internal references on GEM handles") only solved the problem partially. They especially don't work for buffer objects without a DRM framebuffer associated. Hence, this revert to going back to using .import_attach->dmabuf. v3: - cc stable

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38674 In the Linux kernel, the following vulnerability has been resolved: Revert "drm/prime: Use dma_buf from GEM object instance" This reverts commit f83a9b8c7fd0557b0c50784bfdc1bbe9140c9bf8. The dma_buf field in struct drm_gem_object is not stable over the object instance's lifetime. The field becomes NULL when user space releases the final GEM handle on the buffer object. This resulted in a NULL-pointer deref. Workarounds in commit 5307dce878d4 ("drm/gem: Acquire references on GEM handles for framebuffers") and commit f6bfc9afc751 ("drm/framebuffer: Acquire internal references on GEM handles") only solved the problem partially. They especially don't work for buffer objects without a DRM framebuffer associated. Hence, this revert to going back to using .import_attach->dmabuf. v3: - cc stable

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-38689 In the Linux kernel, the following vulnerability has been resolved: x86/fpu: Fix NULL dereference in avx512_status() Problem ------- With CONFIG_X86_DEBUG_FPU enabled, reading /proc/[kthread]/arch_status causes a warning and a NULL pointer dereference. This is because the AVX-512 timestamp code uses x86_task_fpu() but doesn't check it for NULL. CONFIG_X86_DEBUG_FPU addles that function for kernel threads (PF_KTHREAD specifically), making it return NULL. The point of the warning was to ensure that kernel threads only access task->fpu after going through kernel_fpu_begin()/_end(). Note: all kernel tasks exposed in /proc have a valid task->fpu. Solution -------- One option is to silence the warning and check for NULL from x86_task_fpu(). However, that warning is fairly fresh and seems like a defense against misuse of the FPU state in kernel threads. Instead, stop outputting AVX-512_elapsed_ms for kernel threads altogether. The data was garbage anyway because avx512_timestamp is only updated for user threads, not kernel threads. If anyone ever wants to track kernel thread AVX-512 use, they can come back later and do it properly, separate from this bug fix. [ dhansen: mostly rewrite changelog ]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-39908 In the Linux kernel, the following vulnerability has been resolved: net: dev_ioctl: take ops lock in hwtstamp lower paths ndo hwtstamp callbacks are expected to run under the per-device ops lock. Make the lower get/set paths consistent with the rest of ndo invocations. Kernel log: WARNING: CPU: 13 PID: 51364 at ./include/net/netdev_lock.h:70 __netdev_update_features+0x4bd/0xe60 ... RIP: 0010:__netdev_update_features+0x4bd/0xe60 ... Call Trace: <TASK> netdev_update_features+0x1f/0x60 mlx5_hwtstamp_set+0x181/0x290 [mlx5_core] mlx5e_hwtstamp_set+0x19/0x30 [mlx5_core] dev_set_hwtstamp_phylib+0x9f/0x220 dev_set_hwtstamp_phylib+0x9f/0x220 dev_set_hwtstamp+0x13d/0x240 dev_ioctl+0x12f/0x4b0 sock_ioctl+0x171/0x370 __x64_sys_ioctl+0x3f7/0x900 ? __sys_setsockopt+0x69/0xb0 do_syscall_64+0x6f/0x2e0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 ... </TASK> .... ---[ end trace 0000000000000000 ]--- Note that the mlx5_hwtstamp_set and mlx5e_hwtstamp_set functions shown in the trace come from an in progress patch converting the legacy ioctl to ndo_hwtstamp_get/set and are not present in mainline.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-40012 In the Linux kernel, the following vulnerability has been resolved: net/smc: fix warning in smc_rx_splice() when calling get_page() smc_lo_register_dmb() allocates DMB buffers with kzalloc(), which are later passed to get_page() in smc_rx_splice(). Since kmalloc memory is not page-backed, this triggers WARN_ON_ONCE() in get_page() and prevents holding a refcount on the buffer. This can lead to use-after-free if the memory is released before splice_to_pipe() completes. Use folio_alloc() instead, ensuring DMBs are page-backed and safe for get_page(). WARNING: CPU: 18 PID: 12152 at ./include/linux/mm.h:1330 smc_rx_splice+0xaf8/0xe20 [smc] CPU: 18 UID: 0 PID: 12152 Comm: smcapp Kdump: loaded Not tainted 6.17.0-rc3-11705-g9cf4672ecfee #10 NONE Hardware name: IBM 3931 A01 704 (z/VM 7.4.0) Krnl PSW : 0704e00180000000 000793161032696c (smc_rx_splice+0xafc/0xe20 [smc]) R:0 T:1 IO:1 EX:1 Key:0 M:1 W:0 P:0 AS:3 CC:2 PM:0 RI:0 EA:3 Krnl GPRS: 0000000000000000 001cee80007d3001 00077400000000f8 0000000000000005 0000000000000001 001cee80007d3006 0007740000001000 001c000000000000 000000009b0c99e0 0000000000001000 001c0000000000f8 001c000000000000 000003ffcc6f7c88 0007740003e98000 0007931600000005 000792969b2ff7b8 Krnl Code: 0007931610326960: af000000 mc 0,0 0007931610326964: a7f4ff43 brc 15,00079316103267ea #0007931610326968: af000000 mc 0,0 >000793161032696c: a7f4ff3f brc 15,00079316103267ea 0007931610326970: e320f1000004 lg %r2,256(%r15) 0007931610326976: c0e53fd1b5f5 brasl %r14,000793168fd5d560 000793161032697c: a7f4fbb5 brc 15,00079316103260e6 0007931610326980: b904002b lgr %r2,%r11 Call Trace: smc_rx_splice+0xafc/0xe20 [smc] smc_rx_splice+0x756/0xe20 [smc]) smc_rx_recvmsg+0xa74/0xe00 [smc] smc_splice_read+0x1ce/0x3b0 [smc] sock_splice_read+0xa2/0xf0 do_splice_read+0x198/0x240 splice_file_to_pipe+0x7e/0x110 do_splice+0x59e/0xde0 __do_splice+0x11a/0x2d0 __s390x_sys_splice+0x140/0x1f0 __do_syscall+0x122/0x280 system_call+0x6e/0x90 Last Breaking-Event-Address: smc_rx_splice+0x960/0xe20 [smc] ---[ end trace 0000000000000000 ]---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-40014 In the Linux kernel, the following vulnerability has been resolved: objtool, spi: amd: Fix out-of-bounds stack access in amd_set_spi_freq() If speed_hz < AMD_SPI_MIN_HZ, amd_set_spi_freq() iterates over the entire amd_spi_freq array without breaking out early, causing 'i' to go beyond the array bounds. Fix that by stopping the loop when it gets to the last entry, so the low speed_hz value gets clamped up to AMD_SPI_MIN_HZ. Fixes the following warning with an UBSAN kernel: drivers/spi/spi-amd.o: error: objtool: amd_set_spi_freq() falls through to next function amd_spi_set_opcode()

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2025-40222 In the Linux kernel, the following vulnerability has been resolved: tty: serial: sh-sci: fix RSCI FIFO overrun handling The receive error handling code is shared between RSCI and all other SCIF port types, but the RSCI overrun_reg is specified as a memory offset, while for other SCIF types it is an enum value used to index into the sci_port_params->regs array, as mentioned above the sci_serial_in() function. For RSCI, the overrun_reg is CSR (0x48), causing the sci_getreg() call inside the sci_handle_fifo_overrun() function to index outside the bounds of the regs array, which currently has a size of 20, as specified by SCI_NR_REGS. Because of this, we end up accessing memory outside of RSCI's rsci_port_params structure, which, when interpreted as a plat_sci_reg, happens to have a non-zero size, causing the following WARN when sci_serial_in() is called, as the accidental size does not match the supported register sizes. The existence of the overrun_reg needs to be checked because SCIx_SH3_SCIF_REGTYPE has overrun_reg set to SCLSR, but SCLSR is not present in the regs array. Avoid calling sci_getreg() for port types which don't use standard register handling. Use the ops->read_reg() and ops->write_reg() functions to properly read and write registers for RSCI, and change the type of the status variable to accommodate the 32-bit CSR register. sci_getreg() and sci_serial_in() are also called with overrun_reg in the sci_mpxed_interrupt() interrupt handler, but that code path is not used for RSCI, as it does not have a muxed interrupt. ------------[ cut here ]------------ Invalid register access WARNING: CPU: 0 PID: 0 at drivers/tty/serial/sh-sci.c:522 sci_serial_in+0x38/0xac Modules linked in: renesas_usbhs at24 rzt2h_adc industrialio_adc sha256 cfg80211 bluetooth ecdh_generic ecc rfkill fuse drm backlight ipv6 CPU: 0 UID: 0 PID: 0 Comm: swapper/0 Not tainted 6.17.0-rc1+ #30 PREEMPT Hardware name: Renesas RZ/T2H EVK Board based on r9a09g077m44 (DT) pstate: 604000c5 (nZCv daIF +PAN -UAO -TCO -DIT -SSBS BTYPE=--) pc : sci_serial_in+0x38/0xac lr : sci_serial_in+0x38/0xac sp : ffff800080003e80 x29: ffff800080003e80 x28: ffff800082195b80 x27: 000000000000000d x26: ffff8000821956d0 x25: 0000000000000000 x24: ffff800082195b80 x23: ffff000180e0d800 x22: 0000000000000010 x21: 0000000000000000 x20: 0000000000000010 x19: ffff000180e72000 x18: 000000000000000a x17: ffff8002bcee7000 x16: ffff800080000000 x15: 0720072007200720 x14: 0720072007200720 x13: 0720072007200720 x12: 0720072007200720 x11: 0000000000000058 x10: 0000000000000018 x9 : ffff8000821a6a48 x8 : 0000000000057fa8 x7 : 0000000000000406 x6 : ffff8000821fea48 x5 : ffff00033ef88408 x4 : ffff8002bcee7000 x3 : ffff800082195b80 x2 : 0000000000000000 x1 : 0000000000000000 x0 : ffff800082195b80 Call trace: sci_serial_in+0x38/0xac (P) sci_handle_fifo_overrun.isra.0+0x70/0x134 sci_er_interrupt+0x50/0x39c __handle_irq_event_percpu+0x48/0x140 handle_irq_event+0x44/0xb0 handle_fasteoi_irq+0xf4/0x1a0 handle_irq_desc+0x34/0x58 generic_handle_domain_irq+0x1c/0x28 gic_handle_irq+0x4c/0x140 call_on_irq_stack+0x30/0x48 do_interrupt_handler+0x80/0x84 el1_interrupt+0x34/0x68 el1h_64_irq_handler+0x18/0x24 el1h_64_irq+0x6c/0x70 default_idle_call+0x28/0x58 (P) do_idle+0x1f8/0x250 cpu_startup_entry+0x34/0x3c rest_init+0xd8/0xe0 console_on_rootfs+0x0/0x6c __primary_switched+0x88/0x90 ---[ end trace 0000000000000000 ]---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-40336 In the Linux kernel, the following vulnerability has been resolved: drm/gpusvm: fix hmm_pfn_to_map_order() usage Handle the case where the hmm range partially covers a huge page (like 2M), otherwise we can potentially end up doing something nasty like mapping memory which is outside the range, and maybe not even mapped by the mm. Fix is based on the xe userptr code, which in a future patch will directly use gpusvm, so needs alignment here. v2: - Add kernel-doc (Matt B) - s/fls/ilog2/ (Thomas)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-40356 In the Linux kernel, the following vulnerability has been resolved: spi: rockchip-sfc: Fix DMA-API usage Use DMA-API dma_map_single() call for getting the DMA address of the transfer buffer instead of hacking with virt_to_phys(). This fixes the following DMA-API debug warning: ------------[ cut here ]------------ DMA-API: rockchip-sfc fe300000.spi: device driver tries to sync DMA memory it has not allocated [device address=0x000000000cf70000] [size=288 bytes] WARNING: kernel/dma/debug.c:1106 at check_sync+0x1d8/0x690, CPU#2: systemd-udevd/151 Modules linked in: ... Hardware name: Hardkernel ODROID-M1 (DT) pstate: 604000c9 (nZCv daIF +PAN -UAO -TCO -DIT -SSBS BTYPE=--) pc : check_sync+0x1d8/0x690 lr : check_sync+0x1d8/0x690 .. Call trace: check_sync+0x1d8/0x690 (P) debug_dma_sync_single_for_cpu+0x84/0x8c __dma_sync_single_for_cpu+0x88/0x234 rockchip_sfc_exec_mem_op+0x4a0/0x798 [spi_rockchip_sfc] spi_mem_exec_op+0x408/0x498 spi_nor_read_data+0x170/0x184 spi_nor_read_sfdp+0x74/0xe4 spi_nor_parse_sfdp+0x120/0x11f0 spi_nor_sfdp_init_params_deprecated+0x3c/0x8c spi_nor_scan+0x690/0xf88 spi_nor_probe+0xe4/0x304 spi_mem_probe+0x6c/0xa8 spi_probe+0x94/0xd4 really_probe+0xbc/0x298 ...

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-40364 In the Linux kernel, the following vulnerability has been resolved: io_uring: fix io_req_prep_async with provided buffers io_req_prep_async() can import provided buffers, commit the ring state by giving up on that before, it'll be reimported later if needed.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-46560 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the algorithm exhibits ​​quadratic time complexity (O(n²))​​, allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.

nim-meta-llama3.2-1b-instruct-v1.12.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0

CVE-2025-46570 vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-46722 vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-47277 vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side.​ The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-47910 When using http.CrossOriginProtection, the AddInsecureBypassPattern method can unexpectedly bypass more requests than intended. CrossOriginProtection then skips validation, but forwards the original request path, which may be served by a different handler without the intended security protections.

traefik

CVE-2025-48060 jq is a command-line JSON processor. In versions up to and including 1.7.1, a heap-buffer-overflow is present in function `jv_string_vfmt` in the jq_fuzz_execute harness from oss-fuzz. This crash happens on file jv.c, line 1456 `void* p = malloc(sz);`. As of time of publication, no patched versions are available.

nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-48379 Pillow is a Python imaging library. In versions 11.2.0 to before 11.3.0, there is a heap buffer overflow when writing a sufficiently large (>64k encoded with default settings) image in the DDS format due to writing into a buffer without checking for available space. This only affects users who save untrusted data as a compressed DDS image. This issue has been patched in version 11.3.0.

nim-deepseek-r1-v1.7.3

CVE-2025-48887 vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-48942 vLLM is an inference and serving engine for large language models (LLMs). In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0

CVE-2025-48943 vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0

CVE-2025-48944 vLLM is an inference and serving engine for large language models (LLMs). In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.

nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0

CVE-2025-49794 A use-after-free vulnerability was found in libxml2. This issue occurs when parsing XPath elements under certain circumstances when the XML schematron has the <sch:name path="..."/> schema elements. This flaw allows a malicious actor to craft a malicious XML document used as input for libxml, resulting in the program's crash using libxml or other possible undefined behaviors.

nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-49796 A vulnerability was found in libxml2. Processing certain sch:name elements from the input XML file can trigger a memory corruption issue. This flaw allows an attacker to craft a malicious XML input file that can lead libxml to crash, resulting in a denial of service or other possible undefined behavior due to sensitive data being corrupted in memory.

nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0

CVE-2025-50106 Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: 2D). Supported versions that are affected are Oracle Java SE: 8u451, 8u451-perf, 11.0.27, 17.0.15, 21.0.7, 24.0.1; Oracle GraalVM for JDK: 17.0.15, 21.0.7 and 24.0.1; Oracle GraalVM Enterprise Edition: 21.3.14. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in takeover of Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 8.1 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-50817 A vulnerability in the Python-Future 1.0.0 module allows for arbitrary code execution via the unintended import of a file named test.py. When the module is loaded, it automatically imports test.py, if present in the same directory or in the sys.path. This behavior can be exploited by an attacker who has the ability to write files to the server, allowing the execution of arbitrary code.

cdwdataviz
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
hue
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
runtimedataviz

CVE-2025-51427 An issue was discovered in ModelScope 1.25.0 allowing attackers to execute arbitrary code via crafted module listed in the configuration file (dey_mini.yaml) under the key ['nnet']['module'].

kserve_huggingfaceserver
nim-deepseek-r1-v1.7.3

CVE-2025-53000 The nbconvert tool, jupyter nbconvert, converts Jupyter notebooks to various other formats via Jinja templates. Versions of nbconvert up to and including 7.16.6 on Windows have a vulnerability in which converting a notebook containing SVG output to a PDF results in unauthorized code execution. Specifically, a third party can create a `inkscape.bat` file that defines a Windows batch script, capable of arbitrary code execution. When a user runs `jupyter nbconvert --to pdf` on a notebook containing SVG output to a PDF on a Windows platform from this directory, the `inkscape.bat` file is run unexpectedly. As of time of publication, no known patches exist.

nim-mit-boltz2-v1.3.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-53057 Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: Security). Supported versions that are affected are Oracle Java SE: 8u461, 8u461-perf, 11.0.28, 17.0.16, 21.0.8, 25; Oracle GraalVM for JDK: 17.0.16 and 21.0.8; Oracle GraalVM Enterprise Edition: 21.3.15. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized creation, deletion or modification access to critical data or all Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 5.9 (Integrity impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-53066 Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: JAXP). Supported versions that are affected are Oracle Java SE: 8u461, 8u461-perf, 11.0.28, 17.0.16, 21.0.8, 25; Oracle GraalVM for JDK: 17.0.16 and 21.0.8; Oracle GraalVM Enterprise Edition: 21.3.15. Easily exploitable vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 7.5 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-53643 AIOHTTP is an asynchronous HTTP client/server framework for asyncio and Python. Prior to version 3.12.14, the Python parser is vulnerable to a request smuggling vulnerability due to not parsing trailer sections of an HTTP request. If a pure Python version of aiohttp is installed (i.e. without the usual C extensions) or AIOHTTP_NO_EXTENSIONS is enabled, then an attacker may be able to execute a request smuggling attack to bypass certain firewalls or proxy protections. Version 3.12.14 contains a patch for this issue.

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0

CVE-2025-54121 Starlette is a lightweight ASGI (Asynchronous Server Gateway Interface) framework/toolkit, designed for building async web services in Python. In versions 0.47.1 and below, when parsing a multi-part form with large files (greater than the default max spool size) starlette will block the main thread to roll the file over to disk. This blocks the event thread which means the application can't accept new connections. The UploadFile code has a minor bug where instead of just checking for self._in_memory, the logic should also check if the additional bytes will cause a rollover. The vulnerability is fixed in version 0.47.2.

cmlserving-triton-runtime
dex-upgrade-utils
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-54368 uv is a Python package and project manager written in Rust. In versions 0.8.5 and earlier, remote ZIP archives were handled in a streamwise fashion, and file entries were not reconciled against the archive's central directory. An attacker could contrive a ZIP archive that would extract with legitimate contents on some package installers, and malicious contents on others due to multiple local file entries. An attacker could also contrive a "stacked" ZIP input with multiple internal ZIPs, which would be handled differently by different package installers. The attacker could choose which installer to target in both scenarios. This issue is fixed in version 0.8.6. To work around this issue, users may choose to set UV_INSECURE_NO_ZIP_VALIDATION=1 to revert to the previous behavior.

nim-meta-llama3.1-8b-instruct-v1.13.1

CVE-2025-54388 Moby is an open source container framework developed by Docker Inc. that is distributed as Docker Engine, Mirantis Container Runtime, and various other downstream projects/products. In versions 28.2.0 through 28.3.2, when the firewalld service is reloaded it removes all iptables rules including those created by Docker. While Docker should automatically recreate these rules, versions before 28.3.3 fail to recreate the specific rules that block external access to containers. This means that after a firewalld reload, containers with ports published to localhost (like 127.0.0.1:8080) become accessible from remote machines that have network routing to the Docker bridge, even though they should only be accessible from the host itself. The vulnerability only affects explicitly published ports - unpublished ports remain protected. This issue is fixed in version 28.3.3.

cdsw-runtime-manager

CVE-2025-55039 This issue affects Apache Spark versions before 3.4.4, 3.5.2 and 4.0.0. Apache Spark versions before 4.0.0, 3.5.2 and 3.4.4 use an insecure default network encryption cipher for RPC communication between nodes. When spark.network.crypto.enabled is set to true (it is set to false by default), but spark.network.crypto.cipher is not explicitly configured, Spark defaults to AES in CTR mode (AES/CTR/NoPadding), which provides encryption without authentication. This vulnerability allows a man-in-the-middle attacker to modify encrypted RPC traffic undetected by flipping bits in ciphertext, potentially compromising heartbeat messages or application data and affecting the integrity of Spark workflows. To mitigate this issue, users should either configure spark.network.crypto.cipher to AES/GCM/NoPadding to enable authenticated encryption or enable SSL encryption by setting spark.ssl.enabled to true, which provides stronger transport security.

dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-3.3.2-7.1.9.1078
dex-spark-history-server-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2025-55198 Helm is a package manager for Charts for Kubernetes. Prior to version 3.18.5, when parsing Chart.yaml and index.yaml files, an improper validation of type error can lead to a panic. This issue has been resolved in Helm 3.18.5. A workaround involves ensuring YAML files are formatted as Helm expects prior to processing them with Helm.

compute-operator
compute-usage-monitor
dex-cp
diagnostic-data-generator
dwx
impala-autoscaler
impala-proxy
service-discovery
trino-autoscaler

CVE-2025-55199 Helm is a package manager for Charts for Kubernetes. Prior to version 3.18.5, it is possible to craft a JSON Schema file in a manner which could cause Helm to use all available memory and have an out of memory (OOM) termination. This issue has been resolved in Helm 3.18.5. A workaround involves ensuring all Helm charts that are being loaded into Helm do not have any reference of $ref pointing to /dev/zero.

compute-operator
compute-usage-monitor
dex-cp
diagnostic-data-generator
dwx
impala-autoscaler
impala-proxy
service-discovery
trino-autoscaler

CVE-2025-57809 XGrammar is an open-source library for efficient, flexible, and portable structured generation. Prior to version 0.1.21, XGrammar has an infinite recursion issue in the grammar. This issue has been resolved in version 0.1.21.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

CVE-2025-58060 OpenPrinting CUPS is an open source printing system for Linux and other Unix-like operating systems. In versions 2.4.12 and earlier, when the `AuthType` is set to anything but `Basic`, if the request contains an `Authorization: Basic ...` header, the password is not checked. This results in authentication bypass. Any configuration that allows an `AuthType` that is not `Basic` is affected. Version 2.4.13 fixes the issue.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-58364 OpenPrinting CUPS is an open source printing system for Linux and other Unix-like operating systems. In versions 2.4.12 and earlier, an unsafe deserialization and validation of printer attributes causes null dereference in the libcups library. This is a remote DoS vulnerability available in local subnet in default configurations. It can cause the cups & cups-browsed to crash, on all the machines in local network who are listening for printers (so by default for all regular linux machines). On systems where the vulnerability CVE-2024-47176 (cups-filters 1.x/cups-browsed 2.x vulnerability) was not fixed, and the firewall on the machine does not reject incoming communication to IPP port, and the machine is set to be available to public internet, attack vector "Network" is possible. The current versions of CUPS and cups-browsed projects have the attack vector "Adjacent" in their default configurations. Version 2.4.13 contains a patch for CVE-2025-58364.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-58436 OpenPrinting CUPS is an open source printing system for Linux and other Unix-like operating systems. Prior to version 2.4.15, a client that connects to cupsd but sends slow messages, e.g. only one byte per second, delays cupsd as a whole, such that it becomes unusable by other clients. This issue has been patched in version 2.4.15.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-58446 xgrammar is an open-source library for efficient, flexible, and portable structured generation. A grammar optimizer introduced in 0.1.23 processes large grammars (>100k characters) at very low rates, and can be used for DOS of model providers. This issue is fixed in version 0.1.24.

nemotron_nano_12b_v2_vl_v150
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-59250 Improper input validation in JDBC Driver for SQL Server allows an unauthorized attacker to perform spoofing over a network.

trino

CVE-2025-59343 tar-fs provides filesystem bindings for tar-stream. Versions prior to 3.1.1, 2.1.3, and 1.16.5 are vulnerable to symlink validation bypass if the destination directory is predictable with a specific tarball. This issue has been patched in version 3.1.1, 2.1.4, and 1.16.6. A workaround involves using the ignore option on non files/directories.

cdsw-web

CVE-2025-59530 quic-go is an implementation of the QUIC protocol in Go. In versions prior to 0.49.0, 0.54.1, and 0.55.0, a misbehaving or malicious server can cause a denial-of-service (DoS) attack on the quic-go client by triggering an assertion failure, leading to a process crash. This requires no authentication and can be exploited during the handshake phase. This was observed in the wild with certain server implementations. quic-go needs to be able to handle misbehaving server implementations, including those that prematurely send a HANDSHAKE_DONE frame. Versions 0.49.0, 0.54.1, and 0.55.0 discard Initial keys when receiving a HANDSHAKE_DONE frame, thereby correctly handling premature HANDSHAKE_DONE frames.

traefik

CVE-2025-59842 jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. Prior to version 4.4.8, links generated with LaTeX typesetters in Markdown files and Markdown cells in JupyterLab and Jupyter Notebook did not include the noopener attribute. This is deemed to have no impact on the default installations. Theoretically users of third-party LaTeX-rendering extensions could find themselves vulnerable to reverse tabnabbing attacks if links generated by those extensions included target=_blank (no such extensions are known at time of writing) and they were to click on a link generated in LaTeX (typically visibly different from other links). This issue has been patched in version 4.4.8.

nim-mit-boltz2-v1.3.0

CVE-2025-61147 strukturag libde265 commit d9fea9d wa discovered to contain a segmentation fault via the component decoder_context::compute_framedrop_table().

ml-runtime-pbj-workbench-r4.5-standard

CVE-2025-61669 Jupyter Server is the backend for Jupyter web applications. In jupyter_server versions through 2.17.0, the next query parameter in the login flow is insufficiently validated in `LoginFormHandler._redirect_safe()`, which allows redirects to arbitrary external domains via values such as `///example.com`. An attacker can use a crafted login URL to redirect users to a malicious site and facilitate phishing attacks. This issue is fixed in version 2.18.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2025-61915 OpenPrinting CUPS is an open source printing system for Linux and other Unix-like operating systems. Prior to version 2.4.15, a user in the lpadmin group can use the cups web ui to change the config and insert a malicious line. Then the cupsd process which runs as root will parse the new config and cause an out-of-bound write. This issue has been patched in version 2.4.15.

cml-addon-hadoop-cli-7.3.1.200-90

CVE-2025-62164 vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.

nim-bigcode-starcoder2-7b-v1.15.3
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-62593 Ray is an AI compute engine. Prior to version 2.52.0, developers working with Ray as a development tool can be exploited via a critical RCE vulnerability exploitable via Firefox and Safari. This vulnerability is due to an insufficient guard against browser-based attacks, as the current defense uses the User-Agent header starting with the string "Mozilla" as a defense mechanism. This defense is insufficient as the fetch specification allows the User-Agent header to be modified. Combined with a DNS rebinding attack against the browser, and this vulnerability is exploitable against a developer running Ray who inadvertently visits a malicious website, or is served a malicious advertisement (malvertising). This issue has been patched in version 2.52.0.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.3-70b-instruct-v1.15.1

CVE-2025-62727 Starlette is a lightweight ASGI framework/toolkit. Starting in version 0.39.0 and prior to version 0.49.1 , an unauthenticated attacker can send a crafted HTTP Range header that triggers quadratic-time processing in Starlette's FileResponse Range parsing/merging logic. This enables CPU exhaustion per request, causing denial‑of‑service for endpoints serving files (e.g., StaticFiles or any use of FileResponse). This vulnerability is fixed in 0.49.1.

cmlserving-triton-runtime
dex-upgrade-utils
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2025-62878 A malicious user can manipulate the parameters.pathPattern to create PersistentVolumes in arbitrary locations on the host node, potentially overwriting sensitive files or gaining access to unintended directories.

local-path-provisioner

CVE-2025-66019 pypdf is a free and open-source pure-python PDF library. Prior to version 6.4.0, an attacker who uses this vulnerability can craft a PDF which leads to a memory usage of up to 1 GB per stream. This requires parsing the content stream of a page using the LZWDecode filter. This issue has been patched in version 6.4.0.

cloudera-ai-rag-studio

CVE-2025-66221 Werkzeug is a comprehensive WSGI web application library. Prior to version 3.1.4, Werkzeug's safe_join function allows path segments with Windows device names. On Windows, there are special device names such as CON, AUX, etc that are implicitly present and readable in every directory. send_from_directory uses safe_join to safely serve files at user-specified paths under a directory. If the application is running on Windows, and the requested path ends with a special device name, the file will be opened successfully, but reading will hang indefinitely. This issue has been patched in version 3.1.4.

nim-mit-boltz2-v1.3.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2025-66453 Rhino is an open-source implementation of JavaScript written entirely in Java. Prior to 1.8.1, 1.7.15.1, and 1.7.14.1, when an application passed an attacker controlled float poing number into the toFixed() function, it might lead to high CPU consumption and a potential Denial of Service. Small numbers go through this call stack: NativeNumber.numTo > DToA.JS_dtostr > DToA.JS_dtoa > DToA.pow5mult where pow5mult attempts to raise 5 to a ridiculous power. This vulnerability is fixed in 1.8.1, 1.7.15.1, and 1.7.14.1.

databus-producer
obs_agent

CVE-2025-66506 Fulcio is a free-to-use certificate authority for issuing code signing certificates for an OpenID Connect (OIDC) identity. Prior to 1.8.3, function identity.extractIssuerURL splits (via a call to strings.Split) its argument (which is untrusted data) on periods. As a result, in the face of a malicious request with an (invalid) OIDC identity token in the payload containing many period characters, a call to extractIssuerURL incurs allocations to the tune of O(n) bytes (where n stands for the length of the function's argument), with a constant factor of about 16. This vulnerability is fixed in 1.8.3.

cdsw-s2i-builder-buildah

CVE-2025-67030 Directory Traversal vulnerability in the extractFile method of org.codehaus.plexus.util.Expand in plexus-utils before 6d780b3378829318ba5c2d29547e0012d5b29642. This allows an attacker to execute arbitrary code

thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-environment
thunderhead-iam-api

CVE-2025-68317 In the Linux kernel, the following vulnerability has been resolved: io_uring/zctx: check chained notif contexts Send zc only links ubuf_info for requests coming from the same context. There are some ambiguous syz reports, so let's check the assumption on notification completion.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-68368 In the Linux kernel, the following vulnerability has been resolved: md: init bioset in mddev_init IO operations may be needed before md_run(), such as updating metadata after writing sysfs. Without bioset, this triggers a NULL pointer dereference as below: BUG: kernel NULL pointer dereference, address: 0000000000000020 Call Trace: md_update_sb+0x658/0xe00 new_level_store+0xc5/0x120 md_attr_store+0xc9/0x1e0 sysfs_kf_write+0x6f/0xa0 kernfs_fop_write_iter+0x141/0x2a0 vfs_write+0x1fc/0x5a0 ksys_write+0x79/0x180 __x64_sys_write+0x1d/0x30 x64_sys_call+0x2818/0x2880 do_syscall_64+0xa9/0x580 entry_SYSCALL_64_after_hwframe+0x4b/0x53 Reproducer ``` mdadm -CR /dev/md0 -l1 -n2 /dev/sd[cd] echo inactive > /sys/block/md0/md/array_state echo 10 > /sys/block/md0/md/new_level ``` mddev_init() can only be called once per mddev, no need to test if bioset has been initialized anymore.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68463 Bio.Entrez in Biopython through 186 allows doctype XXE.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2025-68779 In the Linux kernel, the following vulnerability has been resolved: net/mlx5e: Avoid unregistering PSP twice PSP is unregistered twice in: _mlx5e_remove -> mlx5e_psp_unregister mlx5e_nic_cleanup -> mlx5e_psp_unregister This leads to a refcount underflow in some conditions: ------------[ cut here ]------------ refcount_t: underflow; use-after-free. WARNING: CPU: 2 PID: 1694 at lib/refcount.c:28 refcount_warn_saturate+0xd8/0xe0 [...] mlx5e_psp_unregister+0x26/0x50 [mlx5_core] mlx5e_nic_cleanup+0x26/0x90 [mlx5_core] mlx5e_remove+0xe6/0x1f0 [mlx5_core] auxiliary_bus_remove+0x18/0x30 device_release_driver_internal+0x194/0x1f0 bus_remove_device+0xc6/0x130 device_del+0x159/0x3c0 mlx5_rescan_drivers_locked+0xbc/0x2a0 [mlx5_core] [...] Do not directly remove psp from the _mlx5e_remove path, the PSP cleanup happens as part of profile cleanup.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-68784 In the Linux kernel, the following vulnerability has been resolved: xfs: fix a UAF problem in xattr repair The xchk_setup_xattr_buf function can allocate a new value buffer, which means that any reference to ab->value before the call could become a dangling pointer. Fix this by moving an assignment to after the buffer setup.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68791 In the Linux kernel, the following vulnerability has been resolved: fuse: missing copy_finish in fuse-over-io-uring argument copies Fix a possible reference count leak of payload pages during fuse argument copies. [Joanne: simplified error cleanup]

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68792 In the Linux kernel, the following vulnerability has been resolved: tpm2-sessions: Fix out of range indexing in name_size 'name_size' does not have any range checks, and it just directly indexes with TPM_ALG_ID, which could lead into memory corruption at worst. Address the issue by only processing known values and returning -EINVAL for unrecognized values. Make also 'tpm_buf_append_name' and 'tpm_buf_fill_hmac_session' fallible so that errors are detected before causing any spurious TPM traffic. End also the authorization session on failure in both of the functions, as the session state would be then by definition corrupted.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68793 In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: fix a job->pasid access race in gpu recovery Avoid a possible UAF in GPU recovery due to a race between the sched timeout callback and the tdr work queue. The gpu recovery function calls drm_sched_stop() and later drm_sched_start(). drm_sched_start() restarts the tdr queue which will eventually free the job. If the tdr queue frees the job before time out callback completes, the job will be freed and we'll get a UAF when accessing the pasid. Cache it early to avoid the UAF. Example KASAN trace: [ 493.058141] BUG: KASAN: slab-use-after-free in amdgpu_device_gpu_recover+0x968/0x990 [amdgpu] [ 493.067530] Read of size 4 at addr ffff88b0ce3f794c by task kworker/u128:1/323 [ 493.074892] [ 493.076485] CPU: 9 UID: 0 PID: 323 Comm: kworker/u128:1 Tainted: G E 6.16.0-1289896.2.zuul.bf4f11df81c1410bbe901c4373305a31 #1 PREEMPT(voluntary) [ 493.076493] Tainted: [E]=UNSIGNED_MODULE [ 493.076495] Hardware name: TYAN B8021G88V2HR-2T/S8021GM2NR-2T, BIOS V1.03.B10 04/01/2019 [ 493.076500] Workqueue: amdgpu-reset-dev drm_sched_job_timedout [gpu_sched] [ 493.076512] Call Trace: [ 493.076515] <TASK> [ 493.076518] dump_stack_lvl+0x64/0x80 [ 493.076529] print_report+0xce/0x630 [ 493.076536] ? _raw_spin_lock_irqsave+0x86/0xd0 [ 493.076541] ? __pfx__raw_spin_lock_irqsave+0x10/0x10 [ 493.076545] ? amdgpu_device_gpu_recover+0x968/0x990 [amdgpu] [ 493.077253] kasan_report+0xb8/0xf0 [ 493.077258] ? amdgpu_device_gpu_recover+0x968/0x990 [amdgpu] [ 493.077965] amdgpu_device_gpu_recover+0x968/0x990 [amdgpu] [ 493.078672] ? __pfx_amdgpu_device_gpu_recover+0x10/0x10 [amdgpu] [ 493.079378] ? amdgpu_coredump+0x1fd/0x4c0 [amdgpu] [ 493.080111] amdgpu_job_timedout+0x642/0x1400 [amdgpu] [ 493.080903] ? pick_task_fair+0x24e/0x330 [ 493.080910] ? __pfx_amdgpu_job_timedout+0x10/0x10 [amdgpu] [ 493.081702] ? _raw_spin_lock+0x75/0xc0 [ 493.081708] ? __pfx__raw_spin_lock+0x10/0x10 [ 493.081712] drm_sched_job_timedout+0x1b0/0x4b0 [gpu_sched] [ 493.081721] ? __pfx__raw_spin_lock_irq+0x10/0x10 [ 493.081725] process_one_work+0x679/0xff0 [ 493.081732] worker_thread+0x6ce/0xfd0 [ 493.081736] ? __pfx_worker_thread+0x10/0x10 [ 493.081739] kthread+0x376/0x730 [ 493.081744] ? __pfx_kthread+0x10/0x10 [ 493.081748] ? __pfx__raw_spin_lock_irq+0x10/0x10 [ 493.081751] ? __pfx_kthread+0x10/0x10 [ 493.081755] ret_from_fork+0x247/0x330 [ 493.081761] ? __pfx_kthread+0x10/0x10 [ 493.081764] ret_from_fork_asm+0x1a/0x30 [ 493.081771] </TASK> (cherry picked from commit 20880a3fd5dd7bca1a079534cf6596bda92e107d)

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68805 In the Linux kernel, the following vulnerability has been resolved: fuse: fix io-uring list corruption for terminated non-committed requests When a request is terminated before it has been committed, the request is not removed from the queue's list. This leaves a dangling list entry that leads to list corruption and use-after-free issues. Remove the request from the queue's list for terminated non-committed requests.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-68807 In the Linux kernel, the following vulnerability has been resolved: block: fix race between wbt_enable_default and IO submission When wbt_enable_default() is moved out of queue freezing in elevator_change(), it can cause the wbt inflight counter to become negative (-1), leading to hung tasks in the writeback path. Tasks get stuck in wbt_wait() because the counter is in an inconsistent state. The issue occurs because wbt_enable_default() could race with IO submission, allowing the counter to be decremented before proper initialization. This manifests as: rq_wait[0]: inflight: -1 has_waiters: True rwb_enabled() checks the state, which can be updated exactly between wbt_wait() (rq_qos_throttle()) and wbt_track()(rq_qos_track()), then the inflight counter will become negative. And results in hung task warnings like: task:kworker/u24:39 state:D stack:0 pid:14767 Call Trace: rq_qos_wait+0xb4/0x150 wbt_wait+0xa9/0x100 __rq_qos_throttle+0x24/0x40 blk_mq_submit_bio+0x672/0x7b0 ... Fix this by: 1. Splitting wbt_enable_default() into: - __wbt_enable_default(): Returns true if wbt_init() should be called - wbt_enable_default(): Wrapper for existing callers (no init) - wbt_init_enable_default(): New function that checks and inits WBT 2. Using wbt_init_enable_default() in blk_register_queue() to ensure proper initialization during queue registration 3. Move wbt_init() out of wbt_enable_default() which is only for enabling disabled wbt from bfq and iocost, and wbt_init() isn't needed. Then the original lock warning can be avoided. 4. Removing the ELEVATOR_FLAG_ENABLE_WBT_ON_EXIT flag and its handling code since it's no longer needed This ensures WBT is properly initialized before any IO can be submitted, preventing the counter from going negative.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-69873 ajv (Another JSON Schema Validator) before 8.18.0 is vulnerable to Regular Expression Denial of Service (ReDoS) when the $data option is enabled. The pattern keyword accepts runtime data via JSON Pointer syntax ($data reference), which is passed directly to the JavaScript RegExp() constructor without validation. An attacker can inject a malicious regex pattern (e.g., "^(a|a)*$") combined with crafted input to cause catastrophic backtracking. A 31-character payload causes approximately 44 seconds of CPU blocking, with each additional character doubling execution time. This enables complete denial of service with a single HTTP request against any API using ajv with $data: true for dynamic schema validation. This issue is also fixed in version 6.14.0.

cdsw-web

CVE-2025-71070 In the Linux kernel, the following vulnerability has been resolved: ublk: clean up user copy references on ublk server exit If a ublk server process releases a ublk char device file, any requests dispatched to the ublk server but not yet completed will retain a ref value of UBLK_REFCOUNT_INIT. Before commit e63d2228ef83 ("ublk: simplify aborting ublk request"), __ublk_fail_req() would decrement the reference count before completing the failed request. However, that commit optimized __ublk_fail_req() to call __ublk_complete_rq() directly without decrementing the request reference count. The leaked reference count incorrectly allows user copy and zero copy operations on the completed ublk request. It also triggers the WARN_ON_ONCE(refcount_read(&io->ref)) warnings in ublk_queue_reinit() and ublk_deinit_queue(). Commit c5c5eb24ed61 ("ublk: avoid ublk_io_release() called after ublk char dev is closed") already fixed the issue for ublk devices using UBLK_F_SUPPORT_ZERO_COPY or UBLK_F_AUTO_BUF_REG. However, the reference count leak also affects UBLK_F_USER_COPY, the other reference-counted data copy mode. Fix the condition in ublk_check_and_reset_active_ref() to include all reference-counted data copy modes. This ensures that any ublk requests still owned by the ublk server when it exits have their reference counts reset to 0.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71076 In the Linux kernel, the following vulnerability has been resolved: drm/xe/oa: Limit num_syncs to prevent oversized allocations The OA open parameters did not validate num_syncs, allowing userspace to pass arbitrarily large values, potentially leading to excessive allocations. Add check to ensure that num_syncs does not exceed DRM_XE_MAX_SYNCS, returning -EINVAL when the limit is violated. v2: use XE_IOCTL_DBG() and drop duplicated check. (Ashutosh) (cherry picked from commit e057b2d2b8d815df3858a87dffafa2af37e5945b)

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71080 In the Linux kernel, the following vulnerability has been resolved: ipv6: fix a BUG in rt6_get_pcpu_route() under PREEMPT_RT On PREEMPT_RT kernels, after rt6_get_pcpu_route() returns NULL, the current task can be preempted. Another task running on the same CPU may then execute rt6_make_pcpu_route() and successfully install a pcpu_rt entry. When the first task resumes execution, its cmpxchg() in rt6_make_pcpu_route() will fail because rt6i_pcpu is no longer NULL, triggering the BUG_ON(prev). It's easy to reproduce it by adding mdelay() after rt6_get_pcpu_route(). Using preempt_disable/enable is not appropriate here because ip6_rt_pcpu_alloc() may sleep. Fix this by handling the cmpxchg() failure gracefully on PREEMPT_RT: free our allocation and return the existing pcpu_rt installed by another task. The BUG_ON is replaced by WARN_ON_ONCE for non-PREEMPT_RT kernels where such races should not occur.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71090 In the Linux kernel, the following vulnerability has been resolved: nfsd: fix nfsd_file reference leak in nfsd4_add_rdaccess_to_wrdeleg() nfsd4_add_rdaccess_to_wrdeleg() unconditionally overwrites fp->fi_fds[O_RDONLY] with a newly acquired nfsd_file. However, if the client already has a SHARE_ACCESS_READ open from a previous OPEN operation, this action overwrites the existing pointer without releasing its reference, orphaning the previous reference. Additionally, the function originally stored the same nfsd_file pointer in both fp->fi_fds[O_RDONLY] and fp->fi_rdeleg_file with only a single reference. When put_deleg_file() runs, it clears fi_rdeleg_file and calls nfs4_file_put_access() to release the file. However, nfs4_file_put_access() only releases fi_fds[O_RDONLY] when the fi_access[O_RDONLY] counter drops to zero. If another READ open exists on the file, the counter remains elevated and the nfsd_file reference from the delegation is never released. This potentially causes open conflicts on that file. Then, on server shutdown, these leaks cause __nfsd_file_cache_purge() to encounter files with an elevated reference count that cannot be cleaned up, ultimately triggering a BUG() in kmem_cache_destroy() because there are still nfsd_file objects allocated in that cache.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71099 In the Linux kernel, the following vulnerability has been resolved: drm/xe/oa: Fix potential UAF in xe_oa_add_config_ioctl() In xe_oa_add_config_ioctl(), we accessed oa_config->id after dropping metrics_lock. Since this lock protects the lifetime of oa_config, an attacker could guess the id and call xe_oa_remove_config_ioctl() with perfect timing, freeing oa_config before we dereference it, leading to a potential use-after-free. Fix this by caching the id in a local variable while holding the lock. v2: (Matt A) - Dropped mutex_unlock(&oa->metrics_lock) ordering change from xe_oa_remove_config_ioctl() (cherry picked from commit 28aeaed130e8e587fd1b73b6d66ca41ccc5a1a31)

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71100 In the Linux kernel, the following vulnerability has been resolved: wifi: rtlwifi: 8192cu: fix tid out of range in rtl92cu_tx_fill_desc() TID getting from ieee80211_get_tid() might be out of range of array size of sta_entry->tids[], so check TID is less than MAX_TID_COUNT. Othwerwise, UBSAN warn: UBSAN: array-index-out-of-bounds in drivers/net/wireless/realtek/rtlwifi/rtl8192cu/trx.c:514:30 index 10 is out of range for type 'rtl_tid_data [9]'

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71117 In the Linux kernel, the following vulnerability has been resolved: block: Remove queue freezing from several sysfs store callbacks Freezing the request queue from inside sysfs store callbacks may cause a deadlock in combination with the dm-multipath driver and the queue_if_no_path option. Additionally, freezing the request queue slows down system boot on systems where sysfs attributes are set synchronously. Fix this by removing the blk_mq_freeze_queue() / blk_mq_unfreeze_queue() calls from the store callbacks that do not strictly need these callbacks. Add the __data_racy annotation to request_queue.rq_timeout to suppress KCSAN data race reports about the rq_timeout reads. This patch may cause a small delay in applying the new settings. For all the attributes affected by this patch, I/O will complete correctly whether the old or the new value of the attribute is used. This patch affects the following sysfs attributes: * io_poll_delay * io_timeout * nomerges * read_ahead_kb * rq_affinity Here is an example of a deadlock triggered by running test srp/002 if this patch is not applied: task:multipathd Call Trace: <TASK> __schedule+0x8c1/0x1bf0 schedule+0xdd/0x270 schedule_preempt_disabled+0x1c/0x30 __mutex_lock+0xb89/0x1650 mutex_lock_nested+0x1f/0x30 dm_table_set_restrictions+0x823/0xdf0 __bind+0x166/0x590 dm_swap_table+0x2a7/0x490 do_resume+0x1b1/0x610 dev_suspend+0x55/0x1a0 ctl_ioctl+0x3a5/0x7e0 dm_ctl_ioctl+0x12/0x20 __x64_sys_ioctl+0x127/0x1a0 x64_sys_call+0xe2b/0x17d0 do_syscall_64+0x96/0x3a0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 </TASK> task:(udev-worker) Call Trace: <TASK> __schedule+0x8c1/0x1bf0 schedule+0xdd/0x270 blk_mq_freeze_queue_wait+0xf2/0x140 blk_mq_freeze_queue_nomemsave+0x23/0x30 queue_ra_store+0x14e/0x290 queue_attr_store+0x23e/0x2c0 sysfs_kf_write+0xde/0x140 kernfs_fop_write_iter+0x3b2/0x630 vfs_write+0x4fd/0x1390 ksys_write+0xfd/0x230 __x64_sys_write+0x76/0xc0 x64_sys_call+0x276/0x17d0 do_syscall_64+0x96/0x3a0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 </TASK>

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71124 In the Linux kernel, the following vulnerability has been resolved: drm/msm/a6xx: move preempt_prepare_postamble after error check Move the call to preempt_prepare_postamble() after verifying that preempt_postamble_ptr is valid. If preempt_postamble_ptr is NULL, dereferencing it in preempt_prepare_postamble() would lead to a crash. This change avoids calling the preparation function when the postamble allocation has failed, preventing potential NULL pointer dereference and ensuring proper error handling. Patchwork: https://patchwork.freedesktop.org/patch/687659/

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71134 In the Linux kernel, the following vulnerability has been resolved: mm/page_alloc: change all pageblocks migrate type on coalescing When a page is freed it coalesces with a buddy into a higher order page while possible. When the buddy page migrate type differs, it is expected to be updated to match the one of the page being freed. However, only the first pageblock of the buddy page is updated, while the rest of the pageblocks are left unchanged. That causes warnings in later expand() and other code paths (like below), since an inconsistency between migration type of the list containing the page and the page-owned pageblocks migration types is introduced. [ 308.986589] ------------[ cut here ]------------ [ 308.987227] page type is 0, passed migratetype is 1 (nr=256) [ 308.987275] WARNING: CPU: 1 PID: 5224 at mm/page_alloc.c:812 expand+0x23c/0x270 [ 308.987293] Modules linked in: algif_hash(E) af_alg(E) nft_fib_inet(E) nft_fib_ipv4(E) nft_fib_ipv6(E) nft_fib(E) nft_reject_inet(E) nf_reject_ipv4(E) nf_reject_ipv6(E) nft_reject(E) nft_ct(E) nft_chain_nat(E) nf_nat(E) nf_conntrack(E) nf_defrag_ipv6(E) nf_defrag_ipv4(E) nf_tables(E) s390_trng(E) vfio_ccw(E) mdev(E) vfio_iommu_type1(E) vfio(E) sch_fq_codel(E) drm(E) i2c_core(E) drm_panel_orientation_quirks(E) loop(E) nfnetlink(E) vsock_loopback(E) vmw_vsock_virtio_transport_common(E) vsock(E) ctcm(E) fsm(E) diag288_wdt(E) watchdog(E) zfcp(E) scsi_transport_fc(E) ghash_s390(E) prng(E) aes_s390(E) des_generic(E) des_s390(E) libdes(E) sha3_512_s390(E) sha3_256_s390(E) sha_common(E) paes_s390(E) crypto_engine(E) pkey_cca(E) pkey_ep11(E) zcrypt(E) rng_core(E) pkey_pckmo(E) pkey(E) autofs4(E) [ 308.987439] Unloaded tainted modules: hmac_s390(E):2 [ 308.987650] CPU: 1 UID: 0 PID: 5224 Comm: mempig_verify Kdump: loaded Tainted: G E 6.18.0-gcc-bpf-debug #431 PREEMPT [ 308.987657] Tainted: [E]=UNSIGNED_MODULE [ 308.987661] Hardware name: IBM 3906 M04 704 (z/VM 7.3.0) [ 308.987666] Krnl PSW : 0404f00180000000 00000349976fa600 (expand+0x240/0x270) [ 308.987676] R:0 T:1 IO:0 EX:0 Key:0 M:1 W:0 P:0 AS:3 CC:3 PM:0 RI:0 EA:3 [ 308.987682] Krnl GPRS: 0000034980000004 0000000000000005 0000000000000030 000003499a0e6d88 [ 308.987688] 0000000000000005 0000034980000005 000002be803ac000 0000023efe6c8300 [ 308.987692] 0000000000000008 0000034998d57290 000002be00000100 0000023e00000008 [ 308.987696] 0000000000000000 0000000000000000 00000349976fa5fc 000002c99b1eb6f0 [ 308.987708] Krnl Code: 00000349976fa5f0: c020008a02f2 larl %r2,000003499883abd4 00000349976fa5f6: c0e5ffe3f4b5 brasl %r14,0000034997378f60 #00000349976fa5fc: af000000 mc 0,0 >00000349976fa600: a7f4ff4c brc 15,00000349976fa498 00000349976fa604: b9040026 lgr %r2,%r6 00000349976fa608: c0300088317f larl %r3,0000034998800906 00000349976fa60e: c0e5fffdb6e1 brasl %r14,00000349976b13d0 00000349976fa614: af000000 mc 0,0 [ 308.987734] Call Trace: [ 308.987738] [<00000349976fa600>] expand+0x240/0x270 [ 308.987744] ([<00000349976fa5fc>] expand+0x23c/0x270) [ 308.987749] [<00000349976ff95e>] rmqueue_bulk+0x71e/0x940 [ 308.987754] [<00000349976ffd7e>] __rmqueue_pcplist+0x1fe/0x2a0 [ 308.987759] [<0000034997700966>] rmqueue.isra.0+0xb46/0xf40 [ 308.987763] [<0000034997703ec8>] get_page_from_freelist+0x198/0x8d0 [ 308.987768] [<0000034997706fa8>] __alloc_frozen_pages_noprof+0x198/0x400 [ 308.987774] [<00000349977536f8>] alloc_pages_mpol+0xb8/0x220 [ 308.987781] [<0000034997753bf6>] folio_alloc_mpol_noprof+0x26/0xc0 [ 308.987786] [<0000034997753e4c>] vma_alloc_folio_noprof+0x6c/0xa0 [ 308.987791] [<0000034997775b22>] vma_alloc_anon_folio_pmd+0x42/0x240 [ 308.987799] [<000003499777bfea>] __do_huge_pmd_anonymous_page+0x3a/0x210 [ 308.987804] [<00000349976cb0 ---truncated---

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71139 In the Linux kernel, the following vulnerability has been resolved: kernel/kexec: fix IMA when allocation happens in CMA area *** Bug description *** When I tested kexec with the latest kernel, I ran into the following warning: [ 40.712410] ------------[ cut here ]------------ [ 40.712576] WARNING: CPU: 2 PID: 1562 at kernel/kexec_core.c:1001 kimage_map_segment+0x144/0x198 [...] [ 40.816047] Call trace: [ 40.818498] kimage_map_segment+0x144/0x198 (P) [ 40.823221] ima_kexec_post_load+0x58/0xc0 [ 40.827246] __do_sys_kexec_file_load+0x29c/0x368 [...] [ 40.855423] ---[ end trace 0000000000000000 ]--- *** How to reproduce *** This bug is only triggered when the kexec target address is allocated in the CMA area. If no CMA area is reserved in the kernel, use the "cma=" option in the kernel command line to reserve one. *** Root cause *** The commit 07d24902977e ("kexec: enable CMA based contiguous allocation") allocates the kexec target address directly on the CMA area to avoid copying during the jump. In this case, there is no IND_SOURCE for the kexec segment. But the current implementation of kimage_map_segment() assumes that IND_SOURCE pages exist and map them into a contiguous virtual address by vmap(). *** Solution *** If IMA segment is allocated in the CMA area, use its page_address() directly.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71142 In the Linux kernel, the following vulnerability has been resolved: cpuset: fix warning when disabling remote partition A warning was triggered as follows: WARNING: kernel/cgroup/cpuset.c:1651 at remote_partition_disable+0xf7/0x110 RIP: 0010:remote_partition_disable+0xf7/0x110 RSP: 0018:ffffc90001947d88 EFLAGS: 00000206 RAX: 0000000000007fff RBX: ffff888103b6e000 RCX: 0000000000006f40 RDX: 0000000000006f00 RSI: ffffc90001947da8 RDI: ffff888103b6e000 RBP: ffff888103b6e000 R08: 0000000000000000 R09: 0000000000000000 R10: 0000000000000001 R11: ffff88810b2e2728 R12: ffffc90001947da8 R13: 0000000000000000 R14: ffffc90001947da8 R15: ffff8881081f1c00 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f55c8bbe0b2 CR3: 000000010b14c000 CR4: 00000000000006f0 Call Trace: <TASK> update_prstate+0x2d3/0x580 cpuset_partition_write+0x94/0xf0 kernfs_fop_write_iter+0x147/0x200 vfs_write+0x35d/0x500 ksys_write+0x66/0xe0 do_syscall_64+0x6b/0x390 entry_SYSCALL_64_after_hwframe+0x4b/0x53 RIP: 0033:0x7f55c8cd4887 Reproduction steps (on a 16-CPU machine): # cd /sys/fs/cgroup/ # mkdir A1 # echo +cpuset > A1/cgroup.subtree_control # echo "0-14" > A1/cpuset.cpus.exclusive # mkdir A1/A2 # echo "0-14" > A1/A2/cpuset.cpus.exclusive # echo "root" > A1/A2/cpuset.cpus.partition # echo 0 > /sys/devices/system/cpu/cpu15/online # echo member > A1/A2/cpuset.cpus.partition When CPU 15 is offlined, subpartitions_cpus gets cleared because no CPUs remain available for the top_cpuset, forcing partitions to share CPUs with the top_cpuset. In this scenario, disabling the remote partition triggers a warning stating that effective_xcpus is not a subset of subpartitions_cpus. Partitions should be invalidated in this case to inform users that the partition is now invalid(cpus are shared with top_cpuset). To fix this issue: 1. Only emit the warning only if subpartitions_cpus is not empty and the effective_xcpus is not a subset of subpartitions_cpus. 2. During the CPU hotplug process, invalidate partitions if subpartitions_cpus is empty.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71146 In the Linux kernel, the following vulnerability has been resolved: netfilter: nf_conncount: fix leaked ct in error paths There are some situations where ct might be leaked as error paths are skipping the refcounted check and return immediately. In order to solve it make sure that the check is always called.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71155 In the Linux kernel, the following vulnerability has been resolved: KVM: s390: Fix gmap_helper_zap_one_page() again A few checks were missing in gmap_helper_zap_one_page(), which can lead to memory corruption in the guest under specific circumstances. Add the missing checks.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71156 In the Linux kernel, the following vulnerability has been resolved: gve: defer interrupt enabling until NAPI registration Currently, interrupts are automatically enabled immediately upon request. This allows interrupt to fire before the associated NAPI context is fully initialized and cause failures like below: [ 0.946369] Call Trace: [ 0.946369] <IRQ> [ 0.946369] __napi_poll+0x2a/0x1e0 [ 0.946369] net_rx_action+0x2f9/0x3f0 [ 0.946369] handle_softirqs+0xd6/0x2c0 [ 0.946369] ? handle_edge_irq+0xc1/0x1b0 [ 0.946369] __irq_exit_rcu+0xc3/0xe0 [ 0.946369] common_interrupt+0x81/0xa0 [ 0.946369] </IRQ> [ 0.946369] <TASK> [ 0.946369] asm_common_interrupt+0x22/0x40 [ 0.946369] RIP: 0010:pv_native_safe_halt+0xb/0x10 Use the `IRQF_NO_AUTOEN` flag when requesting interrupts to prevent auto enablement and explicitly enable the interrupt in NAPI initialization path (and disable it during NAPI teardown). This ensures that interrupt lifecycle is strictly coupled with readiness of NAPI context.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71157 In the Linux kernel, the following vulnerability has been resolved: RDMA/core: always drop device refcount in ib_del_sub_device_and_put() Since nldev_deldev() (introduced by commit 060c642b2ab8 ("RDMA/nldev: Add support to add/delete a sub IB device through netlink") grabs a reference using ib_device_get_by_index() before calling ib_del_sub_device_and_put(), we need to drop that reference before returning -EOPNOTSUPP error.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2025-71158 In the Linux kernel, the following vulnerability has been resolved: gpio: mpsse: ensure worker is torn down When an IRQ worker is running, unplugging the device would cause a crash. The sealevel hardware this driver was written for was not hotpluggable, so I never realized it. This change uses a spinlock to protect a list of workers, which it tears down on disconnect.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-71300 In the Linux kernel, the following vulnerability has been resolved: Revert "arm64: zynqmp: Add an OP-TEE node to the device tree" This reverts commit 06d22ed6b6635b17551f386b50bb5aaff9b75fbe. OP-TEE logic in U-Boot automatically injects a reserved-memory node along with optee firmware node to kernel device tree. The injection logic is dependent on that there is no manually defined optee node. Having the node in zynqmp.dtsi effectively breaks OP-TEE's insertion of the reserved-memory node, causing memory access violations during runtime.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2025-71302 In the Linux kernel, the following vulnerability has been resolved: drm/panthor: fix for dma-fence safe access rules Commit 506aa8b02a8d6 ("dma-fence: Add safe access helpers and document the rules") details the dma-fence safe access rules. The most common culprit is that drm_sched_fence_get_timeline_name may race with group_free_queue.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-0545 In mlflow/mlflow, the FastAPI job endpoints under `/ajax-api/3.0/jobs/*` are not protected by authentication or authorization when the `basic-auth` app is enabled. This vulnerability affects the latest version of the repository. If job execution is enabled (`MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true`) and any job function is allowlisted, any network client can submit, read, search, and cancel jobs without credentials, bypassing basic-auth entirely. This can lead to unauthenticated remote code execution if allowed jobs perform privileged actions such as shell execution or filesystem changes. Even if jobs are deemed safe, this still constitutes an authentication bypass, potentially resulting in job spam, denial of service (DoS), or data exposure in job results.

cloudera-ai-rag-studio

CVE-2026-0603 A flaw was found in Hibernate. A remote attacker with low privileges could exploit a second-order SQL injection vulnerability by providing specially crafted, unsanitized non-alphanumeric characters in the ID column when the InlineIdsOrClauseBuilder is used. This could lead to sensitive information disclosure, such as reading system files, and allow for data manipulation or deletion within the application's database, resulting in an application level denial of service.

databus-producer
dex_thunderhead-dbuswxmclient
obs_agent

CVE-2026-0846 A vulnerability in the `filestring()` function of the `nltk.util` module in nltk version 3.9.2 allows arbitrary file read due to improper validation of input paths. The function directly opens files specified by user input without sanitization, enabling attackers to access sensitive system files by providing absolute paths or traversal paths. This vulnerability can be exploited locally or remotely, particularly in scenarios where the function is used in web APIs or other interfaces that accept user-supplied input.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-0847 A vulnerability in NLTK versions up to and including 3.9.2 allows arbitrary file read via path traversal in multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader. These classes fail to properly sanitize or validate file paths, enabling attackers to traverse directories and access sensitive files on the server. This issue is particularly critical in scenarios where user-controlled file inputs are processed, such as in machine learning APIs, chatbots, or NLP pipelines. Exploitation of this vulnerability can lead to unauthorized access to sensitive files, including system files, SSH private keys, and API tokens, and may potentially escalate to remote code execution when combined with other vulnerabilities.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-1229 The CombinedMult function in the CIRCL ecc/p384 package (secp384r1 curve) produces an incorrect value for specific inputs. The issue is fixed by using complete addition formulas. ECDH and ECDSA signing relying on this curve are not affected. The bug was fixed in v1.6.3 https://github.com/cloudflare/circl/releases/tag/v1.6.3 .

nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-1260 Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure.

kserve_huggingfaceserver
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-1605 In Eclipse Jetty, versions 12.0.0-12.0.31 and 12.1.0-12.0.5, class GzipHandler exposes a vulnerability when a compressed HTTP request, with Content-Encoding: gzip, is processed and the corresponding response is not compressed. This happens because the JDK Inflater is allocated for decompressing the request, but it is not released because the release mechanism is tied to the compressed response. In this case, since the response is not compressed, the release mechanism does not trigger, causing the leak.

trino

CVE-2026-2327 Versions of the package markdown-it from 13.0.0 and before 14.1.1 are vulnerable to Regular Expression Denial of Service (ReDoS) due to the use of the regex /\*+$/ in the linkify function. An attacker can supply a long sequence of * characters followed by a non-matching character, which triggers excessive backtracking and may lead to a denial-of-service condition.

cloudera-ai-agent-studio

CVE-2026-2359 Multer is a node.js middleware for handling `multipart/form-data`. A vulnerability in Multer prior to version 2.1.0 allows an attacker to trigger a Denial of Service (DoS) by dropping connection during file upload, potentially causing resource exhaustion. Users should upgrade to version 2.1.0 to receive a patch. No known workarounds are available.

cdsw-web

CVE-2026-2651 A vulnerability in MLflow versions <=3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when the `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded. The issue is resolved in version 3.10.0.

cloudera-ai-rag-studio

CVE-2026-2652 A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows unauthenticated access to certain FastAPI routes when the server is started with authentication enabled (`--app-name basic-auth`) and served via uvicorn (ASGI). The FastAPI permission middleware only enforces authentication on `/gateway/` routes, leaving other routes such as the Job API (`/ajax-api/3.0/jobs/*`) and the OpenTelemetry trace ingestion API (`/v1/traces`) unprotected. This allows unauthenticated remote attackers to submit jobs, read job results, cancel running jobs, and inject arbitrary trace data into experiments. The issue arises from an architectural mismatch between Flask and FastAPI authentication mechanisms, where the `_find_fastapi_validator()` function fails to handle non-`/gateway/` paths, resulting in a complete authentication bypass. This vulnerability is fixed in version 3.10.0.

cloudera-ai-rag-studio

CVE-2026-2781 Integer overflow in the Libraries component in NSS. This vulnerability affects Firefox < 148, Firefox ESR < 140.8, Thunderbird < 148, and Thunderbird < 140.8.

cml-addon-hadoop-cli-7.3.1.200-90
cmlserving-triton-runtime

CVE-2026-3198 MLflow 3.9.0 with basic-auth (`--app-name basic-auth`) fails to enforce authorization checks for multiple Gateway API 'list' endpoints. Specifically, the `BEFORE_REQUEST_HANDLERS` dictionary in `mlflow/server/auth/__init__.py` does not include entries for `ListGatewaySecretInfos`, `ListGatewayEndpoints`, and `ListGatewayModelDefinitions`. This allows any authenticated user, regardless of their assigned permissions, to enumerate all gateway secrets, endpoints, and model definitions. This vulnerability exposes sensitive information, such as API keys, endpoint configurations, and proprietary model definitions, to unauthorized users.

cloudera-ai-rag-studio

CVE-2026-3293 A weakness has been identified in snowflakedb snowflake-jdbc up to 4.0.1. Impacted is the function SdkProxyRoutePlanner of the file src/main/java/net/snowflake/client/internal/core/SdkProxyRoutePlanner.java of the component JDBC URL Handler. Executing a manipulation of the argument nonProxyHosts can lead to inefficient regular expression complexity. The attack can only be executed locally. The exploit has been made available to the public and could be used for attacks. This patch is called 5fb0a8a318a2ed87f4022a1f56e742424ba94052. A patch should be applied to remediate this issue.

trino

CVE-2026-3304 Multer is a node.js middleware for handling `multipart/form-data`. A vulnerability in Multer prior to version 2.1.0 allows an attacker to trigger a Denial of Service (DoS) by sending malformed requests, potentially causing resource exhaustion. Users should upgrade to version 2.1.0 to receive a patch. No known workarounds are available.

cdsw-web

CVE-2026-3520 Multer is a node.js middleware for handling `multipart/form-data`. A vulnerability in Multer prior to version 2.1.1 allows an attacker to trigger a Denial of Service (DoS) by sending malformed requests, potentially causing stack overflow. Users should upgrade to version 2.1.1 to receive a patch. No known workarounds are available.

cdsw-web

CVE-2026-3805 When doing a second SMB request to the same host again, curl would wrongly use a data pointer pointing into already freed memory.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-3902 An issue was discovered in 6.0 before 6.0.4, 5.2 before 5.2.13, and 4.2 before 4.2.30. `ASGIRequest` allows a remote attacker to spoof headers by exploiting an ambiguous mapping of two header variants (with hyphens or with underscores) to a single version with underscores. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Tarek Nakkouch for reporting this issue.

cdwdataviz
runtimedataviz

CVE-2026-3950 A vulnerability was identified in strukturag libheif up to 1.21.2. This impacts the function Track::load of the file libheif/sequences/track.cc of the component stsz/stts. The manipulation leads to out-of-bounds read. The attack needs to be performed locally. The exploit is publicly available and might be used. Applying a patch is the recommended action to fix this issue. The patch available is inofficial and not approved yet.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-4035 A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.

cloudera-ai-rag-studio

CVE-2026-4137 In mlflow/mlflow versions prior to 3.11.0, the `get_or_create_nfs_tmp_dir()` function in `mlflow/utils/file_utils.py` creates temporary directories with world-writable permissions (0o777), and the `_create_model_downloading_tmp_dir()` function in `mlflow/pyfunc/__init__.py` creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via `cloudpickle.load()`. This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.

cloudera-ai-rag-studio

CVE-2026-4277 An issue was discovered in 6.0 before 6.0.4, 5.2 before 5.2.13, and 4.2 before 4.2.30. Add permissions on inline model instances were not validated on submission of forged `POST` data in `GenericInlineModelAdmin`. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank N05ec@LZU-DSLab for reporting this issue.

cdwdataviz
runtimedataviz

CVE-2026-4292 An issue was discovered in 6.0 before 6.0.4, 5.2 before 5.2.13, and 4.2 before 4.2.30. Admin changelist forms using `ModelAdmin.list_editable` incorrectly allowed new instances to be created via forged `POST` data. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Cantina for reporting this issue.

cdwdataviz
runtimedataviz

CVE-2026-4367 A flaw was found in libXpm. A local user with low privileges could exploit an Out-of-Bounds Read vulnerability in the `xpmNextWord()` function by processing a specially crafted or very small XPM (X PixMap) image file. This improper validation of file boundaries can cause an internal pointer to read beyond the file's end, leading to application crashes and Denial of Service conditions.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard

CVE-2026-5038 Impact: multer versions 2.0.0-alpha.1 through 2.1.1 and 3.0.0-alpha.1 are vulnerable to a Denial of Service when using diskStorage. Aborted or malformed multipart uploads leave orphaned partial files on disk because the Readable.pipe() call does not propagate the stream destroy signal to the underlying fs.WriteStream. An attacker can exhaust disk space by triggering many aborted uploads, with no application bug required. Patches: Users should upgrade to multer 2.2.0 (2.x line) or 3.0.0-alpha.2 (3.x prerelease). Both versions track in-flight write streams and clean them up on the abort path. Workarounds: None.

cdsw-web

CVE-2026-5079 Impact: multer versions 1.0.0 through 2.1.1 and 3.0.0-alpha.1 are vulnerable to a Denial of Service via deeply nested field names in multipart form data. The append-field dependency parses bracket notation in field names with no limit on nesting depth, allowing an attacker to force allocation of deeply nested object structures that consume CPU and memory. A single HTTP request with a crafted multipart body is sufficient to exploit this. Patches: Users should upgrade to multer 2.2.0 (2.x line) or 3.0.0-alpha.2 (3.x prerelease) and configure the new limits.fieldNestingDepth option to the minimum depth their application requires. Workarounds: Set limits.fields to a reasonable value to reduce the number of fields an attacker can send per request. This does not fully mitigate the issue but limits the impact.

cdsw-web

CVE-2026-5201 A flaw was found in the gdk-pixbuf library. This heap-based buffer overflow vulnerability occurs in the JPEG image loader due to improper validation of color component counts when processing a specially crafted JPEG image. A remote attacker can exploit this flaw without user interaction, for example, via thumbnail generation. Successful exploitation leads to application crashes and denial of service (DoS) conditions.

cloudera-ai-rag-studio
cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard

CVE-2026-5598 Covert timing channel vulnerability in Legion of the Bouncy Castle Inc. BC-JAVA core on all (core modules). This vulnerability is associated with program files FrodoEngine.Java. This issue affects BC-JAVA: from 1.71 before 1.80.2, from 1.81 before 1.81.1, from 1.82 before 1.84.

admissiond
catalogd
cdc-profilers
cdc_profilers
cdsw-mlops-governance
cml-addon-hadoop-cli-7.1.9.20000-24
cml-addon-hadoop-cli-7.3.1.200-90
cml-addon-hadoop-cli-7.3.1.709-1
cml-addon-hadoop-cli-7.3.2.0-957
cml-addon-ozone-732.1.0-b4
configtemplate
databus-producer
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-knox
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-runtime-3.5.4-7.1.9.1078
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-runtime-3.5.4-7.3.2.0
dex-livy-server-2.4.8-7.1.9.1078
dex-livy-server-3.3.2-7.1.9.1078
dex-livy-server-3.5.4-7.1.9.1078
dex-livy-server-3.5.4-7.3.1.709
dex-livy-server-3.5.4-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-history-server-3.3.2-7.1.9.1078
dex-spark-history-server-3.5.4-7.1.9.1078
dex-spark-history-server-3.5.4-7.3.1.709
dex-spark-history-server-3.5.4-7.3.2.0
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat
dex-spark-runtime-3.5.4-7.1.9.1078
dex-spark-runtime-3.5.4-7.3.1.709
dex-spark-runtime-3.5.4-7.3.2.0
dex_ozone-parcel-image
dex_thunderhead-configtemplate
dex_thunderhead-dbuswxmclient
dex_thunderhead-tgtgenerator
hive
hueqp
impalad_coord_exec
impalad_coordinator
impalad_executor
knox-gateway
obs_agent
ozone-parcel-image
thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api
trino

CVE-2026-6192 A vulnerability was identified in uclouvain openjpeg up to 2.5.4. This impacts the function opj_pi_initialise_encode in the library src/lib/openjp2/pi.c. The manipulation leads to integer overflow. The attack must be carried out locally. The exploit is publicly available and might be used. The identifier of the patch is 839936aa33eb8899bbbd80fda02796bb65068951. It is suggested to install a patch to address this issue.

cloudera-ai-rag-studio
cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-6476 SQL injection in PostgreSQL pg_createsubscriber allows an attacker with pg_create_subscription rights to execute arbitrary SQL as a superuser. The attack takes effect when pg_createsubscriber next runs. Within major versions 17 and 18, minor versions before PostgreSQL 18.4 and 17.10 are affected. Versions before PostgreSQL 17 are unaffected.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-6575 Buffer over-read in PostgreSQL function pg_restore_attribute_stats() accepts array values of unmatched length, which causes query planning to read past end of one array. This allows a table maintainer to infer memory values past that array end. Within major version 18, minor versions before PostgreSQL 18.4 are affected. Versions before PostgreSQL 18 are unaffected.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-6638 SQL injection in PostgreSQL logical replication ALTER SUBSCRIPTION ... REFRESH PUBLICATION allows a subscriber table creator to execute arbitrary SQL with the subscription's publication-side credentials. The attack takes effect at the next REFRESH PUBLICATION. Within major versions 16, 17, and 18, minor versions before PostgreSQL 18.4, 17.10, and 16.14 are affected. Versions before PostgreSQL 16 are unaffected.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-6732 A flaw was found in libxml2. This vulnerability occurs when the library processes a specially crafted XML Schema Definition (XSD) validated document that includes an internal entity reference. An attacker could exploit this by providing a malicious document, leading to a type confusion error that causes the application to crash. This results in a denial of service (DoS), making the affected system or application unavailable.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat
kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-6733 Impact: Undici's HTTP/1.1 client is vulnerable to response queue poisoning on reused keep-alive sockets. An attacker-controlled upstream server can inject an unsolicited HTTP/1.1 response onto an idle socket after a request completes. When the client dispatches the next request on that socket, it associates the injected response with the new request, causing responses to be delivered to the wrong requests. This requires an attacker-controlled or compromised upstream HTTP/1.1 server and keep-alive connection reuse. Patches: Upgrade to undici v6.26.0, v7.28.0 or v8.5.0. Workarounds: Disable keep-alive connection reuse by setting keepAliveTimeout: 0 on the Client or Pool.

cdsw-s2i-registry
cdsw-web
cloudera-ai-rag-studio
dpsgateway
hue

CVE-2026-7598 A security vulnerability has been detected in libssh2 up to 1.11.1. The impacted element is the function userauth_password of the file src/userauth.c. Such manipulation of the argument username_len/password_len leads to integer overflow. The attack may be launched remotely. The name of the patch is 256d04b60d80bf1190e96b0ad1e91b2174d744b1. A patch should be applied to remediate this issue.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-8461 An out-of-bounds write vulnerability in FFmpeg's libavcodec library, specifically in the MagicYUV decoder, allows denial-of-service and, in some cases, can be exploited for remote code execution. This vulnerability is associated with the file libavcodec/magicyuv.C. This issue affects FFmpeg before version 8.1.2.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-8838 Unsafe use of Python's eval() on server-received data in the vector_in() function in amazon-redshift-python-driver before 2.1.14 allows a rogue server or man-in-the-middle actor to execute arbitrary code on the client. To remediate this issue, users should upgrade to version 2.1.14.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0

CVE-2026-8926 When asking curl to use a `.netrc` file to find credentials and at the same time specifying a URL with a username(without a password), like `https://user@example.com/`, curl could wrongly get and use the password for *another* user set in the `.netrc` file for that host if such a one exists and there is no match for the specified user.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-9080 Calling `curl_easy_pause()` within the event-based `CURLMOPT_SOCKETFUNCTION` callback triggers a use-after-free vulnerability, where libcurl attempts to store a flag using a dangling struct pointer immediately after that pointer's memory has been freed.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-9256 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_rewrite_module module. This vulnerability exists when a rewrite directive uses a regex pattern with distinct, overlapping Perl-Compatible Regular Expression (PCRE) captures (for example, ^/((.*))$) and a replacement string that references multiple such captures (for example, $1$2) in a redirect or arguments context. An unauthenticated attacker along with conditions beyond their control can exploit this vulnerability by sending crafted HTTP requests. This may cause a heap buffer overflow in the NGINX worker process leading to a restart. Additionally, attackers can execute code on systems with Address Space Layout Randomization (ASLR) disabled or when the attacker can bypass ASLR. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

dex-downloads
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-9375 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

cmlserving-triton-runtime
nim-minimax-ai-minimax-m25-v1.7.1
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
python-runtime

CVE-2026-9545 In this scenario, libcurl first uses a proper HTTP/3 server for the initial transfers, and when it makes a second transfer to the same site it has been replaced by the attacker's impostor machine - without a valid certificate. When libcurl returns to the hostname the second time with a cached SSL session (`CURLOPT_SSL_SESSIONID_CACHE` is not disabled) and early data enabled (the `CURLSSLOPT_EARLYDATA` bit is set in `CURLOPT_SSL_OPTIONS`), libcurl might send off the second request's bytes on that new connection *before* enforcing the certificate verification failure. Potentially leaking sensitive information.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-9679 Impact: undici's cookie parser in parseSetCookie percent-decodes cookie values via qsUnescape, turning encoded sequences like %0D%0A, %00, %3B, and %3D into their literal byte equivalents. RFC 6265 §5.4 does not specify any decoding and browsers do not decode either. Applications that parse a Set-Cookie header and then forward the parsed value into a response header (proxies, middleware, SSR frameworks) become vulnerable to HTTP response header injection: an attacker-controlled upstream can inject arbitrary Set-Cookie, Location, or Cache-Control headers into the application's downstream response, enabling session fixation, open redirect, or cache poisoning. Affected applications are those that use undici's cookie parsing (parseSetCookie, parseCookie, getSetCookies) and forward the parsed cookie value into a response header. This was introduced in undici 7.0.0 via PR #3789. Patches: Upgrade to undici v6.26.0, v7.28.0 or v8.5.0. Workarounds: If upgrade is not immediately possible, do not forward values returned by parseSetCookie/parseCookie/getSetCookies directly into response headers; sanitize the value first to strip or reject CR, LF, NUL, ;, and = bytes.

cdsw-s2i-registry
cdsw-web
cloudera-ai-rag-studio
dpsgateway
hue

CVE-2026-10118 A flaw was found in Poppler's Splash backend. A remote attacker could exploit this vulnerability by crafting a malicious PDF file that, when rendered, triggers an integer overflow in the `tilingPatternFill` function. This overflow leads to an undersized heap memory allocation, allowing a subsequent out-of-bounds write. Successful exploitation could result in arbitrary code execution, information disclosure, or denial of service within the context of the application processing the PDF.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-10723 BIND may accept incorrect child-zone NSEC3 records as valid, which could allow an attacker to forge authenticated NXDOMAIN responses. This issue affects BIND 9 versions 9.18.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-10881 Out of bounds read and write in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10882 Use after free in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10883 Type Confusion in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10884 Use after free in Chromecast in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10886 Use after free in FileSystem in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10888 Use after free in Cast Streaming in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to execute arbitrary code via malicious network traffic. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10889 Out of bounds read in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10890 Use after free in Cast in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to potentially exploit heap corruption via malicious network traffic. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10891 Use after free in GFX in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10893 Use after free in Chromoting in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10894 Use after free in Printing in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10895 Use after free in Ozone in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10897 Inappropriate implementation in GPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10898 Stack buffer overflow in GPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10899 Use after free in Ozone in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10902 Use after free in Ozone in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-10903 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10904 Inappropriate implementation in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10905 Use after free in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10906 Use after free in WebAuthentication in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10907 Out of bounds write in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10909 Use after free in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10910 Type Confusion in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10911 Insufficient validation of untrusted input in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10912 Insufficient validation of untrusted input in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10916 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10917 Insufficient validation of untrusted input in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10918 Use after free in Viz in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10919 Use after free in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10921 Integer overflow in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10922 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to bypass same origin policy via malicious network traffic. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10924 Integer overflow in Chromecast in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10926 Use after free in Cast in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to execute arbitrary code via malicious network traffic. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10927 Out of bounds read in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10928 Script injection in Headless in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10931 Use after free in FileSystem in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10935 Type Confusion in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10936 Type Confusion in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10937 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10938 Inappropriate implementation in Input in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10939 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10941 Out of bounds memory access in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10943 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10945 Use after free in PDF in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10946 Heap buffer overflow in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10947 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10948 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10949 Heap buffer overflow in Video in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10954 Use after free in Actor in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10956 Use after free in MimeHandlerView in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10957 Use after free in Glic in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10960 Uninitialized Use in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10962 Type Confusion in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10963 Integer overflow in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10964 Integer overflow in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10965 Integer overflow in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10966 Inappropriate implementation in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted video file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10969 Insufficient validation of untrusted input in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform privilege escalation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10970 Insufficient validation of untrusted input in InterestGroups in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10972 Use after free in Ozone in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10973 Uninitialized Use in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10974 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10975 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10976 Uninitialized Use in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10977 Uninitialized Use in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10979 Out of bounds read in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10980 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10981 Insufficient validation of untrusted input in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted video file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-10982 Use after free in WebXR in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10983 Insufficient validation of untrusted input in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10985 Out of bounds read in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10986 Integer overflow in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a malicious file. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10987 Integer overflow in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10988 Use after free in Views in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10989 Inappropriate implementation in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
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CVE-2026-10990 Use after free in Glic in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10991 Use after free in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10992 Insufficient data validation in Animation in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10993 Heap buffer overflow in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10994 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10995 Heap buffer overflow in TabStrip in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10996 Inappropriate implementation in Workers in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10997 Insufficient policy enforcement in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass discretionary access control via a crafted Chrome Extension. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10998 Out of bounds read in Media in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to perform an out of bounds memory read via malicious network traffic. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-10999 Integer overflow in ANGLE in Google Chrome on Windows prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11000 Use after free in Fonts in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11001 Inappropriate implementation in Payments in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11002 Use after free in Autofill in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11003 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11004 Out of bounds read in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11006 Out of bounds read in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11008 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11011 Insufficient policy enforcement in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11013 Insufficient validation of untrusted input in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11014 Insufficient policy enforcement in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass site isolation via a crafted Chrome Extension. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11015 Out of bounds read in WebGPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11016 Insufficient validation of untrusted input in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11017 Inappropriate implementation in Link Preview in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11018 Insufficient policy enforcement in Actor in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11020 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted XML file. (Chromium security severity: Medium)

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CVE-2026-11022 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11023 Inappropriate implementation in WebAppInstalls in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11024 Stack buffer overflow in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit stack corruption via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11026 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass navigation restrictions via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11027 Insufficient validation of untrusted input in Glic in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11028 Use after free in Media in Google Chrome on Linux and ChromeOS prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11030 Use after free in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11031 Insufficient validation of untrusted input in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11032 Inappropriate implementation in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11036 Inappropriate implementation in DOM in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11037 Out of bounds write in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted video file. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11038 Insufficient policy enforcement in Subresource Integrity in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass content security policy via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11039 Uninitialized Use in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11040 Use after free in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11042 Use after free in Views in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11045 Insufficient validation of untrusted input in GPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11046 Insufficient validation of untrusted input in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11048 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass same origin policy via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11049 Use after free in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11050 Use after free in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11051 Out of bounds read in ANGLE in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11054 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11057 Uninitialized Use in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11059 Use after free in Blink in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11061 Type Confusion in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11062 Insufficient policy enforcement in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to inject scripts or HTML into a privileged page via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11065 Use after free in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11066 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11067 Uninitialized Use in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11068 Use after free in WebSockets in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11069 Insufficient validation of untrusted input in Cast in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11071 Use after free in Base in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11073 Use after free in WebGL in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11074 Use after free in WebRTC in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11075 Out of bounds read in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11076 Type Confusion in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11077 Bad cast in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11078 Inappropriate implementation in FileSystem in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11079 Insufficient validation of untrusted input in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform an out of bounds memory write via a crafted video file. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11081 Inappropriate implementation in Canvas in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11083 Inappropriate implementation in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11084 Inappropriate implementation in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11086 Inappropriate implementation in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11087 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
runtimedataviz

CVE-2026-11088 Integer overflow in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11089 Uninitialized Use in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11090 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11091 Inappropriate implementation in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
runtimedataviz

CVE-2026-11092 Insufficient policy enforcement in DevTools in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to perform privilege escalation via a crafted Chrome Extension. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11093 Inappropriate implementation in Printing in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11095 Insufficient validation of untrusted input in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11096 Out of bounds read in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11098 Insufficient validation of untrusted input in GPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11102 Inappropriate implementation in Isolated Web Apps in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a malicious file. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11104 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11105 Insufficient validation of untrusted input in WebUI in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11106 Inappropriate implementation in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11107 Inappropriate implementation in Downloads in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11109 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11110 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11111 Out of bounds read in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11112 Insufficient validation of untrusted input in Chromoting in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted Chrome Extension. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11113 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11116 Use after free in Chromoting in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11118 Use after free in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11120 Insufficient validation of untrusted input in Enterprise Reporting in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11121 Insufficient validation of untrusted input in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11122 Inappropriate implementation in Keyboard in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11123 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11124 Integer overflow in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11125 Use after free in Compositing in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11126 Inappropriate implementation in DevTools in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to leak cross-origin data via a crafted Chrome Extension. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11128 Inappropriate implementation in Web Share in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11129 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11130 Use after free in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
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CVE-2026-11132 Insufficient policy enforcement in Paint in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11133 Insufficient policy enforcement in Paint in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11134 Inappropriate implementation in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11135 Insufficient policy enforcement in Autofill in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11136 Use after free in Canvas in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11137 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11138 Uninitialized Use in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11139 Inappropriate implementation in Paint in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11140 Out of bounds read in Chromecast in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11141 Uninitialized Use in Audio in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11142 Insufficient policy enforcement in Paint in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11143 Out of bounds read in Extensions in Google Chrome on Linux prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to obtain potentially sensitive information from process memory via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11144 Use after free in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted video file. (Chromium security severity: Medium)

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CVE-2026-11146 Insufficient validation of untrusted input in Chromoting in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11149 Insufficient validation of untrusted input in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform privilege escalation via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11150 Inappropriate implementation in XML in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11151 Insufficient validation of untrusted input in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11152 Object lifecycle issue in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11153 Side-channel information leakage in Forms in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11154 Use after free in Dawn in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11155 Inappropriate implementation in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11156 Inappropriate implementation in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11157 Script injection in Accessibility in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to inject arbitrary scripts or HTML (UXSS) via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11159 Uninitialized Use in Skia in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11160 Out of bounds read in Input in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11161 Inappropriate implementation in DataTransfer in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11162 Inappropriate implementation in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11164 Use after free in Blink in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11166 Inappropriate implementation in SVG in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11168 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11169 Inappropriate implementation in XML in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted XML file. (Chromium security severity: Medium)

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CVE-2026-11170 Inappropriate implementation in Chromoting in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to perform OS-level privilege escalation via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11171 Integer overflow in Blink in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11173 Out of bounds write in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11174 Inappropriate implementation in Site Isolation in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11176 Inappropriate implementation in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11177 Use after free in Omnibox in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11179 Inappropriate implementation in ORB in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11180 Inappropriate implementation in SVG in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11181 Inappropriate implementation in Media Session in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11182 Inappropriate implementation in SVG in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11183 Out of bounds read in GWP-ASan in Google Chrome prior to 149.0.7827.53 allowed a local attacker to obtain potentially sensitive information from process memory via a malicious file. (Chromium security severity: Medium)

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CVE-2026-11184 Insufficient policy enforcement in Actor in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11185 Use after free in V8 in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to execute arbitrary code inside a sandbox via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11186 Inappropriate implementation in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11187 Inappropriate implementation in Glic in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11189 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass navigation restrictions via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11190 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass discretionary access control via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11191 Out of bounds memory access in ANGLE in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11192 Insufficient validation of untrusted input in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11193 Insufficient policy enforcement in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11194 Inappropriate implementation in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11195 Inappropriate implementation in MHTML in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11196 Type Confusion in XML in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted XML file. (Chromium security severity: Medium)

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CVE-2026-11197 Insufficient policy enforcement in Workers in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11198 Insufficient validation of untrusted input in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted video file. (Chromium security severity: Medium)

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CVE-2026-11199 Inappropriate implementation in WebRTC in Google Chrome prior to 149.0.7827.53 allowed an attacker in a privileged network position to leak cross-origin data via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11200 Inappropriate implementation in WebRTC in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11201 Use after free in ServiceWorker in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to execute arbitrary code via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11206 Insufficient policy enforcement in ServiceWorker in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11207 Insufficient validation of untrusted input in Autofill in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via malicious network traffic. (Chromium security severity: Medium)

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CVE-2026-11208 Use after free in Codecs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11209 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11210 Inappropriate implementation in Safe Browsing in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass discretionary access control via a crafted RAR file. (Chromium security severity: Medium)

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CVE-2026-11211 Integer overflow in V8 in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11212 Insufficient policy enforcement in DevTools in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to leak cross-origin data via a crafted Chrome Extension. (Chromium security severity: Medium)

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CVE-2026-11213 Insufficient validation of untrusted input in Reading Mode in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

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CVE-2026-11216 Incorrect security UI in File Input in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11217 Inappropriate implementation in Fenced Frames in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11219 Inappropriate implementation in Navigation in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11220 Insufficient validation of untrusted input in Navigation in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11221 Insufficient validation of untrusted input in PointerLock in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11222 Incorrect security UI in Tab Strip in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform domain spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11223 Insufficient validation of untrusted input in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11224 Use after free in Chromoting in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Low)

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CVE-2026-11225 Inappropriate implementation in WebUI in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform domain spoofing via a crafted domain name. (Chromium security severity: Low)

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CVE-2026-11227 Incorrect security UI in Tab Hover Cards in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform domain spoofing via a crafted domain name. (Chromium security severity: Low)

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CVE-2026-11228 Inappropriate implementation in File Input in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11229 Inappropriate implementation in Enterprise in Google Chrome prior to 149.0.7827.53 allowed a local attacker to perform privilege escalation via physical access to the device. (Chromium security severity: Low)

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CVE-2026-11230 Use after free in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11232 Inappropriate implementation in TabGroups in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via malicious network traffic. (Chromium security severity: Low)

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CVE-2026-11233 Insufficient policy enforcement in FoldableAPIs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11234 Inappropriate implementation in FoldableAPIs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11235 Insufficient policy enforcement in Compositing in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11236 Insufficient policy enforcement in Web Bluetooth in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11237 Insufficient validation of untrusted input in Media in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11238 Inappropriate implementation in DevTools in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to obtain potentially sensitive information from process memory via a crafted Chrome Extension. (Chromium security severity: Low)

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CVE-2026-11239 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11240 Insufficient validation of untrusted input in Loader in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11241 Insufficient validation of untrusted input in Cast in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11242 Insufficient validation of untrusted input in Plugins in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11243 Inappropriate implementation in Downloads in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11244 Insufficient validation of untrusted input in WebAuthentication in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11245 Inappropriate implementation in Payments in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11246 Insufficient validation of untrusted input in IndexedDB in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11248 Inappropriate implementation in Google Lens in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11249 Use after free in Network in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11250 Inappropriate implementation in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11251 Insufficient policy enforcement in Password Manager in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11252 Insufficient policy enforcement in Content Settings in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11253 Inappropriate implementation in Permissions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11254 Inappropriate implementation in Permissions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11255 Insufficient validation of untrusted input in Storage Access API in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11256 Integer overflow in GPU in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11257 Inappropriate implementation in Browser in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11258 Inappropriate implementation in File System Access in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11259 Insufficient validation of untrusted input in Cast in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11260 Inappropriate implementation in Permissions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass content security policy via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11261 Inappropriate implementation in PDF in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11262 Use after free in TabStrip in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Low)

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CVE-2026-11264 Policy bypass in Content Security Policy in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass content security policy via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11265 Inappropriate implementation in Autofill in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11266 Inappropriate implementation in SafeBrowsing in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass Safe Browsing via a malicious file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11267 Insufficient policy enforcement in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to bypass content security policy via a crafted Chrome Extension. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11269 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker in a privileged network position to execute arbitrary code inside a sandbox via a crafted Chrome Extension. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11271 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11273 Insufficient validation of untrusted input in Omnibox in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who convinced a user to engage in specific UI gestures to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11276 Inappropriate implementation in Cast in Google Chrome prior to 149.0.7827.53 allowed an attacker on the local network segment to bypass discretionary access control via malicious network traffic. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11279 Out of bounds read in DevTools in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11282 Insufficient policy enforcement in Sandbox in Google Chrome on Linux prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11284 Side-channel information leakage in PerformanceAPIs in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11286 Insufficient validation of untrusted input in Wallet in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11288 Insufficient policy enforcement in CSS in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11289 Side-channel information leakage in Paint in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11292 Insufficient policy enforcement in Blink in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to bypass content security policy via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11293 Use after free in Input in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11294 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11296 Inappropriate implementation in ImageCapture in Google Chrome prior to 149.0.7827.53 allowed a remote attacker who had compromised the renderer process to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11299 Integer overflow in Fonts in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11300 Inappropriate implementation in Permissions in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11301 Inappropriate implementation in LiveCaption in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially perform out of bounds memory access via malicious network traffic. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11303 Use after free in PDFium in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11304 Use after free in PDFium in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11305 Use after free in PDFium in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11306 Use after free in PDFium in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11307 Use after free in PDFium in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11308 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.53 allowed an attacker who convinced a user to install a malicious extension to perform privilege escalation via a crafted Chrome Extension. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11309 Insufficient policy enforcement in History in Google Chrome prior to 149.0.7827.53 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdwdataviz
runtimedataviz

CVE-2026-11331 An attacker who knows (or guesses) that a resolver uses RPZ with wildcard CNAME policies can craft query names long enough to trigger a NAMETOOLONG error condition during RPZ processing. This is not handled correctly and may lead to defeating the RPZ rule. It also may lead to an unexpected exit of the BIND 9 software. This issue affects BIND 9 versions 9.16.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.16.8-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-11525 Impact: When undici parses a Set-Cookie header, it accepts any SameSite attribute value that contains Strict, Lax, or None as a substring, rather than the case-insensitive exact match specified by RFC 6265. Non-spec values are silently mapped to one of the three standard tokens. For example, SameSite=NoneOfYourBusiness is parsed as None (the most permissive setting), and SameSite=StrictLax is parsed as Lax (a downgrade from Strict). Affected applications are those that consume Set-Cookie headers from server responses (for example via undici's fetch or proxy code paths) and then forward or rely on the parsed sameSite attribute. A malicious or non-compliant server can coerce the consumer's view of a cookie's SameSite policy to a weaker value, silently degrading the SameSite enforcement the cookie is supposed to provide. This was introduced in undici 5.15.0 when the cookies feature was added. Patches: Upgrade to undici v6.26.0, v7.28.0 or v8.5.0. Workarounds: After parsing a Set-Cookie header, validate that the resulting sameSite attribute is one of 'Strict', 'Lax', or 'None' (exact, case-insensitive) before forwarding or relying on it.

cdsw-s2i-registry
cdsw-web
cloudera-ai-rag-studio
dpsgateway
hue

CVE-2026-11622 A DNSSEC validating resolver that is under a random subdomain attack against a DNSSEC-signed zone can suffer from runaway memory usage. The attacker needs to be able to send queries faster than the resolver can perform validation. The increased memory usage can be orders of magnitude beyond the limit configured in the `max-cache-size` parameter. This issue affects BIND 9 versions 9.11.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-11628 Use after free in Ozone in Google Chrome prior to 149.0.7827.103 allowed a local attacker to potentially exploit heap corruption via physical access to the device. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11629 Use after free in Ozone in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11630 Use after free in File Input in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11632 Use after free in TabStrip in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11638 Use after free in Printing in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11640 Integer overflow in libyuv in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11642 Use after free in Web Apps in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11643 Use after free in Proxy in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11644 Use after free in Views in Google Chrome on Linux prior to 149.0.7827.103 allowed an attacker who convinced a user to install a malicious extension to execute arbitrary code via a crafted Chrome Extension. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-11645 Out of bounds read and write in V8 in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11646 Use after free in ViewTransitions in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11649 Use after free in V8 in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11650 Use after free in V8 in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11651 Use after free in Network in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11652 Use after free in Extensions in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11653 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11656 Use after free in ServiceWorker in Google Chrome prior to 149.0.7827.103 allowed an attacker who convinced a user to install a malicious extension to potentially perform a sandbox escape via a crafted Chrome Extension. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11658 Insufficient validation of untrusted input in Extensions in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11659 Integer overflow in UI in Google Chrome on Linux prior to 149.0.7827.103 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11660 Insufficient validation of untrusted input in New Tab Page in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11662 Type Confusion in Bindings in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11663 Use after free in Skia in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11664 Use after free in Payments in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11666 Insufficient validation of untrusted input in Input in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11667 Out of bounds read in WebRTC in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the GPU process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11668 Uninitialized Use in Codecs in Google Chrome on Linux, ChromeOS prior to 149.0.7827.103 allowed a remote attacker to leak cross-origin data via a crafted video file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11670 Use after free in PDF in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted PDF file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11671 Use after free in Navigation in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11673 Use after free in InterestGroups in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11674 Use after free in Guest View in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11675 Out of bounds read in Skia in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11676 Insufficient validation of untrusted input in Dawn in Google Chrome on Linux and ChromeOS prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11678 Integer overflow in libyuv in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11681 Use after free in Ozone in Google Chrome on Linux prior to 149.0.7827.103 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11682 Inappropriate implementation in Views in Google Chrome on Linux prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11683 Use after free in WebCodecs in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11684 Insufficient policy enforcement in Network in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the utility process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11688 Inappropriate implementation in SVG in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11689 Insufficient policy enforcement in Passwords in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11691 Insufficient validation of untrusted input in New Tab Page in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11692 Use after free in Read Anything in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11693 Inappropriate implementation in Plugins in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11694 Use after free in ServiceWorker in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11695 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11697 Insufficient validation of untrusted input in UI in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-11700 Use after free in Tracing in Google Chrome prior to 149.0.7827.103 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
runtimedataviz

CVE-2026-11701 Inappropriate implementation in Guest View in Google Chrome prior to 149.0.7827.103 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdwdataviz
runtimedataviz

CVE-2026-11721 It is possible for an attacker's zone to respond to a query with an RRSIG that has a smaller number of labels than the zone in which the RRSIG is contained. This causes `named` to produce a wildcard name for a zone that is shorter than the attacker's zone, which can result in cache poisoning. For this attack to have any effect, the resolver under attack must have set `synth-from-dnssec yes;` (which is the default). This issue affects BIND 9 versions 9.11.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-11979 libxml2 is vulnerable to multiple stack-based buffer overflows in the xmlcatalog utility when running in --shell mode. The usershell() function processes user input using fixed-size stack buffers without proper bounds checking. By supplying an overly long input line, an attacker can overflow internal buffers (command, arg, and argv) during input parsing. This results in memory corruption within the stack frame. Successful exploitation may cause a crash or potentially allow arbitrary code execution in the context of the xmlcatalog process. This issue has been fixed in the commit c2e233fc. NOTE: The maintainers of this project did not agree that this issue is a vulnerability and considered it a bug.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-12007 Use after free in Core in Google Chrome on Windows prior to 149.0.7827.115 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12008 Use after free in DigitalCredentials in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdwdataviz
runtimedataviz

CVE-2026-12011 Use after free in WebMIDI in Google Chrome on Windows prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12012 Use after free in Network in Google Chrome prior to 149.0.7827.115 allowed an attacker in a privileged network position to potentially exploit heap corruption via malicious network traffic. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12014 Use after free in Cast in Google Chrome prior to 149.0.7827.115 allowed an attacker on the local network segment to potentially perform a sandbox escape via malicious network traffic. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12015 Use after free in Autofill in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12016 Inappropriate implementation in DevTools in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12017 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12018 Inappropriate implementation in Mojo in Google Chrome on Windows prior to 149.0.7827.115 allowed a local attacker to perform OS-level privilege escalation via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-12019 Heap buffer overflow in Codecs in Google Chrome on Linux and ChromeOS prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12024 Insufficient policy enforcement in DevTools in Google Chrome prior to 149.0.7827.115 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12025 Insufficient validation of untrusted input in Network in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12027 Inappropriate implementation in Headless in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12029 Use after free in Video in Google Chrome on Windows prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12031 Inappropriate implementation in Views in Google Chrome on Windows prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12033 Out of bounds read in VideoCapture in Google Chrome prior to 149.0.7827.115 allowed a remote attacker who had compromised the GPU process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12034 Insufficient validation of untrusted input in Linux Toolkit Theming in Google Chrome on Linux prior to 149.0.7827.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a malicious file. (Chromium security severity: High)

cdwdataviz
runtimedataviz

CVE-2026-12035 Use after free in Views in Google Chrome on Windows prior to 149.0.7827.115 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12143 form-data is a library for creating readable multipart/form-data streams. In versions through 4.0.5, the `field` argument to `FormData#append` and the `filename` option are concatenated verbatim into the `Content-Disposition` header without escaping carriage return (CR), line feed (LF), or double-quote (") characters. An application that passes attacker-controlled data as a field name or filename (for example, an API gateway that turns JSON object keys into multipart field names) allows the attacker to terminate the header line and inject additional headers, or to smuggle entire additional multipart parts, into the request the application forwards to a backend. This can let the attacker add or override form fields (e.g. set `is_admin=true`) seen by the downstream parser. This is an instance of CWE-93 (CRLF injection). The fix escapes CR, LF, and `"` as `%0D`, `%0A`, and `%22` in field names and filenames, matching the serialization browsers use per the WHATWG HTML multipart/form-data encoding algorithm. Exploitation requires the consuming application to use untrusted input as a field name or filename; applications that use only fixed/trusted field names are not affected. Fixed in 2.5.6, 3.0.5, and 4.0.6.

cdsw-web
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-12151 Impact: The undici WebSocket client enforces maxPayloadSize on the cumulative byte count of fragments in a message but does not enforce a limit on the number of fragments. A malicious WebSocket server can stream many small or empty continuation frames that each pass per-frame and cumulative-size validation, collectively causing unbounded memory growth in the client process. The result is memory exhaustion and a denial of service. Affected applications are those using the undici WebSocket client (new WebSocket(...)) or the WebSocketStream API that can be induced to connect to an attacker-controlled or compromised WebSocket endpoint. All releases starting at undici 6.17.0 are affected. Patches: Upgrade to undici >= 6.26.0, >= 7.28.0, or >= 8.5.0. Workarounds: No workaround is available. The fix must be applied through an upgrade.

cdsw-s2i-registry
cdsw-web
cloudera-ai-rag-studio
dpsgateway
hue

CVE-2026-12437 Use after free in WebShare in Google Chrome on Windows prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12438 Inappropriate implementation in WebView in Google Chrome on Android prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12439 Use after free in Digital Credentials in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12440 Use after free in DigitalCredentials in Google Chrome on Windows prior to 149.0.7827.155 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12441 Use after free in File Input in Google Chrome on Linux prior to 149.0.7827.155 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12442 Use after free in Passwords in Google Chrome on Android prior to 149.0.7827.155 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12443 Use after free in Web Authentication in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-12444 Out of bounds read in Chromoting in Google Chrome on Windows prior to 149.0.7827.155 allowed a local attacker to obtain potentially sensitive information from process memory via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-12445 Use after free in Extensions in Google Chrome prior to 149.0.7827.155 allowed an attacker who convinced a user to install a malicious extension to potentially exploit heap corruption via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-12446 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12447 Heap buffer overflow in WebRTC in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12448 Inappropriate implementation in WebView in Google Chrome on Android prior to 149.0.7827.155 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12449 Use after free in Chromoting in Google Chrome on Windows prior to 149.0.7827.155 allowed a local attacker to perform OS-level privilege escalation via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-12450 Inappropriate implementation in Media in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12451 Use after free in DigitalCredentials in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12452 Use after free in Downloads in Google Chrome on Android prior to 149.0.7827.155 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12453 Insufficient validation of untrusted input in Input in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12454 Race in Safe Browsing in Google Chrome on Mac prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12455 Use after free in Tab Strip in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12456 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.155 allowed an attacker who convinced a user to install a malicious extension to bypass same origin policy via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-12457 Inappropriate implementation in Extensions in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12458 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12459 Inappropriate implementation in Serial in Google Chrome prior to 149.0.7827.155 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12460 Insufficient policy enforcement in File System Access in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted PDF file. (Chromium security severity: High)

cdsw-web

CVE-2026-12461 Out of bounds read in WebRTC in Google Chrome on Windows prior to 149.0.7827.155 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12462 Use after free in Media in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12463 Inappropriate implementation in Views in Google Chrome on Linux prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12464 Use after free in Browser in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12465 Object lifecycle issue in Metrics in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12466 Heap buffer overflow in WebRTC in Google Chrome on Windows prior to 149.0.7827.155 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12467 Use after free in Extensions in Google Chrome prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12468 Race in Updater in Google Chrome on Mac prior to 149.0.7827.155 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12469 Uninitialized Use in GPU in Google Chrome on Android prior to 149.0.7827.155 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-12617 The issue is unexpected program termination based on ordering and/or specific content in responses to queries for CNAME or DNAME, and A records. Specifically, if a client queries for a DNAME and A record below the DNAME to the resolver, and the authoritative server responds positively to the A query but delays the DNAME response and later responds negatively, `named` may quit unexpectedly. Or, if a client queries for a CNAME and A record for the same name to the resolver, and the authoritative server responds positively to the A query but delays the CNAME response and later responds with a self-referential CNAME, the same failure may occur. This issue affects BIND 9 versions 9.18.0 through 9.18.50, 9.20.0 through 9.20.24, 9.18.11-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-13021 Inappropriate implementation in DeviceBoundSessionCredentials in Google Chrome prior to 149.0.7827.197 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13022 Inappropriate implementation in Autofill in Google Chrome prior to 149.0.7827.197 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13023 Uninitialized Use in GPU in Google Chrome prior to 149.0.7827.197 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13024 Insufficient validation of untrusted input in Navigation in Google Chrome prior to 149.0.7827.197 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13025 Race in DevTools in Google Chrome prior to 149.0.7827.197 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13026 Use after free in Digital Credentials in Google Chrome on Mac prior to 149.0.7827.197 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13027 Use after free in FileSystem in Google Chrome prior to 149.0.7827.197 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13028 Use after free in WebGL in Google Chrome on Android prior to 149.0.7827.197 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13029 Use after free in Web Authentication in Google Chrome prior to 149.0.7827.197 allowed an attacker who convinced a user to install a malicious extension to potentially exploit heap corruption via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-13030 Uninitialized Use in GPU in Google Chrome on Android prior to 149.0.7827.197 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13031 Use after free in Blink in Google Chrome prior to 149.0.7827.197 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13032 Use after free in WebGL in Google Chrome on Android prior to 149.0.7827.197 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13033 Out of bounds read and write in Blink>InterestGroups in Google Chrome prior to 149.0.7827.197 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13034 Inappropriate implementation in Passwords in Google Chrome prior to 149.0.7827.197 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13035 Use after free in Bluetooth in Google Chrome on Mac prior to 149.0.7827.197 allowed a remote attacker to execute arbitrary code via a malicious peripheral. (Chromium security severity: High)

cdsw-web

CVE-2026-13036 Use after free in Blink in Google Chrome prior to 149.0.7827.197 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13037 Use after free in WebView in Google Chrome on Android prior to 149.0.7827.197 allowed a local attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13038 Use after free in Autofill in Google Chrome on Windows prior to 149.0.7827.197 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13204 If a provably insecure domain is covered by both an NSEC and NSEC3 record at the parent, and there exist an RRSIG for only one of these types, then BIND may exit unexpectedly with an assertion while validating this proof. This issue affects BIND 9 versions 9.11.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-13281 Integer overflow in Mojo in Google Chrome prior to 149.0.7827.201 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-13282 Use after free in Payments in Google Chrome on Android prior to 149.0.7827.201 allowed a local attacker to potentially exploit heap corruption via physical access to the device. (Chromium security severity: High)

cdsw-web

CVE-2026-13283 Use after free in AdFilter in Google Chrome on Android prior to 149.0.7827.201 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13321 The BIND resolver accepts validly-signed NSEC records where the "Next Domain Name" field points outside the signer's zone. This issue affects BIND 9 versions 9.11.0 through 9.18.50, 9.20.0 through 9.20.24, 9.21.0 through 9.21.23, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-13769 Overly permissive file permissions in AWS CLI before 1.44.78 (v1) and 2.34.29 (v2) on Unix-like systems where the umask has not been configured to restrict file permissions (the default on most systems) may allow other local users on the same host to read credentials written by certain CLI subcommands (aws codeartifact login, aws iam create-virtual-mfa-device, aws deploy register). To remediate this issue, users should upgrade to AWS CLI 1.44.78 (v1) or 2.34.29 (v2) or later.

model-registry

CVE-2026-13778 Use after free in WebUSB in Google Chrome on Mac prior to 150.0.7871.47 allowed a local attacker to execute arbitrary code via a malicious peripheral. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13779 Use after free in Chromoting in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13785 Use after free in Bluetooth in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13786 Use after free in Ozone in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13788 Use after free in Fullscreen in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-13789 Use after free in GPU in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13790 Side-channel information leakage in Scroll in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13792 Use after free in Touchbar in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13795 Insufficient policy enforcement in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13802 Use after free in Views in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13803 Type Confusion in Chrome Tabs in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13804 Use after free in Chromecast in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13805 Use after free in GFX in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13806 Insufficient validation of untrusted input in Accessibility in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13807 Use after free in Import in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-13808 Insufficient data validation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a local attacker to obtain potentially sensitive information from process memory via physical access to the device. (Chromium security severity: High)

cdsw-web

CVE-2026-13809 Side-channel information leakage in Safe Browsing in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13810 Inappropriate implementation in Input in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13812 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13813 Insufficient policy enforcement in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13814 Use after free in Views in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13815 Use after free in Blink in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13816 Insufficient validation of untrusted input in File Input in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13817 Insufficient validation of untrusted input in Glic in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13818 Inappropriate implementation in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13819 Out of bounds read in ANGLE in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13820 Out of bounds read in Skia in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13822 Inappropriate implementation in Extensions in Google Chrome on Android prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to bypass same origin policy via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-13823 Use after free in Glic in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13826 Inappropriate implementation in Autofill in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13827 Use after free in Updater in Google Chrome on Mac prior to 150.0.7871.47 allowed a local attacker to perform privilege escalation via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-13830 Use after free in Chromoting in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: High)

cdsw-web

CVE-2026-13832 Use after free in Headless in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13833 Uninitialized Use in ANGLE in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13834 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13835 Inappropriate implementation in XML in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13836 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13837 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13838 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13839 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13840 Insufficient policy enforcement in Canvas in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13841 Integer overflow in Skia in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13842 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13843 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13846 Use after free in USB in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13847 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13850 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a local attacker to execute arbitrary code inside a sandbox via a malicious file. (Chromium security severity: High)

cdsw-web

CVE-2026-13851 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13852 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13854 Use after free in Ozone in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13855 Use after free in Ozone in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-13856 Insufficient validation of untrusted input in Speech in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform privilege escalation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13857 Inappropriate implementation in Geometry in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13858 Out of bounds read in FFmpeg in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted video file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13862 Insufficient policy enforcement in Web Authentication (Passkeys & Security Keys) in Google Chrome on iOS prior to 150.0.7871.47 allowed an attacker in a privileged network position to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13863 Insufficient validation of untrusted input in CustomTabs in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to perform privilege escalation via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13864 Insufficient policy enforcement in WebHID in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to perform privilege escalation via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13865 Insufficient validation of untrusted input in Enterprise in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13866 Inappropriate implementation in Input in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13867 Inappropriate implementation in Geolocation in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13868 Inappropriate implementation in Network in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13870 Use after free in WebView in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13871 Insufficient policy enforcement in GuestView in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13872 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to potentially perform a sandbox escape via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13876 Inappropriate implementation in Network in Google Chrome prior to 150.0.7871.47 allowed an attacker in a privileged network position to bypass content security policy via malicious network traffic. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13878 Use after free in Bluetooth in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13880 Use after free in USB in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13884 Integer overflow in Chromecast in Google Chrome prior to 150.0.7871.47 allowed a local attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13885 Use after free in Skia in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13887 Inappropriate implementation in NFC in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13889 Side-channel information leakage in WebAuthentication in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13892 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13893 Insufficient validation of untrusted input in WebUI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via malicious network traffic. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13895 Inappropriate implementation in Autofill in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13897 Insufficient policy enforcement in Chromecast in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13898 Use after free in Cast Receiver in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13899 Use after free in HTML in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13900 Inappropriate implementation in Chromecast in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13901 Insufficient policy enforcement in Serial in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13902 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13903 Insufficient policy enforcement in Bluetooth in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13904 Inappropriate implementation in Safe Browsing in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13905 Race in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a local attacker to obtain potentially sensitive information from process memory via physical access to the device. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13906 Out of bounds read in Codecs in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13907 Inappropriate implementation in iOSWeb in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13908 Insufficient validation of untrusted input in Omnibox in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to bypass navigation restrictions via malicious network traffic. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13909 Insufficient policy enforcement in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13910 Insufficient policy enforcement in WebXR in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13911 Insufficient policy enforcement in Spellcheck in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13912 Inappropriate implementation in Safe Browsing in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13913 Insufficient policy enforcement in Autofill in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13914 Inappropriate implementation in Passwords in Google Chrome on Mac prior to 150.0.7871.47 allowed a local attacker to obtain potentially sensitive information from process memory via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13915 Use after free in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13916 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13917 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13918 Use after free in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13919 Insufficient policy enforcement in Extensions in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13921 Insufficient validation of untrusted input in DeviceBoundSessionCredentials in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13922 Side-channel information leakage in Paint in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13923 Uninitialized Use in GPU in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13924 Insufficient validation of untrusted input in WebView in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13926 Insufficient validation of untrusted input in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13927 Insufficient validation of untrusted input in UI in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to perform privilege escalation via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13928 Insufficient validation of untrusted input in Enterprise in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13929 Insufficient policy enforcement in DevTools in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to bypass navigation restrictions via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13930 Insufficient policy enforcement in Actor in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13932 Inappropriate implementation in Sharing in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13933 Insufficient policy enforcement in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13934 Insufficient validation of untrusted input in Dawn in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13935 Side-channel information leakage in ComputePressure in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13936 Inappropriate implementation in Passwords in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13937 Insufficient policy enforcement in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13938 Integer overflow in Fonts in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform an out of bounds memory write via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13939 Insufficient validation of untrusted input in WebShare in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13940 Uninitialized Use in Cast in Google Chrome prior to 150.0.7871.47 allowed an attacker on the local network segment to obtain potentially sensitive information from process memory via malicious network traffic. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13941 Inappropriate implementation in SiteSettings in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13942 Inappropriate implementation in Video Capture in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed a local attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13943 Uninitialized Use in CSS in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13944 Inappropriate implementation in DataTransfer in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13945 Insufficient policy enforcement in Extensions in Google Chrome on Linux prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to perform UI spoofing via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13946 Inappropriate implementation in ScriptInjections in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13947 Uninitialized Use in XR in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13948 Insufficient policy enforcement in Extensions in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to perform UI spoofing via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13949 Insufficient policy enforcement in Payments in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13950 Uninitialized Use in GPU in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13951 Insufficient policy enforcement in USB in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13952 Inappropriate implementation in PerformanceAPIs in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13953 Inappropriate implementation in SplitView in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13954 Insufficient policy enforcement in XML in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13955 Insufficient validation of untrusted input in CustomTabs in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to perform UI spoofing via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13956 Incorrect security UI in PageInfo in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13957 Incorrect security UI in Extensions in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13959 Insufficient validation of untrusted input in Blink in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13960 Inappropriate implementation in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13962 Insufficient data validation in PDF in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13963 Inappropriate implementation in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13964 Insufficient policy enforcement in WebView in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13965 Use after free in Oilpan in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13966 Inappropriate implementation in History in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13967 Heap buffer overflow in V8 in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13968 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13969 Uninitialized Use in UI in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13972 Inappropriate implementation in Paint in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13974 Integer overflow in Safe Browsing in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a malicious file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13975 Out of bounds read in ANGLE in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13976 Insufficient data validation in Storage in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13977 Inappropriate implementation in HTMLParser in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13978 Insufficient policy enforcement in PageInfo in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13979 Inappropriate implementation in Paint in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13980 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13981 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13982 Incorrect security UI in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13983 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13984 Incorrect security UI in TabStrip in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13985 Inappropriate implementation in MediaCapture in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13986 Inappropriate implementation in Media UI in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13987 Incorrect security UI in Mobile in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13988 Inappropriate implementation in Paint in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13989 Inappropriate implementation in PageInfo in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13991 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13992 Inappropriate implementation in UI in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13994 Inappropriate implementation in Credential Management in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13995 Insufficient validation of untrusted input in Autofill in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13996 Inappropriate implementation in Permissions in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13997 Incorrect security UI in Extensions in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13998 Incorrect security UI in File Input in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-13999 Insufficient validation of untrusted input in Extensions in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to perform UI spoofing via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14000 Inappropriate implementation in XML in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14001 Inappropriate implementation in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14002 Inappropriate implementation in Geolocation in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14003 Insufficient policy enforcement in Extensions in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to leak cross-origin data via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14004 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14005 Use after free in Omnibox in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14006 Use after free in Navigation in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14007 Insufficient policy enforcement in PermissionsPolicy in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14008 Uninitialized Use in WebXR in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14009 Inappropriate implementation in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14011 Out of bounds read in SurfaceCapture in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14013 Inappropriate implementation in SVG in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14014 Inappropriate implementation in Paint in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14016 Inappropriate implementation in SVG in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14017 Inappropriate implementation in Navigation in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14019 Inappropriate implementation in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14020 Insufficient validation of untrusted input in WebXR in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14021 Insufficient policy enforcement in StorageAccessAPI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14022 Insufficient validation of untrusted input in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14023 Insufficient validation of untrusted input in SanitizerAPI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14024 Use after free in Ozone in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14025 Use after free in Views in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14027 Use after free in SignIn in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14028 Incorrect security UI in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14030 Inappropriate implementation in SplitView in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14032 Use after free in Bluetooth in Google Chrome on Mac prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to execute arbitrary code via a crafted Chrome Extension. (Chromium security severity: Low)

cdsw-web

CVE-2026-14034 Inappropriate implementation in WebXR in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14036 Insufficient policy enforcement in Bluetooth in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14037 Insufficient policy enforcement in GPU in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14038 Insufficient validation of untrusted input in New Tab Page in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14039 Insufficient policy enforcement in GetUserMedia in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14040 Use after free in BrowserTag in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to potentially exploit heap corruption via a crafted Chrome Extension. (Chromium security severity: Low)

cdsw-web

CVE-2026-14041 Insufficient policy enforcement in Serial in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14042 Inappropriate implementation in Isolated Web Apps in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14043 Use after free in GetUserMedia in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14044 Use after free in ANGLE in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14045 Insufficient validation of untrusted input in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14046 Inappropriate implementation in CustomTabs in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14047 Insufficient policy enforcement in Extensions in Google Chrome prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to bypass content security policy via a crafted Chrome Extension. (Chromium security severity: Low)

cdsw-web

CVE-2026-14050 Insufficient policy enforcement in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14052 Insufficient policy enforcement in FileSystem in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass discretionary access control via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14053 Insufficient policy enforcement in Extensions in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14054 Insufficient policy enforcement in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14056 Insufficient validation of untrusted input in Media in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted video file. (Chromium security severity: Low)

cdsw-web

CVE-2026-14057 Inappropriate implementation in FedCM in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14058 Insufficient policy enforcement in Parser in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass content security policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14062 Inappropriate implementation in Views in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed an attacker who convinced a user to install a malicious extension to obtain potentially sensitive information from process memory via a crafted Chrome Extension. (Chromium security severity: Low)

cdsw-web

CVE-2026-14064 Use after free in PageInfo in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14066 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14067 Use after free in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14068 Inappropriate implementation in Omnibox in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14072 Inappropriate implementation in SplitView in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14074 Side-channel information leakage in WebAuthentication in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14075 Insufficient policy enforcement in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to bypass no-referrer policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14077 Inappropriate implementation in Select in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14078 Insufficient validation of untrusted input in WebRTC in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform privilege escalation via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14079 Insufficient policy enforcement in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14080 Insufficient validation of untrusted input in TabSwitcher in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to bypass navigation restrictions via malicious network traffic. (Chromium security severity: Low)

cdsw-web

CVE-2026-14082 Race in Storage in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14083 Insufficient validation of untrusted input in HTML in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14084 Insufficient validation of untrusted input in Chromoting in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to potentially exploit heap corruption via malicious network traffic. (Chromium security severity: Low)

cdsw-web

CVE-2026-14085 Side-channel information leakage in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14086 Insufficient policy enforcement in HID in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14088 Uninitialized Use in Canvas in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14089 Insufficient validation of untrusted input in PopupBlocker in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14090 Insufficient validation of untrusted input in CameraCapture in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14096 Inappropriate implementation in Input in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14097 Inappropriate implementation in WebAppInstalls in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14099 Use after free in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14101 Insufficient policy enforcement in Sandbox in Google Chrome on Mac prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14102 Use after free in Passwords in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14103 Use after free in SSL in Google Chrome on ChromeOS prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14104 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14105 Insufficient policy enforcement in Speech in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14106 Insufficient validation of untrusted input in Text in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14109 Insufficient policy enforcement in Mojo in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14110 Inappropriate implementation in DarkMode in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14114 Inappropriate implementation in WebAppInstalls in Google Chrome on Android prior to 150.0.7871.47 allowed a local attacker to perform UI spoofing via a malicious file. (Chromium security severity: Low)

cdsw-web

CVE-2026-14116 Insufficient validation of untrusted input in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14118 Insufficient data validation in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14120 Inappropriate implementation in DevTools in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14121 Use after free in Chromoting in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via malicious network traffic. (Chromium security severity: Low)

cdsw-web

CVE-2026-14123 Incorrect security UI in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14126 Incorrect security UI in UI in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform domain spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14127 Inappropriate implementation in Printing in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14128 Inappropriate implementation in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to spoof the contents of the Omnibox (URL bar) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14129 Inappropriate implementation in PreviewTab in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14130 Incorrect security UI in Omnibox in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14131 Insufficient validation of untrusted input in WebAppInstalls in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14132 Inappropriate implementation in WebXR in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14133 Race in History Embeddings in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14134 Inappropriate implementation in Autofill in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14135 Insufficient validation of untrusted input in Network in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14136 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14137 Insufficient validation of untrusted input in Chrome for iOS in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker who convinced a user to engage in specific UI gestures to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14140 Insufficient validation of untrusted input in Input in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14141 Incorrect security UI in Document Picture-in-Picture in Google Chrome on Android prior to 150.0.7871.47 allowed a remote attacker to perform domain spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14142 Inappropriate implementation in Extensions in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14143 Incorrect security UI in Passwords in Google Chrome on iOS prior to 150.0.7871.47 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14145 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14146 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14147 Inappropriate implementation in CSS in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14149 Use after free in Audio in Google Chrome on Linux prior to 150.0.7871.47 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14150 Insufficient validation of untrusted input in Speech in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14151 Inappropriate implementation in AI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14152 Out of bounds read and write in ANGLE in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14155 Insufficient policy enforcement in StorageAccessAPI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14156 Insufficient policy enforcement in StorageAccessAPI in Google Chrome prior to 150.0.7871.47 allowed a remote attacker who had compromised the renderer process to bypass same origin policy via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14381 Incorrect security UI in WebAppInstalls in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to perform UI spoofing via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14382 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14383 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14385 Heap buffer overflow in ANGLE in Google Chrome on Mac prior to 150.0.7871.46 allowed a remote attacker to perform out of bounds memory access via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14386 Out of bounds read in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14387 Integer overflow in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14388 Out of bounds read in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14389 Integer overflow in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14390 Use after free in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14392 Out of bounds write in Tint in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14393 Use after free in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14394 Use after free in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14395 Out of bounds write in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14396 Out of bounds read in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14397 Out of bounds write in ANGLE in Google Chrome on Mac prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14398 Use after free in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-14399 Uninitialized Use in Dawn in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14400 Out of bounds write in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14401 Insufficient validation of untrusted input in ANGLE in Google Chrome on Android prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14403 Use after free in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14404 Inappropriate implementation in PDFium in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to perform UI spoofing via a crafted PDF file. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14405 Uninitialized Use in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14406 Out of bounds read in V8 in Google Chrome prior to 150.0.7871.46 allowed an attacker who convinced a user to install a malicious extension to obtain potentially sensitive information from process memory via a crafted Chrome Extension. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14407 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14408 Uninitialized Use in Dawn in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14409 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14410 Inappropriate implementation in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to perform UI spoofing via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14411 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14412 Insufficient validation of untrusted input in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14413 Uninitialized Use in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14414 Insufficient validation of untrusted input in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14415 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14416 Out of bounds read in Dawn in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

cdsw-web

CVE-2026-14417 Use after free in Dawn in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-14418 Uninitialized Use in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14419 Use after free in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-14420 Out of bounds read and write in Dawn in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-14421 Uninitialized Use in Dawn in Google Chrome on ChromeOS prior to 150.0.7871.46 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-14422 Out of bounds read and write in Tint in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14423 Type Confusion in Tint in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14424 Use after free in Dawn in Google Chrome on Mac prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14425 Use after free in ANGLE in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14426 Use after free in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who convinced a user to engage in specific UI gestures to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14427 Heap buffer overflow in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-14428 Insufficient validation of untrusted input in Dawn in Google Chrome on Android prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14429 Insufficient validation of untrusted input in Skia in Google Chrome prior to 150.0.7871.46 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14430 Integer overflow in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14431 Type Confusion in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-14432 Use after free in V8 in Google Chrome prior to 150.0.7871.46 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-15028 A flaw was found in libarchive. This vulnerability allows a remote attacker to trigger a heap overflow by providing a specially crafted tar archive. The issue occurs during the parsing of a PAX extended header containing a malformed SUN.holesdata sparse-file attribute. Successful exploitation could lead to a denial of service, making the system unavailable, or potentially allow for arbitrary code execution, giving the attacker control over the affected system.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-15107 Use after free in IndexedDB in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-15108 Integer overflow in Extensions API in Google Chrome prior to 150.0.7871.115 allowed an attacker who convinced a user to install a malicious extension to perform an out of bounds memory read via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-15109 Uninitialized Use in ANGLE in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15110 Use after free in Extensions in Google Chrome prior to 150.0.7871.115 allowed an attacker who convinced a user to install a malicious extension to potentially exploit heap corruption via a crafted Chrome Extension. (Chromium security severity: High)

cdsw-web

CVE-2026-15111 Use after free in Views in Google Chrome prior to 150.0.7871.115 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15112 Use after free in Ozone in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-15114 Out of bounds read and write in Codecs in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to potentially exploit heap corruption via a crafted video file. (Chromium security severity: High)

cdsw-web

CVE-2026-15116 Use after free in Actor in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15117 Use after free in Payments in Google Chrome prior to 150.0.7871.115 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15118 Use after free in Input in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15119 Race in GetUserMedia in Google Chrome prior to 150.0.7871.115 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15121 Use after free in WebRTC in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15123 Inappropriate implementation in DOM in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15124 Insufficient policy enforcement in Passwords in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15125 Inappropriate implementation in Forms in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15126 Use after free in Forms in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15127 Inappropriate implementation in WebGL in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15128 Inappropriate implementation in Forms in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to inject arbitrary scripts or HTML (UXSS) via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15129 Use after free in Views in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-15130 Insufficient policy enforcement in Navigation in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to bypass site isolation via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15131 Inappropriate implementation in Navigation in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to bypass site isolation via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-15132 Uninitialized Use in V8 in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15133 Use after free in InterestGroups in Google Chrome prior to 150.0.7871.115 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15711 A vulnerability was found in libsoup's WebSocket frame parsing implementation. The library fails to validate length rules specified in RFC 6455 §5.5, which mandates that all WebSocket control frames (e.g., PING, PONG, CLOSE) contain a payload of 125 bytes or less. A remote, unauthenticated attacker can exploit this by sending a non-compliant, oversized control frame. Because the parser handles this protocol violation improperly instead of throwing an immediate connection termination error, it triggers a internal processing crash, resulting in a remote denial of service (DoS) for applications utilizing libsoup WebSockets.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-15764 Use after free in Ozone in Google Chrome on Linux prior to 150.0.7871.125 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-15765 Use after free in Ozone in Google Chrome prior to 150.0.7871.125 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Critical)

cdsw-web

CVE-2026-15766 Uninitialized Use in Skia in Google Chrome prior to 150.0.7871.125 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15768 Insufficient policy enforcement in HTML-in-Canvas in Google Chrome prior to 150.0.7871.125 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15769 Insufficient validation of untrusted input in Linux Toolkit Theming in Google Chrome on Linux prior to 150.0.7871.125 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15770 Uninitialized Use in V8 in Google Chrome prior to 150.0.7871.125 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15772 Use after free in GPU in Google Chrome on Android prior to 150.0.7871.125 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15774 Use after free in Skia in Google Chrome prior to 150.0.7871.125 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15775 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.125 allowed a remote attacker to bypass same origin policy via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15776 Inappropriate implementation in V8 in Google Chrome prior to 150.0.7871.125 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15777 Use after free in UI in Google Chrome on Linux prior to 150.0.7871.125 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

cdsw-web

CVE-2026-15778 Insufficient validation of untrusted input in Navigation in Google Chrome prior to 150.0.7871.125 allowed a remote attacker who had compromised the renderer process to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: Medium)

cdsw-web

CVE-2026-16517 A signed integer overflow vulnerability was found in libarchive's ZIP writer. In the archive_write_zip_header function in archive_write_set_format_zip.c, when ZIP encryption is enabled and the entry file size is close to INT64_MAX, the addition of the encryption overhead to the entry size overflows int64_t, resulting in undefined behavior. This could lead to incorrect Zip64 extension decisions or potential memory corruption.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-21226 Deserialization of untrusted data in Azure Core shared client library for Python allows an authorized attacker to execute code over a network.

cloudera-ai-rag-studio
kserve_huggingfaceserver
kserve_storage_initializer

CVE-2026-21726 The CVE-2021-36156 fix validates the namespace parameter for path traversal sequences after a single URL decode, by double encoding, an attacker can read files at the Ruler API endpoint /loki/api/v1/rules/{namespace} Thanks to Prasanth Sundararajan for reporting this vulnerability.

dex-grafana
mon_grafana

CVE-2026-21860 Werkzeug is a comprehensive WSGI web application library. Prior to version 3.1.5, Werkzeug's safe_join function allows path segments with Windows device names that have file extensions or trailing spaces. On Windows, there are special device names such as CON, AUX, etc that are implicitly present and readable in every directory. Windows still accepts them with any file extension, such as CON.txt, or trailing spaces such as CON. This issue has been patched in version 3.1.5.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-21998 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22001 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Information Schema). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized read access to a subset of MySQL Server accessible data. CVSS 3.1 Base Score 2.7 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:L/I:N/A:N).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22002 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22004 Vulnerability in the MySQL Server product of Oracle MySQL (component: InnoDB). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22005 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22009 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22015 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Information Schema). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized read access to a subset of MySQL Server accessible data. CVSS 3.1 Base Score 4.3 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22017 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-22020 CVE-2026-22020

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-22690 pypdf is a free and open-source pure-python PDF library. Prior to version 6.6.0, pypdf has possible long runtimes for missing /Root object with large /Size values. An attacker who uses this vulnerability can craft a PDF which leads to possibly long runtimes for actually invalid files. This can be achieved by omitting the /Root entry in the trailer, while using a rather large /Size value. Only the non-strict reading mode is affected. This issue has been patched in version 6.6.0.

cloudera-ai-rag-studio

CVE-2026-22691 pypdf is a free and open-source pure-python PDF library. Prior to version 6.6.0, pypdf has possible long runtimes for malformed startxref. An attacker who uses this vulnerability can craft a PDF which leads to possibly long runtimes for invalid startxref entries. When rebuilding the cross-reference table, PDF files with lots of whitespace characters become problematic. Only the non-strict reading mode is affected. Only the non-strict reading mode is affected. This issue has been patched in version 6.6.0.

cloudera-ai-rag-studio

CVE-2026-22740 A WebFlux server application that processes multipart requests creates temp files for parts larger than 10 K. Under some circumstances, temp files may remain not deleted after the request is fully processed. This allows an attacker to consume available disk space. Older, unsupported versions are also affected.

dex-pipelines-api-server

CVE-2026-22746 Vulnerability in Spring Spring Security. If an application is using the UserDetails#isEnabled, #isAccountNonExpired, or #isAccountNonLocked user attributes, to enable, expire, or lock users, then DaoAuthenticationProvider's timing attack defense can be bypassed for users who are disabled, expired, or locked.This issue affects Spring Security: from 5.7.0 through 5.7.22, from 5.8.0 through 5.8.24, from 6.3.0 through 6.3.15, from 6.5.0 through 6.5.9, from 7.0.0 through 7.0.4.

thunderhead-consoleauthenticationcdp

CVE-2026-22751 Vulnerability in Spring Spring Security. Applications that explicitly configure One-Time Token login with JdbcOneTimeTokenService are vulnerable to a Time-of-check Time-of-use (TOCTOU) race condition. This issue affects Spring Security: from 6.4.0 through 6.4.15, from 6.5.0 through 6.5.9, from 7.0.0 through 7.0.4.

thunderhead-consoleauthenticationcdp

CVE-2026-22772 Fulcio is a certificate authority for issuing code signing certificates for an OpenID Connect (OIDC) identity. Prior to 1.8.5, Fulcio's metaRegex() function uses unanchored regex, allowing attackers to bypass MetaIssuer URL validation and trigger SSRF to arbitrary internal services. Since the SSRF only can trigger GET requests, the request cannot mutate state. The response from the GET request is not returned to the caller so data exfiltration is not possible. A malicious actor could attempt to probe an internal network through Blind SSRF. This vulnerability is fixed in 1.8.5.

cdsw-s2i-builder-buildah

CVE-2026-22807 vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacker-controlled Python code in a model repo/path to execute at server startup. An attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve arbitrary code execution on the vLLM host during model load. This happens before any request handling and does not require API access. Version 0.14.0 fixes the issue.

nemotron_nano_12b_v2_vl_v150
nim-bigcode-starcoder2-7b-v1.15.3
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-nvidia-nemotron-3-nano-v1.7.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-23344 In the Linux kernel, the following vulnerability has been resolved: crypto: ccp - Fix use-after-free on error path In the error path of sev_tsm_init_locked(), the code dereferences 't' after it has been freed with kfree(). The pr_err() statement attempts to access t->tio_en and t->tio_init_done after the memory has been released. Move the pr_err() call before kfree(t) to access the fields while the memory is still valid. This issue reported by Smatch static analyser

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-23346 In the Linux kernel, the following vulnerability has been resolved: arm64: io: Extract user memory type in ioremap_prot() The only caller of ioremap_prot() outside of the generic ioremap() implementation is generic_access_phys(), which passes a 'pgprot_t' value determined from the user mapping of the target 'pfn' being accessed by the kernel. On arm64, the 'pgprot_t' contains all of the non-address bits from the pte, including the permission controls, and so we end up returning a new user mapping from ioremap_prot() which faults when accessed from the kernel on systems with PAN: | Unable to handle kernel read from unreadable memory at virtual address ffff80008ea89000 | ... | Call trace: | __memcpy_fromio+0x80/0xf8 | generic_access_phys+0x20c/0x2b8 | __access_remote_vm+0x46c/0x5b8 | access_remote_vm+0x18/0x30 | environ_read+0x238/0x3e8 | vfs_read+0xe4/0x2b0 | ksys_read+0xcc/0x178 | __arm64_sys_read+0x4c/0x68 Extract only the memory type from the user 'pgprot_t' in ioremap_prot() and assert that we're being passed a user mapping, to protect us against any changes in future that may require additional handling. To avoid falsely flagging users of ioremap(), provide our own ioremap() macro which simply wraps __ioremap_prot().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-23390 In the Linux kernel, the following vulnerability has been resolved: tracing/dma: Cap dma_map_sg tracepoint arrays to prevent buffer overflow The dma_map_sg tracepoint can trigger a perf buffer overflow when tracing large scatter-gather lists. With devices like virtio-gpu creating large DRM buffers, nents can exceed 1000 entries, resulting in: phys_addrs: 1000 * 8 bytes = 8,000 bytes dma_addrs: 1000 * 8 bytes = 8,000 bytes lengths: 1000 * 4 bytes = 4,000 bytes Total: ~20,000 bytes This exceeds PERF_MAX_TRACE_SIZE (8192 bytes), causing: WARNING: CPU: 0 PID: 5497 at kernel/trace/trace_event_perf.c:405 perf buffer not large enough, wanted 24620, have 8192 Cap all three dynamic arrays at 128 entries using min() in the array size calculation. This ensures arrays are only as large as needed (up to the cap), avoiding unnecessary memory allocation for small operations while preventing overflow for large ones. The tracepoint now records the full nents/ents counts and a truncated flag so users can see when data has been capped. Changes in v2: - Use min(nents, DMA_TRACE_MAX_ENTRIES) for dynamic array sizing instead of fixed DMA_TRACE_MAX_ENTRIES allocation (feedback from Steven Rostedt) - This allocates only what's needed up to the cap, avoiding waste for small operations Reviwed-by: Sean Anderson <sean.anderson@linux.dev>

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-23427 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix use-after-free in durable v2 replay of active file handles parse_durable_handle_context() unconditionally assigns dh_info->fp->conn to the current connection when handling a DURABLE_REQ_V2 context with SMB2_FLAGS_REPLAY_OPERATION. ksmbd_lookup_fd_cguid() does not filter by fp->conn, so it returns file handles that are already actively connected. The unconditional overwrite replaces fp->conn, and when the overwriting connection is subsequently freed, __ksmbd_close_fd() dereferences the stale fp->conn via spin_lock(&fp->conn->llist_lock), causing a use-after-free. KASAN report: [ 7.349357] ================================================================== [ 7.349607] BUG: KASAN: slab-use-after-free in _raw_spin_lock+0x75/0xe0 [ 7.349811] Write of size 4 at addr ffff8881056ac18c by task kworker/1:2/108 [ 7.350010] [ 7.350064] CPU: 1 UID: 0 PID: 108 Comm: kworker/1:2 Not tainted 7.0.0-rc3+ #58 PREEMPTLAZY [ 7.350068] Hardware name: QEMU Ubuntu 24.04 PC v2 (i440FX + PIIX, arch_caps fix, 1996), BIOS 1.16.3-debian-1.16.3-2 04/01/2014 [ 7.350070] Workqueue: ksmbd-io handle_ksmbd_work [ 7.350083] Call Trace: [ 7.350087] <TASK> [ 7.350087] dump_stack_lvl+0x64/0x80 [ 7.350094] print_report+0xce/0x660 [ 7.350100] ? __pfx__raw_spin_lock_irqsave+0x10/0x10 [ 7.350101] ? __pfx___mod_timer+0x10/0x10 [ 7.350106] ? _raw_spin_lock+0x75/0xe0 [ 7.350108] kasan_report+0xce/0x100 [ 7.350109] ? _raw_spin_lock+0x75/0xe0 [ 7.350114] kasan_check_range+0x105/0x1b0 [ 7.350116] _raw_spin_lock+0x75/0xe0 [ 7.350118] ? __pfx__raw_spin_lock+0x10/0x10 [ 7.350119] ? __call_rcu_common.constprop.0+0x25e/0x780 [ 7.350125] ? close_id_del_oplock+0x2cc/0x4e0 [ 7.350128] __ksmbd_close_fd+0x27f/0xaf0 [ 7.350131] ksmbd_close_fd+0x135/0x1b0 [ 7.350133] smb2_close+0xb19/0x15b0 [ 7.350142] ? __pfx_smb2_close+0x10/0x10 [ 7.350143] ? xas_load+0x18/0x270 [ 7.350146] ? _raw_spin_lock+0x84/0xe0 [ 7.350148] ? __pfx__raw_spin_lock+0x10/0x10 [ 7.350150] ? _raw_spin_unlock+0xe/0x30 [ 7.350151] ? ksmbd_smb2_check_message+0xeb2/0x24c0 [ 7.350153] ? ksmbd_tree_conn_lookup+0xcd/0xf0 [ 7.350154] handle_ksmbd_work+0x40f/0x1080 [ 7.350156] process_one_work+0x5fa/0xef0 [ 7.350162] ? assign_work+0x122/0x3e0 [ 7.350163] worker_thread+0x54b/0xf70 [ 7.350165] ? __pfx_worker_thread+0x10/0x10 [ 7.350166] kthread+0x346/0x470 [ 7.350170] ? recalc_sigpending+0x19b/0x230 [ 7.350176] ? __pfx_kthread+0x10/0x10 [ 7.350178] ret_from_fork+0x4fb/0x6c0 [ 7.350183] ? __pfx_ret_from_fork+0x10/0x10 [ 7.350185] ? __switch_to+0x36c/0xbe0 [ 7.350188] ? __pfx_kthread+0x10/0x10 [ 7.350190] ret_from_fork_asm+0x1a/0x30 [ 7.350197] </TASK> [ 7.350197] [ 7.355160] Allocated by task 123: [ 7.355261] kasan_save_stack+0x33/0x60 [ 7.355373] kasan_save_track+0x14/0x30 [ 7.355484] __kasan_kmalloc+0x8f/0xa0 [ 7.355593] ksmbd_conn_alloc+0x44/0x6d0 [ 7.355711] ksmbd_kthread_fn+0x243/0xd70 [ 7.355839] kthread+0x346/0x470 [ 7.355942] ret_from_fork+0x4fb/0x6c0 [ 7.356051] ret_from_fork_asm+0x1a/0x30 [ 7.356164] [ 7.356214] Freed by task 134: [ 7.356305] kasan_save_stack+0x33/0x60 [ 7.356416] kasan_save_track+0x14/0x30 [ 7.356527] kasan_save_free_info+0x3b/0x60 [ 7.356646] __kasan_slab_free+0x43/0x70 [ 7.356761] kfree+0x1ca/0x430 [ 7.356862] ksmbd_tcp_disconnect+0x59/0xe0 [ 7.356993] ksmbd_conn_handler_loop+0x77e/0xd40 [ 7.357138] kthread+0x346/0x470 [ 7.357240] ret_from_fork+0x4fb/0x6c0 [ 7.357350] ret_from_fork_asm+0x1a/0x30 [ 7.357463] [ 7.357513] The buggy address belongs to the object at ffff8881056ac000 [ 7.357513] which belongs to the cache kmalloc-1k of size 1024 [ 7.357857] The buggy address is located 396 bytes inside of [ 7.357857] freed 1024-byte region ---truncated---

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-23868 Giflib contains a double-free vulnerability that is the result of a shallow copy in GifMakeSavedImage and incorrect error handling. The conditions needed to trigger this vulnerability are difficult but may be possible.

cmlserving-triton-runtime
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-24001 jsdiff is a JavaScript text differencing implementation. Prior to versions 8.0.3, 5.2.2, 4.0.4, and 3.5.1, attempting to parse a patch whose filename headers contain the line break characters `\r`, `\u2028`, or `\u2029` can cause the `parsePatch` method to enter an infinite loop. It then consumes memory without limit until the process crashes due to running out of memory. Applications are therefore likely to be vulnerable to a denial-of-service attack if they call `parsePatch` with a user-provided patch as input. A large payload is not needed to trigger the vulnerability, so size limits on user input do not provide any protection. Furthermore, some applications may be vulnerable even when calling `parsePatch` on a patch generated by the application itself if the user is nonetheless able to control the filename headers (e.g. by directly providing the filenames of the files to be diffed). The `applyPatch` method is similarly affected if (and only if) called with a string representation of a patch as an argument, since under the hood it parses that string using `parsePatch`. Other methods of the library are unaffected. Finally, a second and lesser interdependent bug - a ReDOS - also exhibits when those same line break characters are present in a patch's *patch* header (also known as its "leading garbage"). A maliciously-crafted patch header of length *n* can take `parsePatch` O(*n*³) time to parse. Versions 8.0.3, 5.2.2, 4.0.4, and 3.5.1 contain a fix. As a workaround, do not attempt to parse patches that contain any of these characters: `\r`, `\u2028`, or `\u2029`.

cdsw-web

CVE-2026-24137 sigstore framework is a common go library shared across sigstore services and clients. In versions 1.10.3 and below, the legacy TUF client (pkg/tuf/client.go) supports caching target files to disk. It constructs a filesystem path by joining a cache base directory with a target name sourced from signed target metadata; however, it does not validate that the resulting path stays within the cache base directory. A malicious TUF repository can trigger arbitrary file overwriting, limited to the permissions that the calling process has. Note that this should only affect clients that are directly using the TUF client in sigstore/sigstore or are using an older version of Cosign. Public Sigstore deployment users are unaffected, as TUF metadata is validated by a quorum of trusted collaborators. This issue has been fixed in version 1.10.4. As a workaround, users can disable disk caching for the legacy client by setting SIGSTORE_NO_CACHE=true in the environment, migrate to https://github.com/sigstore/sigstore-go/tree/main/pkg/tuf, or upgrade to the latest sigstore/sigstore release.

cdsw-s2i-builder-buildah

CVE-2026-24157 NVIDIA NeMo Framework contains a vulnerability in checkpoint loading where an attacker could cause remote code execution. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure and data tampering.

nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-24159 NVIDIA NeMo Framework contains a vulnerability where an attacker may cause remote code execution. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure and data tampering.

nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-24688 pypdf is a free and open-source pure-python PDF library. An attacker who uses an infinite loop vulnerability that is present in versions prior to 6.6.2 can craft a PDF which leads to an infinite loop. This requires accessing the outlines/bookmarks. This has been fixed in pypdf 6.6.2. If projects cannot upgrade yet, consider applying the changes from PR #3610 manually.

cloudera-ai-rag-studio

CVE-2026-25528 LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. The LangSmith SDK's distributed tracing feature is vulnerable to Server-Side Request Forgery via malicious HTTP headers. An attacker can inject arbitrary api_url values through the baggage header, causing the SDK to exfiltrate sensitive trace data to attacker-controlled endpoints. When using distributed tracing, the SDK parses incoming HTTP headers via RunTree.from_headers() in Python or RunTree.fromHeaders() in Typescript. The baggage header can contain replica configurations including api_url and api_key fields. Prior to the fix, these attacker-controlled values were accepted without validation. When a traced operation completes, the SDK's post() and patch() methods send run data to all configured replica URLs, including any injected by an attacker. This vulnerability is fixed in version 0.6.3 of the Python SDK and 0.4.6 of the JavaScript SDK.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

CVE-2026-25949 Traefik is an HTTP reverse proxy and load balancer. Prior to 3.6.8, there is a potential vulnerability in Traefik managing STARTTLS requests. An unauthenticated client can bypass Traefik entrypoint respondingTimeouts.readTimeout by sending the 8-byte Postgres SSLRequest (STARTTLS) prelude and then stalling, causing connections to remain open indefinitely, leading to a denial of service. This vulnerability is fixed in 3.6.8.

traefik

CVE-2026-25960 vLLM is an inference and serving engine for large language models (LLMs). The SSRF protection fix for CVE-2026-24779 add in 0.15.1 can be bypassed in the load_from_url_async method due to inconsistent URL parsing behavior between the validation layer and the actual HTTP client. The SSRF fix uses urllib3.util.parse_url() to validate and extract the hostname from user-provided URLs. However, load_from_url_async uses aiohttp for making the actual HTTP requests, and aiohttp internally uses the yarl library for URL parsing. This vulnerability in 0.17.0.

nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

CVE-2026-26740 Buffer Overflow vulnerability in giflib v.5.2.2 allows a remote attacker to cause a denial of service via the EGifGCBToExtension overwriting an existing Graphic Control Extension block without validating its allocated size.

cmlserving-triton-runtime
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-26960 node-tar is a full-featured Tar for Node.js. When using default options in versions 7.5.7 and below, an attacker-controlled archive can create a hardlink inside the extraction directory that points to a file outside the extraction root, enabling arbitrary file read and write as the extracting user. Severity is high because the primitive bypasses path protections and turns archive extraction into a direct filesystem access primitive. This issue has been fixed in version 7.5.8.

cdsw-web

CVE-2026-27024 pypdf is a free and open-source pure-python PDF library. Prior to 6.7.1, an attacker who uses this vulnerability can craft a PDF which leads to an infinite loop. This requires accessing the children of a TreeObject, for example as part of outlines. This vulnerability is fixed in 6.7.1.

cloudera-ai-rag-studio

CVE-2026-27025 pypdf is a free and open-source pure-python PDF library. Prior to 6.7.1, an attacker who uses this vulnerability can craft a PDF which leads to long runtimes and large memory consumption. This requires parsing the /ToUnicode entry of a font with unusually large values, for example during text extraction. This vulnerability is fixed in 6.7.1.

cloudera-ai-rag-studio

CVE-2026-27026 pypdf is a free and open-source pure-python PDF library. Prior to 6.7.1, an attacker who uses this vulnerability can craft a PDF which leads to long runtimes. This requires a malformed /FlateDecode stream, where the byte-by-byte decompression is used. This vulnerability is fixed in 6.7.1.

cloudera-ai-rag-studio

CVE-2026-27459 pyOpenSSL is a Python wrapper around the OpenSSL library. Starting in version 22.0.0 and prior to version 26.0.0, if a user provided callback to `set_cookie_generate_callback` returned a cookie value greater than 256 bytes, pyOpenSSL would overflow an OpenSSL provided buffer. Starting in version 26.0.0, cookie values that are too long are now rejected.

cdw-diagnostic-tools
cdwdataviz
hue
runtimedataviz

CVE-2026-27482 Ray is an AI compute engine. In versions 2.53.0 and below, thedashboard HTTP server blocks browser-origin POST/PUT but does not cover DELETE, and key DELETE endpoints are unauthenticated by default. If the dashboard/agent is reachable (e.g., --dashboard-host=0.0.0.0), a web page via DNS rebinding or same-network access can issue DELETE requests that shut down Serve or delete jobs without user interaction. This is a drive-by availability impact. The fix for this vulnerability is to update to Ray 2.54.0 or higher.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-nvidia-nemotron-3-nano-v1.7.0

CVE-2026-27628 pypdf is a free and open-source pure-python PDF library. Prior to 6.7.2, an attacker who uses this vulnerability can craft a PDF which leads to an infinite loop. This requires reading the file. This has been fixed in pypdf 6.7.2. As a workaround, one may apply the patch manually.

cloudera-ai-rag-studio

CVE-2026-28374 Editors could delete any annotation, even those they do not have read access to. The editor user cannot create or read the annotations.

dex-grafana
mon_grafana

CVE-2026-28376 The Grafana Live push endpoint can be exploited to cause unbounded memory allocation by sending a large or streaming request body, potentially leading to out-of-memory conditions. An authenticated user with access to the Grafana Live API can trigger this issue.

dex-grafana
mon_grafana

CVE-2026-28379 A race condition in Grafana Live allows authenticated users with Viewer role to trigger a server crash by sending concurrent requests that cause a fatal map access error. This results in complete service unavailability requiring restart of the Grafana server.

dex-grafana
mon_grafana

CVE-2026-28380 Any Editor could delete any snapshot, even if they have no access to read or write them.

dex-grafana
mon_grafana

CVE-2026-28383 A request to the Grafana plugin resources endpoint can cause unbounded memory allocation by reading the entire request body into memory. An authenticated user can exploit this to trigger an out-of-memory condition, potentially causing a denial of service.

dex-grafana
mon_grafana

CVE-2026-29062 jackson-core contains core low-level incremental ("streaming") parser and generator abstractions used by Jackson Data Processor. From version 3.0.0 to before version 3.1.0, the UTF8DataInputJsonParser, which is used when parsing from a java.io.DataInput source, bypasses the maxNestingDepth constraint (default: 500) defined in StreamReadConstraints. A similar issue was found in ReaderBasedJsonParser. This allows a user to supply a JSON document with excessive nesting, which can cause a StackOverflowError when the structure is processed, leading to a Denial of Service (DoS). This issue has been patched in version 3.1.0.

cdsw-mlops-governance
thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-29777 Traefik is an HTTP reverse proxy and load balancer. Prior to 3.6.10, A tenant with write access to an HTTPRoute resource can inject backtick-delimited rule tokens into Traefik's router rule language via unsanitized header or query parameter match values. In shared gateway deployments, this can bypass listener hostname constraints and redirect traffic for victim hostnames to attacker-controlled backends. This vulnerability is fixed in 3.6.10.

traefik

CVE-2026-29786 node-tar is a full-featured Tar for Node.js. Prior to version 7.5.10, tar can be tricked into creating a hardlink that points outside the extraction directory by using a drive-relative link target such as C:../target.txt, which enables file overwrite outside cwd during normal tar.x() extraction. This issue has been patched in version 7.5.10.

cdsw-web

CVE-2026-30836 Step CA is an online certificate authority for secure, automated certificate management for DevOps. Versions 0.30.0-rc6 and below do not safeguard against unauthenticated certificate issuance through the SCEP UpdateReq. This issue has been fixed in version 0.30.0.

cdwdataviz
runtimedataviz

CVE-2026-30997 An out-of-bounds read in the read_global_param() function (libavcodec/av1dec.c) of FFmpeg v8.0.1 allows attackers to cause a Denial of Service (DoS) via a crafted input.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-30998 An improper resource deallocation and closure vulnerability in the tools/zmqsend.c component of FFmpeg v8.0.1 allows attackers to cause a Denial of Service (DoS) via supplying a crafted input file.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-30999 A heap buffer overflow in the av_bprint_finalize() function of FFmpeg v8.0.1 allows attackers to cause a Denial of Service (DoS) via a crafted input.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-31221 PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-31235 The imgaug library thru 0.4.0 contains an insecure deserialization vulnerability in its BackgroundAugmenter class within the multicore.py module. The class uses Python's pickle module to deserialize data received via a multiprocessing queue in the _augment_images_worker() method without any safety checks. An attacker who can influence the data placed into this queue (e.g., through social engineering, malicious input scripts, or a compromised shared queue) can provide a malicious pickle payload. When deserialized, this payload can execute arbitrary code in the context of the worker process, leading to remote or local code execution depending on the deployment scenario.

nim-baidu-paddleocr-v1.5.0

CVE-2026-31589 In the Linux kernel, the following vulnerability has been resolved: mm: call ->free_folio() directly in folio_unmap_invalidate() We can only call filemap_free_folio() if we have a reference to (or hold a lock on) the mapping. Otherwise, we've already removed the folio from the mapping so it no longer pins the mapping and the mapping can be removed, causing a use-after-free when accessing mapping->a_ops. Follow the same pattern as __remove_mapping() and load the free_folio function pointer before dropping the lock on the mapping. That lets us make filemap_free_folio() static as this was the only caller outside filemap.c.

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2026-31635 In the Linux kernel, the following vulnerability has been resolved: rxrpc: fix oversized RESPONSE authenticator length check rxgk_verify_response() decodes auth_len from the packet and is supposed to verify that it fits in the remaining bytes. The existing check is inverted, so oversized RESPONSE authenticators are accepted and passed to rxgk_decrypt_skb(), which can later reach skb_to_sgvec() with an impossible length and hit BUG_ON(len). Decoded from the original latest-net reproduction logs with scripts/decode_stacktrace.sh: RIP: __skb_to_sgvec() [net/core/skbuff.c:5285 (discriminator 1)] Call Trace: skb_to_sgvec() [net/core/skbuff.c:5305] rxgk_decrypt_skb() [net/rxrpc/rxgk_common.h:81] rxgk_verify_response() [net/rxrpc/rxgk.c:1268] rxrpc_process_connection() [net/rxrpc/conn_event.c:266 net/rxrpc/conn_event.c:364 net/rxrpc/conn_event.c:386] process_one_work() [kernel/workqueue.c:3281] worker_thread() [kernel/workqueue.c:3353 kernel/workqueue.c:3440] kthread() [kernel/kthread.c:436] ret_from_fork() [arch/x86/kernel/process.c:164] Reject authenticator lengths that exceed the remaining packet payload.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-31688 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

cmlserving-triton-runtime
dex-runtime-python-builder-7.1.9.1078-compat
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-31718 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix use-after-free in __ksmbd_close_fd() via durable scavenger When a durable file handle survives session disconnect (TCP close without SMB2_LOGOFF), session_fd_check() sets fp->conn = NULL to preserve the handle for later reconnection. However, it did not clean up the byte-range locks on fp->lock_list. Later, when the durable scavenger thread times out and calls __ksmbd_close_fd(NULL, fp), the lock cleanup loop did: spin_lock(&fp->conn->llist_lock); This caused a slab use-after-free because fp->conn was NULL and the original connection object had already been freed by ksmbd_tcp_disconnect(). The root cause is asymmetric cleanup: lock entries (smb_lock->clist) were left dangling on the freed conn->lock_list while fp->conn was nulled out. To fix this issue properly, we need to handle the lifetime of smb_lock->clist across three paths: - Safely skip clist deletion when list is empty and fp->conn is NULL. - Remove the lock from the old connection's lock_list in session_fd_check() - Re-add the lock to the new connection's lock_list in ksmbd_reopen_durable_fd().

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-31769 In the Linux kernel, the following vulnerability has been resolved: gpib: fix use-after-free in IO ioctl handlers The IBRD, IBWRT, IBCMD, and IBWAIT ioctl handlers use a gpib_descriptor pointer after board->big_gpib_mutex has been released. A concurrent IBCLOSEDEV ioctl can free the descriptor via close_dev_ioctl() during this window, causing a use-after-free. The IO handlers (read_ioctl, write_ioctl, command_ioctl) explicitly release big_gpib_mutex before calling their handler. wait_ioctl() is called with big_gpib_mutex held, but ibwait() releases it internally when wait_mask is non-zero. In all four cases, the descriptor pointer obtained from handle_to_descriptor() becomes unprotected. Fix this by introducing a kernel-only descriptor_busy reference count in struct gpib_descriptor. Each handler atomically increments descriptor_busy under file_priv->descriptors_mutex before releasing the lock, and decrements it when done. close_dev_ioctl() checks descriptor_busy under the same lock and rejects the close with -EBUSY if the count is non-zero. A reference count rather than a simple flag is necessary because multiple handlers can operate on the same descriptor concurrently (e.g. IBRD and IBWAIT on the same handle from different threads). A separate counter is needed because io_in_progress can be cleared from unprivileged userspace via the IBWAIT ioctl (through general_ibstatus() with set_mask containing CMPL), which would allow an attacker to bypass a check based solely on io_in_progress. The new descriptor_busy counter is only modified by the kernel IO paths. The lock ordering is consistent (big_gpib_mutex -> descriptors_mutex) and the handlers only hold descriptors_mutex briefly during the lookup, so there is no deadlock risk and no impact on IO throughput.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-31786 In the Linux kernel, the following vulnerability has been resolved: Buffer overflow in drivers/xen/sys-hypervisor.c The build id returned by HYPERVISOR_xen_version(XENVER_build_id) is neither NUL terminated nor a string. The first causes a buffer overflow as sprintf in buildid_show will read and copy till it finds a NUL. 00000000 f4 91 51 f4 dd 38 9e 9d 65 47 52 eb 10 71 db 50 |..Q..8..eGR..q.P| 00000010 b9 a8 01 42 6f 2e 32 |...Bo.2| 00000017 So use a memcpy instead of sprintf to have the correct value: 00000000 f4 91 51 f4 dd 00 9e 9d 65 47 52 eb 10 71 db 50 |..Q.....eGR..q.P| 00000010 b9 a8 01 42 |...B| 00000014 (the above have a hack to embed a zero inside and check it's returned correctly). This is XSA-485 / CVE-2026-31786

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2026-31787 In the Linux kernel, the following vulnerability has been resolved: xen/privcmd: fix double free via VMA splitting privcmd_vm_ops defines .close (privcmd_close), but neither .may_split nor .open. When userspace does a partial munmap() on a privcmd mapping, the kernel splits the VMA via __split_vma(). Since may_split is NULL, the split is allowed. vm_area_dup() copies vm_private_data (a pages array allocated in alloc_empty_pages()) into the new VMA without any fixup, because there is no .open callback. Both VMAs now point to the same pages array. When the unmapped portion is closed, privcmd_close() calls: - xen_unmap_domain_gfn_range() - xen_free_unpopulated_pages() - kvfree(pages) The surviving VMA still holds the dangling pointer. When it is later destroyed, the same sequence runs again, which leads to a double free. Fix this issue by adding a .may_split callback denying the VMA split. This is XSA-487 / CVE-2026-31787

dex-runtime-python-builder-7.1.9.1078-compat

CVE-2026-31802 node-tar is a full-featured Tar for Node.js. Prior to version 7.5.11, tar (npm) can be tricked into creating a symlink that points outside the extraction directory by using a drive-relative symlink target such as C:../../../target.txt, which enables file overwrite outside cwd during normal tar.x() extraction. This vulnerability is fixed in 7.5.11.

cdsw-web

CVE-2026-32274 Black is the uncompromising Python code formatter. Starting in version 24.3.0 and prior to version 26.3.1, Black writes a cache file, the name of which is computed from various formatting options. The value of the --python-cell-magics option was placed in the filename without sanitization, which allowed an attacker who controls the value of this argument to write cache files to arbitrary file system locations. Fixed in Black 26.3.1.

kserve_agent
kserve_controller
kserve_huggingfaceserver
kserve_router
model-registry
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-32285 The Delete function fails to properly validate offsets when processing malformed JSON input. This can lead to a negative slice index and a runtime panic, allowing a denial of service attack.

cdwdataviz
runtimedataviz

CVE-2026-32286 The DataRow.Decode function fails to properly validate field lengths. A malicious or compromised PostgreSQL server can send a DataRow message with a negative field length, causing a slice bounds out of range panic.

cdsw-api
cdsw-user-management

CVE-2026-32695 Traefik is an HTTP reverse proxy and load balancer. Prior to versions 3.6.11 and 3.7.0-ea.2, Traefik's Knative provider builds router rules by interpolating user-controlled values into backtick-delimited rule expressions without escaping. In live cluster validation, Knative `rules[].hosts[]` was exploitable for host restriction bypass (for example `tenant.example.com`) || Host(`attacker.com`), producing a router that serves attacker-controlled hosts. Knative `headers[].exact` also allows rule-syntax injection and proves unsafe rule construction. In multi-tenant clusters, this can route unauthorized traffic to victim services and lead to cross-tenant traffic exposure. Versions 3.6.11 and 3.7.0-ea.2 patch the issue.

traefik

CVE-2026-32738 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and below, a crafted 792-byte HEIF sequence file with samples_per_chunk=0 in the stsc box causes an unsigned integer underflow in the Chunk constructor (m_last_sample = 0 + 0 - 1 = UINT32_MAX), mapping all samples to an empty chunk and resulting in a denial of service. When any sample is accessed, the library reads from index 0 of an empty std::vector, causing a guaranteed SEGV (null-page read). The file parses successfully without producing an error; the crash occurs on the first frame access. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-32739 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and below, a crafted 800-byte HEIF sequence file causes an infinite loop in Box_stts::get_sample_duration(), consuming 100% CPU indefinitely with zero progress, leading to DoS. The loop has no iteration limit or timeout and is triggered during file open (parsing) - before any user interaction or image decoding. The process stays alive (no crash, no error logged), making it invisible to crash-based monitoring. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-32740 libheif is a HEIF and AVIF file format decoder and encoder. Versions 1.21.2 and prior contain a heap-buffer-overflow (write) vulnerability in the grid tile compositing, allowing an attacker to write 64 bytes of fully attacker-controlled data past the end of a chroma plane heap allocation by crafting a HEIF/AVIF file with a 1×4 grid of odd-height tiles. The overflow is triggered during normal image decoding with default build configuration. The written bytes are chroma (Cb/Cr) pixel values from the attacking tile, giving the attacker full control over the overflow content. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-32741 libheif is a HEIF and AVIF file format decoder and encoder. Versions 1.21.2 and below contain a heap buffer overflow in MaskImageCodec::decode_mask_image(). When decoding a HEIF file containing a mask image (mski), the function copies the full iloc extent data into a pixel buffer using memcpy(dst, data.data(), data.size()). The copy length data.size() is determined by the iloc extent in the file (attacker-controlled), while the destination buffer is sized based on the declared image dimensions. Because no upper-bound check exists on the data length, a crafted file whose iloc extent exceeds the pixel buffer allocation overflows the heap. The vulnerable single-memcpy branch is reached when the mskC property specifies bits_per_pixel = 8 and the ispe property declares an even width ≥ 64 (so that stride == width), with no changes to default security limits or external codec plugins required. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-32792 NLnet Labs Unbound 1.6.2 up to and including version 1.25.0 has a denial of service vulnerability when compiled with DNSCrypt support ('--enable-dnscrypt'). A bad DNSCrypt query could underflow Unbound's DNSCrypt packet reading procedure that may lead to heap overflow. A malicious actor can exploit the vulnerability with a single bad DNSCrypt query that its decrypted plaintext consists entirely of '0x00' bytes and does not contain the expected '0x80' marker. Unbound would then start reading more bytes than necessary until it finds a non-'0x00' byte. Based on the underlying memory allocator and the memory layout, it could lead to heap overflow while reading followed by a crash. Likelihood of a crash is low, since it relies heavily on the underlying memory allocator and the memory layout. If the heap overflow does not happen, Unbound's later packet checks will deny the packet. Unbound 1.25.1 contains a patch with a fix to bound reading in the given buffer space.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-32814 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and prior, when decoding a HEIF grid image with strict_decoding=false (the default), a corrupted tile silently fails to decode and the library returns heif_error_Ok with no indication of failure, leading to an uninitialized heap memory information leak. The canvas is allocated via create_clone_image_at_new_size() → plane.alloc() → new (std::nothrow) uint8_t[allocation_size] which does not zero the memory; only the alpha plane is explicitly initialized via fill_plane(), so the Y, Cb, and Cr planes contain whatever was previously at that heap address. The failed tile's region of the canvas is never written. It retains uninitialized heap data that is delivered to the caller as decoded pixel values (4,096 bytes per Y/Cb/Cr plane = 12,288+ bytes total). Any application using libheif to decode grid-based HEIF/AVIF files with default settings is vulnerable: a crafted .heic or .avif file causes 4,096+ bytes of heap memory to appear as pixel values in the decoded image, and the calling application receives heif_error_Ok, so it has no indication the output contains heap garbage. In server-side image processing, an uploaded crafted HEIF decoded and re-encoded (e.g., as PNG/JPEG for thumbnails, CDN, social media) can leak cross-user data such as auth tokens, database results, and other users' image data. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-32882 libheif is a HEIF and AVIF file format decoder and encoder. Versions 1.21.2 and prior contain a heap buffer over-read in HeifPixelImage::overlay() in libheif/pixelimage.cc. When compositing an overlay image (iovl) whose child image has a different bit depth for the alpha channel than for the color channels, the function indexes into the alpha plane using the color channel stride (in_stride) instead of the previously retrieved alpha_stride, causing reads past the end of the alpha buffer (up to 3,123 bytes for a 100×50 image with 10-bit color and 8-bit alpha). A crafted HEIF file can exploit this to cause a denial of service (crash) or potentially disclose adjacent heap memory through leaked bytes embedded in the decoded output pixels. This issue has been fixed in versionThis issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-33033 An issue was discovered in 6.0 before 6.0.4, 5.2 before 5.2.13, and 4.2 before 4.2.30. `MultiPartParser` allows remote attackers to degrade performance by submitting multipart uploads with `Content-Transfer-Encoding: base64` including excessive whitespace. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Seokchan Yoon for reporting this issue.

cdwdataviz
runtimedataviz

CVE-2026-33034 An issue was discovered in 6.0 before 6.0.4, 5.2 before 5.2.13, and 4.2 before 4.2.30. ASGI requests with a missing or understated `Content-Length` header could bypass the `DATA_UPLOAD_MAX_MEMORY_SIZE` limit when reading `HttpRequest.body`, allowing remote attackers to load an unbounded request body into memory. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Superior for reporting this issue.

cdwdataviz
runtimedataviz

CVE-2026-33079 In versions 3.0.0a1 through 3.2.0 of Mistune, there is a ReDoS (Regular Expression Denial of Service) vulnerability in `LINK_TITLE_RE` that allows an attacker who can supply Markdown for parsing to cause denial of service. The regular expression used for parsing link titles contains overlapping alternatives that can trigger catastrophic backtracking. In both the double-quoted and single-quoted branches, a backslash followed by punctuation can be matched either as an escaped punctuation sequence or as two ordinary characters, creating an ambiguous pattern inside a repeated group. If an attacker supplies Markdown containing repeated ! sequences with no closing quote, the regex engine explores an exponential number of backtracking paths. This is reachable through normal Markdown parsing of inline links and block link reference definitions. A small crafted input can therefore cause significant CPU consumption and make applications using Mistune unresponsive.

cloudera-ai-agent-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-33164 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.0.17, a malformed H.265 PPS NAL unit causes a segmentation fault in pic_parameter_set::set_derived_values(). This issue has been patched in version 1.0.17.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-33165 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.0.17, a crafted HEVC bitstream causes an out-of-bounds heap write confirmed by AddressSanitizer. The trigger is a stale ctb_info.log2unitSize after an SPS change where PicWidthInCtbsY and PicHeightInCtbsY stay constant but Log2CtbSizeY changes, causing set_SliceHeaderIndex to index past the allocated image metadata array and write 2 bytes past the end of a heap allocation. This issue has been patched in version 1.0.17.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-33215 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. The nats-server provides an MQTT client interface. Prior to versions 2.11.15 and 2.12.5, Sessions and Messages can by hijacked via MQTT Client ID malfeasance. Versions 2.11.15 and 2.12.5 patch the issue. No known workarounds are available.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33216 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, for MQTT deployments using usercodes/passwords: MQTT passwords are incorrectly classified as a non-authenticating identity statement (JWT) and exposed via monitoring endpoints. Versions 2.11.14 and 2.12.6 contain a fix. As a workaround, ensure monitoring end-points are adequately secured. Best practice remains to not expose the monitoring endpoint to the Internet or other untrusted network users.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33217 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, when using ACLs on message subjects, these ACLs were not applied in the `$MQTT.>` namespace, allowing MQTT clients to bypass ACL checks for MQTT subjects. Versions 2.11.15 and 2.12.6 contain a fix. No known workarounds are available.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33218 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, a client which can connect to the leafnode port can crash the nats-server with a certain malformed message pre-authentication. Versions 2.11.15 and 2.12.6 contain a fix. As a workaround, disable leafnode support if not needed or restrict network connections to the leafnode port, if plausible without compromising the service offered.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33219 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, a malicious client which can connect to the WebSockets port can cause unbounded memory use in the nats-server before authentication; this requires sending a corresponding amount of data. This is a milder variant of CVE-2026-27571. That earlier issue was a compression bomb, this vulnerability is not. Attacks against this new issue thus require significant client bandwidth. Versions 2.11.15 and 2.12.6 contain a fix. As a workaround, disable websockets if not required for project deployment.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33222 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, users with JetStream admin API access to restore one stream could restore to other stream names, impacting data which should have been protected against them. Versions 2.11.15 and 2.12.6 contain a fix. As a workaround, if developers have configured users to have limited JetStream restore permissions, temporarily remove those permissions.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33223 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, the NATS message header `Nats-Request-Info:` is supposed to be a guarantee of identity by the NATS server, but the stripping of this header from inbound messages was not fully effective. An attacker with valid credentials for any regular client interface could thus spoof their identity to services which rely upon this header. Versions 2.11.15 and 2.12.6 contain a fix. No known workarounds are available.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33236 NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. In versions 3.9.3 and prior, the NLTK downloader does not validate the `subdir` and `id` attributes when processing remote XML index files. Attackers can control a remote XML index server to provide malicious values containing path traversal sequences (such as `../`), which can lead to arbitrary directory creation, arbitrary file creation, and arbitrary file overwrite. Commit 89fe2ec2c6bae6e2e7a46dad65cc34231976ed8a patches the issue.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

CVE-2026-33246 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. The nats-server offers a `Nats-Request-Info:` message header, providing information about a request. This is supposed to provide enough information to allow for account/user identification, such that NATS clients could make their own decisions on how to trust a message, provided that they trust the nats-server as a broker. A leafnode connecting to a nats-server is not fully trusted unless the system account is bridged too. Thus identity claims should not have propagated unchecked. Prior to versions 2.11.15 and 2.12.6, NATS clients relying upon the Nats-Request-Info: header could be spoofed. This does not directly affect the nats-server itself, but the CVSS Confidentiality and Integrity scores are based upon what a hypothetical client might choose to do with this NATS header. Versions 2.11.15 and 2.12.6 contain a fix. No known workarounds are available.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33247 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, if a nats-server is run with static credentials for all clients provided via argv (the command-line), then those credentials are visible to any user who can see the monitoring port, if that too is enabled. The `/debug/vars` end-point contains an unredacted copy of argv. Versions 2.11.15 and 2.12.6 contain a fix. As a workaround, configure credentials inside a configuration file instead of via argv, and do not enable the monitoring port if using secrets in argv. Best practice remains to not expose the monitoring port to the Internet, or to untrusted network sources.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33248 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Prior to versions 2.11.15 and 2.12.6, when using mTLS for client identity, with `verify_and_map` to derive a NATS identity from the client certificate's Subject DN, certain patterns of RDN would not be correctly enforced, allowing for authentication bypass. This does require a valid certificate from a CA already trusted for client certificates, and `DN` naming patterns which the NATS maintainers consider highly unlikely. So this is an unlikely attack. Nonetheless, administrators who have been very sophisticated in their `DN` construction patterns might conceivably be impacted. Versions 2.11.15 and 2.12.6 contain a fix. As a workaround, developers should review their CA issuing practices.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33249 NATS-Server is a High-Performance server for NATS.io, a cloud and edge native messaging system. Starting in version 2.11.0 and prior to versions 2.11.15 and 2.12.6, a valid client which uses message tracing headers can indicate that the trace messages can be sent to an arbitrary valid subject, including those to which the client does not have publish permission. The payload is a valid trace message and not chosen by the attacker. Versions 2.11.15 and 2.12.6 contain a fix. No known workarounds are available.

nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64

CVE-2026-33278 NLnet Labs Unbound 1.19.1 up to and including version 1.25.0 has a vulnerability in the DNSSEC validator that enables denial of service and possible remote code execution as a result of deep copying a data structure and erroneously overwriting a destination pointer. An adversary can exploit the vulnerability by controlling a malicious signed zone and querying a vulnerable Unbound. When DS sub-queries need to suspend validation due to NSEC3 computational budget exhaustion (introduced in Unbound 1.19.1), Unbound deep-copies response messages to preserve them across memory region teardown. A struct-assignment bug overwrites the destination's pointer with the source's pointer. After the sub-query region is freed, the resumed validator dereferences this dangling pointer, triggering a crash or potentially enabling arbitrary code execution. Unbound 1.25.1 contains a patch with a fix to preserve the correct pointer when deep copying the data structure.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-33376 When using an IPv6 allow-list for the Auth Proxy feature, it defaults to /32 addresses. Addresses specifying a mask explicitly are not affected; to mitigate easily, add the desired mask (usually /128) to the addresses. Only auth proxy is affected; Okta, SAML, LDAP, etc are unaffected here.

dex-grafana
mon_grafana

CVE-2026-33377 An Editor can overwrite a dashboard not owned by them to acquire admin on that specific dashboard. The user must have write access to the dashboard to escalate privilege.

dex-grafana
mon_grafana

CVE-2026-33378 Using the $__timeGroup macro, one can achieve an OOM by overloading the server. This requires a SQL datasource. If the server is set up to auto-restart, the impact is minimal or non-existent, as the attack can take upwards of half an hour to crash the server.

dex-grafana
mon_grafana

CVE-2026-33433 Traefik is an HTTP reverse proxy and load balancer. Prior to versions 2.11.42, 3.6.11, and 3.7.0-ea.3, when `headerField` is configured with a non-canonical HTTP header name (e.g., `x-auth-user` instead of `X-Auth-User`), an authenticated attacker can inject their own canonical version of that header to impersonate any identity to the backend. The backend receives two header entries — the attacker-injected canonical one is read first, overriding Traefik's non-canonical write. Versions 2.11.42, 3.6.11, and 3.7.0-ea.3 patch the issue.

traefik

CVE-2026-33701 OpenTelemetry Java Instrumentation provides OpenTelemetry auto-instrumentation and instrumentation libraries for Java. In versions prior to 2.26.1, the RMI instrumentation registered a custom endpoint that deserialized incoming data without applying serialization filters. On JDK version 16 and earlier, an attacker with network access to a JMX or RMI port on an instrumented JVM could exploit this to potentially achieve remote code execution. All three of the following conditions must be true to exploit this vulnerability: First, OpenTelemetry Java instrumentation is attached as a Java agent (`-javaagent`) on Java 16 or earlier. Second, JMX/RMI port has been explicitly configured via `-Dcom.sun.management.jmxremote.port` and is network-reachable. Third, gadget-chain-compatible library is present on the classpath. This results in arbitrary remote code execution with the privileges of the user running the instrumented JVM. For JDK >= 17, no action is required, but upgrading is strongly encouraged. For JDK < 17, upgrade to version 2.26.1 or later. As a workaround, set the system property `-Dotel.instrumentation.rmi.enabled=false` to disable the RMI integration.

databus-producer

CVE-2026-33816 Memory-safety vulnerability in github.com/jackc/pgx/v5.

cdwdataviz
runtimedataviz

CVE-2026-33865 MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. This issue affects MLflow version through 3.10.1

cloudera-ai-rag-studio

CVE-2026-33866 MLflow is vulnerable to an authorization bypass affecting the AJAX endpoint used to download saved model artifacts. Due to missing access‑control validation, a user without permissions to a given experiment can directly query this endpoint and retrieve model artifacts they are not authorized to access. This issue affects MLflow version through 3.10.1

cloudera-ai-rag-studio

CVE-2026-34267 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34270 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Group Replication Plugin). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34271 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Group Replication Plugin). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34276 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Group Replication Plugin). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34278 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34293 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: DML). Supported versions that are affected are 8.0.0-8.0.45. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34303 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34304 Vulnerability in the MySQL Server product of Oracle MySQL (component: InnoDB). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34308 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: JSON). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 6.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34317 Vulnerability in the MySQL Shell product of Oracle MySQL (component: Shell: Core Client). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with logon to the infrastructure where MySQL Shell executes to compromise MySQL Shell. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Shell. CVSS 3.1 Base Score 5.0 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34318 Vulnerability in the MySQL Shell product of Oracle MySQL (component: Shell: Core Client). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Difficult to exploit vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Shell. While the vulnerability is in MySQL Shell, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all MySQL Shell accessible data. CVSS 3.1 Base Score 5.8 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:C/C:H/I:N/A:N).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34319 Vulnerability in the MySQL Shell product of Oracle MySQL (component: Shell: Core Client). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows low privileged attacker with logon to the infrastructure where MySQL Shell executes to compromise MySQL Shell. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Shell. CVSS 3.1 Base Score 5.0 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-34601 xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In xmldom versions 0.6.0 and prior and @xmldom/xmldom prior to versions 0.8.12 and 0.9.9, xmldom/xmldom allows attacker-controlled strings containing the CDATA terminator ]]> to be inserted into a CDATASection node. During serialization, XMLSerializer emitted the CDATA content verbatim without rejecting or safely splitting the terminator. As a result, data intended to remain text-only became active XML markup in the serialized output, enabling XML structure injection and downstream business-logic manipulation. This issue has been patched in xmldom version 0.6.0 and @xmldom/xmldom versions 0.8.12 and 0.9.9.

cdsw-web

CVE-2026-34972 OpenFGA is a high-performance and flexible authorization/permission engine built for developers and inspired by Google Zanzibar. From 1.8.0 to 1.13.1, under specific conditions, BatchCheck calls with multiple checks sent for the same object, relation, and user combination can result in improper policy enforcement. This vulnerability is fixed in 1.14.0.

mon_grafana

CVE-2026-35029 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.0, the /config/update endpoint does not enforce admin role authorization. A user who is already authenticated into the platform can then use this endpoint to modify proxy configuration and environment variables, register custom pass-through endpoint handlers pointing to attacker-controlled Python code, achieving remote code execution, read arbitrary server files by setting UI_LOGO_PATH and fetching via /get_image, and take over other privileged accounts by overwriting UI_USERNAME and UI_PASSWORD environment variables. Fixed in v1.83.0.

nim-deepseek-r1-v1.7.3

CVE-2026-35030 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.0, when JWT authentication is enabled (enable_jwt_auth: true), the OIDC userinfo cache uses token[:20] as the cache key. JWT headers produced by the same signing algorithm generate identical first 20 characters. This configuration option is not enabled by default. Most instances are not affected. An unauthenticated attacker can craft a token whose first 20 characters match a legitimate user's cached token. On cache hit, the attacker inherits the legitimate user's identity and permissions. This affects deployments with JWT/OIDC authentication enabled. Fixed in v1.83.0.

nim-deepseek-r1-v1.7.3

CVE-2026-35236 Vulnerability in the MySQL Server product of Oracle MySQL (component: InnoDB). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-35237 Vulnerability in the MySQL Server product of Oracle MySQL (component: InnoDB). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-35238 Vulnerability in the MySQL Server product of Oracle MySQL (component: InnoDB). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-35239 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: DML). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-35240 Vulnerability in the MySQL Server product of Oracle MySQL (component: Server: Optimizer). Supported versions that are affected are 8.0.0-8.0.45, 8.4.0-8.4.8 and 9.0.0-9.6.0. Easily exploitable vulnerability allows high privileged attacker with network access via multiple protocols to compromise MySQL Server. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server. CVSS 3.1 Base Score 4.9 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-35397 Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, a path traversal vulnerability in the REST API allows an authenticated user to escape the configured root_dir and access sibling directories whose names begin with the same prefix as the root_dir. For example, with a root_dir named "test", the API permits access to a sibling directory named "testtest" through a crafted request to the /api/contents endpoint using encoded path components. An attacker can read, write, and delete files in affected sibling directories. Multi-tenant deployments using predictable naming schemes are particularly at risk, as a user with a directory named "user1" could access directories for user10 through user19 and beyond. A user who can choose a single-character folder name could gain access to a significant number of sibling directories. Version 2.18.0 contains a fix. As a workaround, ensure folder names do not share a common prefix with any sibling directory.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-39350 Istio is an open platform to connect, manage, and secure microservices. In versions 1.25.0 through 1.27.8, 1.28.0 through 1.28.5, 1.29.0, and 1.29.1, the serviceAccounts and notServiceAccounts fields in AuthorizationPolicy incorrectly interpret dots (.) as a regular expression matcher. Because . is a valid character in a service account name, an AuthorizationPolicy ALLOW rule targeting a service account such as cert-manager.io also matches cert-manager-io, cert-managerXio, etc. A DENY rule targeting the same name fails to block those variants. Fixes are available in versions 1.29.2, 1.28.6, and 1.27.9.

install-cni
pilot
proxyv2

CVE-2026-40087 LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.84 and 1.2.28, LangChain's f-string prompt-template validation was incomplete in two respects. First, some prompt template classes accepted f-string templates and formatted them without enforcing the same attribute-access validation as PromptTemplate. In particular, DictPromptTemplate and ImagePromptTemplate could accept templates containing attribute access or indexing expressions and subsequently evaluate those expressions during formatting. Second, f-string validation based on parsed top-level field names did not reject nested replacement fields inside format specifiers. In this pattern, the nested replacement field appears in the format specifier rather than in the top-level field name. As a result, earlier validation based on parsed field names did not reject the template even though Python formatting would still attempt to resolve the nested expression at runtime. This vulnerability is fixed in 0.3.84 and 1.2.28.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

CVE-2026-40097 Step CA is an online certificate authority for secure, automated certificate management for DevOps. From 0.24.0 to before 0.30.0-rc3, an attacker can trigger an index out-of-bounds panic in Step CA by sending a crafted attestation key (AK) certificate with an empty Extended Key Usage (EKU) extension during TPM device attestation. When processing a device-attest-01 ACME challenge using TPM attestation, Step CA validates that the AK certificate contains the tcg-kp-AIKCertificate Extended Key Usage OID. During this validation, the EKU extension value is decoded from its ASN.1 representation and the first element is checked. A crafted certificate could include an EKU extension that decodes to an empty sequence, causing the code to panic when accessing the first element of the empty slice. This vulnerability is only reachable when a device-attest-01 ACME challenge with TPM attestation is configured. Deployments not using TPM device attestation are not affected. This vulnerability is fixed in 0.30.0-rc3.

cdwdataviz
runtimedataviz

CVE-2026-40110 Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the Origin header validation uses Python's re.match() to check incoming origins against the allow_origin_pat configuration value. Because re.match() only anchors at the start of the string and does not require a full match, a pattern intended to match only a trusted domain (e.g., trusted.example.com) will also match any origin that begins with that domain followed by additional characters (e.g., trusted.example.com.evil.com). An attacker who controls such a domain can bypass the CORS origin restriction and make cross-origin requests to the Jupyter Server API from an untrusted site. This issue has been fixed in version 2.18.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-40171 In Jupyter Notebook versions 7.0.0 through 7.5.5, JupyterLab versions 4.5.6 and earlier, and the corresponding @jupyter-notebook/help-extension and @jupyterlab/help-extension packages before 7.5.6 and 4.5.7, a stored cross-site scripting issue in the help command linker can be chained with attacker-controlled notebook content to steal authentication tokens with a single click. An attacker can craft a malicious notebook file containing elements that appear indistinguishable from legitimate controls and trigger execution when a user interacts with them. Successful exploitation allows theft of the user's authentication token and complete takeover of the Jupyter session through the REST API, including reading files, creating or modifying files, accessing kernels to execute arbitrary code, and creating terminals for shell access. This issue has been fixed in Notebook 7.5.6, JupyterLab 4.5.7, @jupyter-notebook/help-extension 7.5.6, and @jupyterlab/help-extension 4.5.7. As a workaround, disable the affected help extensions or set allowCommandLinker to false in the sanitizer configuration.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2026-40217 LiteLLM through 2026-04-08 allows remote attackers to execute arbitrary code via bytecode rewriting at the /guardrails/test_custom_code URI.

cloudera-ai-rag-studio

CVE-2026-40460 When NGINX Plus or NGINX Open Source are configured to use the HTTP/3 QUIC module, an attacker may be able to spoof their source IP address allowing for bypass of authorization or bypass of rate limiting.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-40622 NLnet Labs Unbound 1.16.2 up to and including version 1.25.0 has a vulnerability of the 'ghost domain names' family of attacks that could extend the ghost domain window by up to one cached TTL configured value. Similar to other 'ghost domain names' attacks, an adversary needs to control a (ghost) zone and be able to query a vulnerable Unbound. A single client NS query can cause Unbound to overwrite the cached expired parent-side referral NS rrset with the child-side apex NS rrset and essentially extend the ghost domain window by up to one cached TTL configured value ('cache-max-ttl'). In configurations where 'harden-referral-path: yes' is used (non-default configuration), no client NS query is required since Unbound implicitly performs that query. Unbound 1.25.1 contains a patch with a fix that does not allow extension of TTLs for (parent) NS records regardless of their trust.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-40701 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_ssl_module module when the ssl_verify_client directive is set to "on" or "optional," and the ssl_ocsp directive is set to "on" or the leaf parameters are configured with a resolver. With this configuration, an unauthenticated attacker can send requests along with conditions beyond its control that may cause a heap-use-after-free error in the NGINX worker process. This vulnerability may result in limited modification of data or the NGINX worker process restarting.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-40898 quic-go is an implementation of the QUIC protocol in Go. Prior to version 0.59.1, an attacker can cause excessive memory allocation in quic-go's HTTP/3 client and server implementations by sending a QPACK-encoded HEADERS frame that decodes into a large trailer field section with many unique field names and/or large values. The implementation builds an `http.Header` for the corresponding `http.Request` or `http.Response`, while only enforcing limits on the size of the QPACK-compressed HEADERS frame, not on the decoded field section. This can lead to memory exhaustion. This is very similar to CVE-2025-64702. The difference is that this issue uses HTTP trailers, rather than HTTP headers, as the attack vector. A misbehaving or malicious peer can cause a denial-of-service (DoS) attack against quic-go's HTTP/3 servers or clients by triggering excessive memory allocation, potentially leading to crashes or resource exhaustion. This affects both servers and clients due to symmetric header construction. Version 0.59.1 enforces RFC 9114 decoded field section size limits for trailers as well. It incrementally decodes QPACK entries and checks the field section size after each entry, aborting the stream if an entry causes the limit to be exceeded.

cdp-private
cdwdataviz
cm-health-exporter
logger-alert-receiver
metrics-server-exporter
monitoring-app
monitoring-controller-manager
parcel
platform-agent-proxy
runtimedataviz
traefik

CVE-2026-40934 Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the secret used to sign authentication cookies is persisted to a static file at ~/.local/share/jupyter/runtime/jupyter_cookie_secret and is never rotated when a user changes their password. After a password reset and server restart, any previously issued authentication cookie remains cryptographically valid because the signing key has not changed. An attacker who has captured a session cookie through any means retains full authenticated access to the server regardless of subsequent password changes. This affects deployments using password-based authentication, particularly shared or public-facing servers where credential rotation is expected to revoke existing sessions. This issue has been fixed in version 2.18.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-41069 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and prior, a malformed HEIF sequence file can trigger an out-of-bounds read in core sequence parsing logic, causing DoS. A malformed file can have stco.entry_count == 0 (creating no chunks) while still passing validation because saio.entry_count == 0 matches, but with saiz.sample_count > 0 the SampleAuxInfoReader constructor still enters its loop. This leads to an out-of-bounds dereference on the empty chunks[0] in chunked mode.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-41071 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and prior, a crafted HEIF sequence file where the saiz box declares more samples than actually exist in the track's chunk table causes a heap-buffer-overflow (out-of-bounds read) in the SampleAuxInfoReader constructor. The SampleAuxInfoReader constructor iterates over saiz->get_num_samples() samples but doesn't validate that this count is consistent with the number of chunks in the chunks vector. When saiz declares more samples than the chunks cover, the loop increments current_chunk past chunks.size(), causing an out-of-bounds read on the chunks vector. The vulnerability is triggered during file parsing (heif_context_read_from_file) without any additional user interaction. Any application using libheif to open untrusted HEIF files is affected. This issue has been fixed in version 1.22.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-41131 OpenFGA is an authorization/permission engine built for developers. Prior to version 1.14.1, in specific scenarios, models using conditions with caching enabled can result in two different check requests producing the same cache key. This could result in OpenFGA reusing an earlier cached result for a subsequent request. The preconditions for vulnerability are the model having relations which rely on condition evaluation and the user having caching enabled. OpenFGA v1.14.1 contains a fix.

dex-grafana
mon_grafana

CVE-2026-41182 LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. Prior to version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK, the LangSmith SDK's output redaction controls (hideOutputs in JS, hide_outputs in Python) do not apply to streaming token events. When an LLM run produces streaming output, each chunk is recorded as a new_token event containing the raw token value. These events bypass the redaction pipeline entirely — prepareRunCreateOrUpdateInputs (JS) and _hide_run_outputs (Python) only process the inputs and outputs fields on a run, never the events array. As a result, applications relying on output redaction to prevent sensitive LLM output from being stored in LangSmith will still leak the full streamed content via run events. Version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK fix the issue.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

CVE-2026-41284 Allocation of Resources Without Limits or Throttling vulnerability in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117. Older, unsupported versions may also be affected. Users are recommended to upgrade to version [FIXED_VERSION], which fixes the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-41292 NLnet Labs Unbound up to and including version 1.25.0 is vulnerable to a degradation of service attack related to parsing long lists of incoming EDNS options. An adversary sending queries with too many EDNS options can hold Unbound threads hostage while they are parsing and creating internal data structures for the options. Coordinated attacks can result in degradation and/or denial of service. Unbound 1.25.1 contains a patch with a fix to limit acceptable incoming EDNS options (100).

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-41293 Improper Input Validation vulnerability in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 10.0.0-M1 through 10.0.27. Older, end of support versions may also be affected. Users are recommended to upgrade to version [FIXED_VERSION], which fixes the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-41305 PostCSS takes a CSS file and provides an API to analyze and modify its rules by transforming the rules into an Abstract Syntax Tree. Versions prior to 8.5.10 do not escape `</style>` sequences when stringifying CSS ASTs. When user-submitted CSS is parsed and re-stringified for embedding in HTML `<style>` tags, `</style>` in CSS values breaks out of the style context, enabling XSS. Version 8.5.10 fixes the issue.

cloudera-ai-agent-studio

CVE-2026-41324 basic-ftp is an FTP client for Node.js. Versions prior to 5.3.0 are vulnerable to denial of service through unbounded memory growth while processing directory listings from a remote FTP server. A malicious or compromised server can send an extremely large or never-ending listing response to `Client.list()`, causing the client process to consume memory until it becomes unstable or crashes. Version 5.3.0 fixes the issue.

cdsw-web

CVE-2026-41413 Istio is an open platform to connect, manage, and secure microservices. Prior to versions 1.28.6 and 1.29.2, when a RequestAuthentication resource is created with a jwksUri pointing to an internal service, istiod makes an unauthenticated HTTP GET request to that URL without filtering out localhost or link local ips. This can result in sensitive data being distributed to Envoy proxies via xDS configuration. This issue has been patched in versions 1.28.6 and 1.29.2.

install-cni
pilot
proxyv2

CVE-2026-41486 Ray is an AI compute engine. From version 2.54.0 to before version 2.55.0, Ray Data registers custom Arrow extension types (ray.data.arrow_tensor, ray.data.arrow_tensor_v2, ray.data.arrow_variable_shaped_tensor) globally in PyArrow. When PyArrow reads a Parquet file containing one of these extension types, it calls __arrow_ext_deserialize__ on the field's metadata bytes. Ray's implementation passes these bytes directly to cloudpickle.loads(), achieving arbitrary code execution during schema parsing, before any row data is read. This issue has been patched in version 2.55.0.

nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-41672 xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package allows attacker-controlled comment content to be serialized into XML without validating or neutralizing comment-breaking sequences. As a result, an attacker can terminate the comment early and inject arbitrary XML nodes into the serialized output. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

cdsw-web

CVE-2026-41673 xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, seven recursive traversals in lib/dom.js operate without a depth limit. A sufficiently deeply nested DOM tree causes a RangeError: Maximum call stack size exceeded, crashing the application. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

cdsw-web

CVE-2026-41674 xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package serializes DocumentType node fields (internalSubset, publicId, systemId) verbatim without any escaping or validation. When these fields are set programmatically to attacker-controlled strings, XMLSerializer.serializeToString can produce output where the DOCTYPE declaration is terminated early and arbitrary markup appears outside it. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

cdsw-web

CVE-2026-41675 xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package allows attacker-controlled processing instruction data to be serialized into XML without validating or neutralizing the PI-closing sequence ?>. As a result, an attacker can terminate the processing instruction early and inject arbitrary XML nodes into the serialized output. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

cdsw-web

CVE-2026-41695 Spring Data Commons applications may be vulnerable to denial of service through resource exhaustion when attacker-controlled property path strings are passed to MappingContext property path resolution. Affected versions: Spring Data Commons 4.0.0 through 4.0.5; 3.5.0 through 3.5.11; 3.4.0 through 3.4.14.

thunderhead-notification
thunderhead-remotecluster
thunderhead-usermanagement-private
thunderhead-userpreference

CVE-2026-41710 An attacker can craft a large number of unique requests that trigger a failure, exhausting the capacity of the application-wide stateful retry cache. Once the cache is full, it permanently rejects any further updates, causing all later stateful retries and circuit breakers in the application to fail. Affected versions: Spring Retry 2.0.0 through 2.0.12; 1.3.0 through 1.3.4.

thunderhead-notification

CVE-2026-41726 When an application opts into DelegatingDeserializer, a producer can grow the consumer's heap without bound by sending records with unique random spring.kafka.serialization.selector header values, eventually causing GC thrash and OutOfMemoryError. Affected versions: Spring for Apache Kafka 4.0.0 through 4.0.5; 3.3.0 through 3.3.15; 3.2.0 through 3.2.13; 2.9.0 through 2.9.13; 2.8.0 through 2.8.11.

thunderhead-notification

CVE-2026-41731 JsonKafkaHeaderMapper and the deprecated DefaultKafkaHeaderMapper matched type headers against trusted packages using a prefix check, meaning that trusting any package implicitly trusted all of its subpackages. Combined with Jackson's default bean deserialization, a producer could supply crafted header values that caused the consumer to deserialize arbitrary JDK types. Affected versions: Spring for Apache Kafka 4.0.0 through 4.0.5; 3.3.0 through 3.3.15; 3.2.0 through 3.2.13; 2.9.0 through 2.9.13; 2.8.0 through 2.8.11.

thunderhead-notification

CVE-2026-41907 uuid is for the creation of RFC9562 (formerly RFC4122) UUIDs. Prior to 14.0.0, v3, v5, and v6 accept external output buffers but do not reject out-of-range writes (small buf or large offset). This allows silent partial writes into caller-provided buffers. This vulnerability is fixed in 14.0.0.

cdsw-web
dpsgateway

CVE-2026-42046 libcaca is a colour ASCII art library. In 0.99.beta20 and earlier, an integer overflow vulnerability in libcaca's canvas import functionality allows an attacker to cause a controlled heap out-of-bounds write (heap overflow) by supplying a crafted file in the "caca" format. Depending on the build configuration and memory allocator, this may lead to memory corruption or remote code execution. This is the same vulnerability as CVE-2021-3410 but the fix at that time was not fully correct. Commit fb77acff9ba6bb01d53940da34fb10f20b156a23 fixes this vulnerability.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-42055 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_proxy_v2_module and ngx_http_grpc_module modules. This vulnerability exists when the proxy_http_version to 2 or grpc_pass directives are used to proxy HTTP/2 traffic, the ignore_invalid_headers directive is set to off, and the large_client_header_buffers directive size is larger than 2 megabytes. A remote, unauthenticated attacker, along with conditions beyond their control, could send large headers while creating an upstream request. This may cause a heap-based buffer overflow in the NGINX worker process leading to a restart. Additionally, attackers can execute code on systems with Address Space Layout Randomization (ASLR) disabled or when the attacker can bypass ASLR. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3
webhook-certgen

CVE-2026-42127 The public dashboard query endpoint does not limit request body size before processing, allowing unauthenticated attackers to trigger excessive memory allocation by sending arbitrarily large JSON payloads. This can lead to denial of service through memory exhaustion. No valid dashboard access token or authentication is required to exploit this vulnerability.

mon_grafana

CVE-2026-42266 JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.0.0 to 4.5.6, the allow-list of extensions that can be installed from PyPI Extension Manager (allowed_extensions_uris) is not correctly enforced by JupyterLab. The PyPI Extension Manager was not contained to packages listed on the default PyPI index. This vulnerability is fixed in 4.5.7.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2026-42305 Dulwich is a pure-Python implementation of the Git file formats and protocols. Versions starting with 0.10.0 and prior to 1.2.5 have an arbitrary file write leading to remote code execution when cloning or checking out a malicious Git repository on Windows. Dulwich's path-element validator accepted tree entries whose filenames contained bytes that Windows interprets as structural path syntax. Contributing configuration bugs made matters worse. The core.protectNTFS and core.protectHFS settings were looked up under a wrong option name and so user-set values were silently ignored, and core.protectNTFS only defaulted to true on Windows (Git upstream has defaulted it to true everywhere since CVE-2019-1353). Both have been corrected. Anyone who clones, fetches, or checks out an untrusted repository with Dulwich on Windows - either through the Dulwich CLI, porcelain.clone, or any downstream tool built on Dulwich - is impacted. POSIX clones are not directly exploitable (on POSIX \ is a literal filename byte), but a POSIX user can unknowingly propagate a malicious tree to Windows consumers via push or re-publication. This issue is fixed in Dulwich 1.2.5. Users should upgrade to 1.2.5 or later. There is no effective pre-patch workaround. On affected versions the core.protectNTFS configuration key was silently ignored, so setting it to true does not mitigate the issue. Users who cannot upgrade should avoid cloning, fetching, or checking out untrusted repositories with Dulwich on Windows. After upgrading the NTFS validator is on by default on every platform, so no additional configuration is required.

kserve_agent
kserve_controller
kserve_huggingfaceserver
kserve_localmodel_controller
kserve_qpext
kserve_router

CVE-2026-42328 No description available.

cdp-private
parcel

CVE-2026-42498 Exposure of HTTP Authentication Header to unexpected hosts during WebSocket authentication vulnerability in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.2 through 9.0.117, from 8.5.24 through 8.5.100, from 7.0.83 through 7.0.109. Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118, which fix the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-42526 In the AWS Secrets Manager and SSM Parameter Store secrets backends of `apache-airflow-providers-amazon` prior to 9.28.0, the team-scoping logic could resolve a `conn_id` containing a `/` (e.g. `"my_team/conn"`) to the same path as another team's team-scoped secret when the caller had no team context. A privileged caller without team context could therefore retrieve another team's secret by crafting a colliding `conn_id`. Fixed in 9.28.0 by switching the team-scope separator to `--` and rejecting team-shaped `conn_id`s when team context is absent. Affects the experimental multi-tenant teams feature only. Users are recommended to upgrade to `apache-airflow-providers-amazon` 9.28.0, which fixes the issue.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0

CVE-2026-42534 NLnet Labs Unbound up to and including version 1.25.0 has a vulnerability in the jostle logic that could defeat its purpose and degrade resolution performance. Retransmits of the same query could renew the age of slow running queries and not allow the jostle logic to see them as aged and potential targets for replacement with new queries. An adversary who can query a vulnerable Unbound and who can control a domain name server that replies slowly and/or maliciously to Unbound's queries can exploit the vulnerability and degrade the resolution performance of Unbound. When Unbound's 'num-queries-per-thread' reaches its limit, the jostle logic kicks in. When a new query comes in, half of the available queries that are also slow to resolve are candidates for replacement. The vulnerability then happens because duplicate queries that need resolution would skew the aging result by using the timestamp of the latest duplicate query instead of the original one that started the resolution effort. Cache and local data response performance remains unaffected. Coordinated attacks could raise this to a denial of resolution service. Unbound 1.25.1 contains a patch with a fix to attach an initial, non-updatable start time for incoming queries that allow the jostle logic to work as intended.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-42557 jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. Prior to 4.5.7, JupyterLab's HTML sanitizer allowlists data-commandlinker-command and data-commandlinker-args on button elements, while CommandLinker listens for all click events on document.body and executes the named command without checking whether the element came from trusted JupyterLab UI. A notebook with a pre-saved HTML cell output containing a deceptive button can trigger arbitrary JupyterLab commands - including arbitrary code execution - on a single user click, without any code being submitted for execution by the user. This vulnerability is fixed in 4.5.7.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

CVE-2026-42563 Dulwich is a pure-Python implementation of the Git file formats and protocols. Starting in version 0.24.0 and prior to version 1.2.5, Dulwich's `ProcessMergeDriver` substitutes the file path (from the git tree, controllable by an attacker via a malicious branch) into the merge driver command via the `%P` placeholder and executes it with `subprocess.run(..., shell=True)`. An attacker who can cause a victim to merge an untrusted branch can achieve arbitrary command execution by crafting malicious file paths. Version 1.2.5 fixes the issue.

kserve_agent
kserve_controller
kserve_huggingfaceserver
kserve_localmodel_controller
kserve_qpext
kserve_router

CVE-2026-42798 Little CMS (lcms2) 2.16 through 2.18 before 2.19 has an integer overflow in ParseCube in cmscgats.c.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-42923 NLnet Labs Unbound up to and including version 1.25.0 has a vulnerability in the DNSSEC validator where the code path to consult the negative cache for DS records does not take into account the limit on NSEC3 hash calculations introduced in 1.19.1. This leads to degradation of service during the attack. An adversary that controls a DNSSEC signed zone can exploit this by signing NSEC3 records with acceptably high iterations for child delegations and querying a vulnerable Unbound. Unbound will keep performing the allowed hash calculations on the NSEC3 records and will not limit the work by the mitigation introduced in 1.19.1. As a side effect, a global lock for the negative cache will be held for the duration of the hashing, blocking other threads that need to consult the negative cache. Coordinated attacks could raise the vulnerability to denial of service. Unbound 1.25.1 contains a patch with a fix to bound the vulnerable code path with the existing limit for NSEC3 hash calculations.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-42926 When NGINX Open Source is configured to proxy HTTP/2 traffic by setting proxy_http_version to 2, and also uses proxy_set_body, an attacker may be able to inject frame headers and payload bytes to the upstream peer.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-42934 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_charset_module module. When charset, source_charset, and charset_map and proxy_pass with disabled buffering ("off") directives are configured, unauthenticated attackers can send requests that with conditions beyond the attackers' control to cause a heap buffer over-read in the NGINX worker process, leading to limited disclosure of memory or a restart.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-42944 NLnet Labs Unbound 1.14.0 up to and including version 1.25.0 has a vulnerability that results in heap overflow when encoding multiple NSID and/or DNS Cookie EDNS and/or EDNS Padding options in the reply packet. The relevant options ('nsid', 'answer-cookie', 'pad-responses' (default)) need to be enabled for the vulnerability to be exploited. An adversary who can query Unbound can exploit the vulnerability by attaching multiple NSID and/or DNS Cookie EDNS and/or EDNS Padding options to the query. A flaw in the size calculation of the EDNS field truncates the correct value which allows the encoder to overflow the available space when writing. Those two combined lead to a heap overflow write of Unbound controlled data and eventually a crash. Unbound 1.25.1 contains a patch with a fix to de-duplicate the EDNS options and a fix to prevent truncation of the EDNS field size calculation.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-42945 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_rewrite_module module. This vulnerability exists when the rewrite directive is followed by a rewrite, if, or set directive and an unnamed Perl-Compatible Regular Expression (PCRE) capture (for example, $1, $2) with a replacement string that includes a question mark (?). An unauthenticated attacker along with conditions beyond its control can exploit this vulnerability by sending crafted HTTP requests. This may cause a heap buffer overflow in the NGINX worker process leading to a restart. Additionally, attackers can execute code on systems with Address Space Layout Randomization (ASLR) disabled or when the attacker can bypass ASLR.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-42946 A vulnerability exists in the ngx_http_scgi_module and ngx_http_uwsgi_module modules that may result in excessive memory allocation or an over-read of data. When scgi_pass or uwsgi_pass is configured, an unauthenticated attacker with man-in-the-middle (MITM) ability to control responses from an upstream server may be able to read the memory of the NGINX worker process or restart it.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-42959 NLnet Labs Unbound up to and including version 1.25.0 has a denial of service vulnerability in the DNSSEC validator that can lead to a crash given malicious upstream replies. When Unbound constructs chase-reply messages for validation, the code uses the wrong counter to calculate write offsets for ADDITIONAL section rrsets. DNAME duplication could increase the ANSWER section count and authority filtering could decrease the AUTHORITY section count and create an uninitialized array slot. Combining these two, the validator later dereferences this uninitialized pointer, causing an immediate process crash. An adversary controlling a DNSSEC-signed domain can trigger this bug with a single query by configuring a DNAME chain with unsigned CNAMEs and a response containing unsigned AUTHORITY records alongside signed ADDITIONAL glue records. Unbound 1.25.1 contains a patch with a fix to use the proper counters to calculate the write offsets.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-42960 NLnet Labs Unbound up to and including version 1.25.0 is vulnerable to poisoning via promiscuous records for the authority section. Promiscuous RRSets that complement DNS replies in the authority section can be used to trick Unbound to cache such records. If an adversary is able to attach such records in a reply (i.e., spoofed packet, fragmentation attack) he would be able to poison Unbound's cache. A malicious actor can exploit the possible poisonous effect by injecting RRSets other than NS that are also accompanied by address records in a reply, for example MX. This could be achieved by trying to spoof a reply packet or fragmentation attacks. Unbound would then accept the relative address records in the additional section and cache them if the authority RRSet has enough trust at this point, i.e., in-zone data for the delegation point. Unbound 1.25.1 contains a patch with a fix that disregards address records from the additional section if they are not explicitly relevant only to authority NS records, mitigating the possible poison effect. This is a complement fix to CVE-2025-11411.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-43010 In the Linux kernel, the following vulnerability has been resolved: bpf: Reject sleepable kprobe_multi programs at attach time kprobe.multi programs run in atomic/RCU context and cannot sleep. However, bpf_kprobe_multi_link_attach() did not validate whether the program being attached had the sleepable flag set, allowing sleepable helpers such as bpf_copy_from_user() to be invoked from a non-sleepable context. This causes a "sleeping function called from invalid context" splat: BUG: sleeping function called from invalid context at ./include/linux/uaccess.h:169 in_atomic(): 1, irqs_disabled(): 0, non_block: 0, pid: 1787, name: sudo preempt_count: 1, expected: 0 RCU nest depth: 2, expected: 0 Fix this by rejecting sleepable programs early in bpf_kprobe_multi_link_attach(), before any further processing.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-43125 In the Linux kernel, the following vulnerability has been resolved: dlm: validate length in dlm_search_rsb_tree The len parameter in dlm_dump_rsb_name() is not validated and comes from network messages. When it exceeds DLM_RESNAME_MAXLEN, it can cause out-of-bounds write in dlm_search_rsb_tree(). Add length validation to prevent potential buffer overflow.

cmlserving-triton-runtime
dex-runtime-python-builder-7.1.9.1078-compat
kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-43219 In the Linux kernel, the following vulnerability has been resolved: net: cpsw_new: Fix potential unregister of netdev that has not been registered yet If an error occurs during register_netdev() for the first MAC in cpsw_register_ports(), even though cpsw->slaves[0].ndev is set to NULL, cpsw->slaves[1].ndev would remain unchanged. This could later cause cpsw_unregister_ports() to attempt unregistering the second MAC. To address this, add a check for ndev->reg_state before calling unregister_netdev(). With this change, setting cpsw->slaves[i].ndev to NULL becomes unnecessary and can be removed accordingly.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-43240 In the Linux kernel, the following vulnerability has been resolved: x86/kexec: add a sanity check on previous kernel's ima kexec buffer When the second-stage kernel is booted via kexec with a limiting command line such as "mem=<size>", the physical range that contains the carried over IMA measurement list may fall outside the truncated RAM leading to a kernel panic. BUG: unable to handle page fault for address: ffff97793ff47000 RIP: ima_restore_measurement_list+0xdc/0x45a #PF: error_code(0x0000) – not-present page Other architectures already validate the range with page_is_ram(), as done in commit cbf9c4b9617b ("of: check previous kernel's ima-kexec-buffer against memory bounds") do a similar check on x86. Without carrying the measurement list across kexec, the attestation would fail.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-43274 In the Linux kernel, the following vulnerability has been resolved: mailbox: mchp-ipc-sbi: fix out-of-bounds access in mchp_ipc_get_cluster_aggr_irq() The cluster_cfg array is dynamically allocated to hold per-CPU configuration structures, with its size based on the number of online CPUs. Previously, this array was indexed using hartid, which may be non-contiguous or exceed the bounds of the array, leading to out-of-bounds access. Switch to using cpuid as the index, as it is guaranteed to be within the valid range provided by for_each_online_cpu().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-43303 In the Linux kernel, the following vulnerability has been resolved: mm/page_alloc: clear page->private in free_pages_prepare() Several subsystems (slub, shmem, ttm, etc.) use page->private but don't clear it before freeing pages. When these pages are later allocated as high-order pages and split via split_page(), tail pages retain stale page->private values. This causes a use-after-free in the swap subsystem. The swap code uses page->private to track swap count continuations, assuming freshly allocated pages have page->private == 0. When stale values are present, swap_count_continued() incorrectly assumes the continuation list is valid and iterates over uninitialized page->lru containing LIST_POISON values, causing a crash: KASAN: maybe wild-memory-access in range [0xdead000000000100-0xdead000000000107] RIP: 0010:__do_sys_swapoff+0x1151/0x1860 Fix this by clearing page->private in free_pages_prepare(), ensuring all freed pages have clean state regardless of previous use.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-43331 In the Linux kernel, the following vulnerability has been resolved: x86/kexec: Disable KCOV instrumentation after load_segments() The load_segments() function changes segment registers, invalidating GS base (which KCOV relies on for per-cpu data). When CONFIG_KCOV is enabled, any subsequent instrumented C code call (e.g. native_gdt_invalidate()) begins crashing the kernel in an endless loop. To reproduce the problem, it's sufficient to do kexec on a KCOV-instrumented kernel: $ kexec -l /boot/otherKernel $ kexec -e The real-world context for this problem is enabling crash dump collection in syzkaller. For this, the tool loads a panic kernel before fuzzing and then calls makedumpfile after the panic. This workflow requires both CONFIG_KEXEC and CONFIG_KCOV to be enabled simultaneously. Adding safeguards directly to the KCOV fast-path (__sanitizer_cov_trace_pc()) is also undesirable as it would introduce an extra performance overhead. Disabling instrumentation for the individual functions would be too fragile, so disable KCOV instrumentation for the entire machine_kexec_64.c and physaddr.c. If coverage-guided fuzzing ever needs these components in the future, other approaches should be considered. The problem is not relevant for 32 bit kernels as CONFIG_KCOV is not supported there. [ bp: Space out comment for better readability. ]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-43376 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix use-after-free by using call_rcu() for oplock_info ksmbd currently frees oplock_info immediately using kfree(), even though it is accessed under RCU read-side critical sections in places like opinfo_get() and proc_show_files(). Since there is no RCU grace period delay between nullifying the pointer and freeing the memory, a reader can still access oplock_info structure after it has been freed. This can leads to a use-after-free especially in opinfo_get() where atomic_inc_not_zero() is called on already freed memory. Fix this by switching to deferred freeing using call_rcu().

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-43402 In the Linux kernel, the following vulnerability has been resolved: kthread: consolidate kthread exit paths to prevent use-after-free Guillaume reported crashes via corrupted RCU callback function pointers during KUnit testing. The crash was traced back to the pidfs rhashtable conversion which replaced the 24-byte rb_node with an 8-byte rhash_head in struct pid, shrinking it from 160 to 144 bytes. struct kthread (without CONFIG_BLK_CGROUP) is also 144 bytes. With CONFIG_SLAB_MERGE_DEFAULT and SLAB_HWCACHE_ALIGN both round up to 192 bytes and share the same slab cache. struct pid.rcu.func and struct kthread.affinity_node both sit at offset 0x78. When a kthread exits via make_task_dead() it bypasses kthread_exit() and misses the affinity_node cleanup. free_kthread_struct() frees the memory while the node is still linked into the global kthread_affinity_list. A subsequent list_del() by another kthread writes through dangling list pointers into the freed and reused memory, corrupting the pid's rcu.func pointer. Instead of patching free_kthread_struct() to handle the missed cleanup, consolidate all kthread exit paths. Turn kthread_exit() into a macro that calls do_exit() and add kthread_do_exit() which is called from do_exit() for any task with PF_KTHREAD set. This guarantees that kthread-specific cleanup always happens regardless of the exit path - make_task_dead(), direct do_exit(), or kthread_exit(). Replace __to_kthread() with a new tsk_is_kthread() accessor in the public header. Export do_exit() since module code using the kthread_exit() macro now needs it directly.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-43512 DEPRECATED: Authentication Bypass Issues vulnerability in digest authentication in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from before 7.0.0. Older unsupported versions any also be affect Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-43513 Improper Handling of Case Sensitivity vulnerability in LockOutRealm in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from 7.0.0 through 7.0.109. Older unsupported versions may also be affected. Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-43514 Observable Timing Discrepancy vulnerability when comparing AJP secret in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from 7.0.0 through 7.0.109. Older unsupported versions may also be affected. Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-43515 Improper Authorization vulnerability when multiple method constraints define an HTTP method for the same extension in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from 7.0.0 through 7.0.109. Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

cloudera-ai-rag-studio
databus-producer
dex_thunderhead-dbuswxmclient
obs_agent
thunderhead-compute-api
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-userpreference
thunderhead-userpreference-api

CVE-2026-44016 Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. FIn versions >= 2.82.0, < 2.91.0, if the HTML backend was explicitly configured for rendering (rendering option by default deactivated), then the Playwright-based rendering feature could allow JavaScript execution and unrestricted network access when processing untrusted HTML documents. An attacker could craft malicious HTML that executes arbitrary JavaScript in the rendering context or makes unauthorized network requests to internal services, potentially leading to SSRF attacks, data exfiltration, or remote code execution in the rendering environment. This vulnerability is fixed in 2.91.0.

cloudera-ai-rag-studio

CVE-2026-44017 Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. Prior to 2.91.0, the EasyOCR model download functionality extracted ZIP archives without validating member paths, enabling Zip Slip attacks. If an attacker could compromise the model download source (via supply chain attack, DNS spoofing, or MITM), they could write arbitrary files to any location writable by the process, potentially achieving remote code execution by overwriting Python files or system binaries, persistent backdoors by modifying startup scripts or SSH keys, and data corruption or system compromise. This vulnerability is fixed in 2.91.0.

cloudera-ai-rag-studio

CVE-2026-44018 Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.45.0 until 2.91.0, the METS-GBS backend's XML parsing and the input document format detection lacked security controls. An attacker could craft malicious METS-GBS archives that, when processed, could read sensitive files, exhaust system resources, or cause application crashes. This vulnerability is fixed in 2.91.0.

cloudera-ai-rag-studio

CVE-2026-44019 Docling Core defines core data types and transformations for the document processing application Docling. In versions 2.5.0 and above, prior to 2.74.1, docling-core could allow local file:// image references and accepted inline data: content without a decoded-size limit. In applications that accept untrusted image references, this may allow access to local files readable by the process or excessive memory use from large inline payloads. This issue has been fixed in version 2.74.1.

cloudera-ai-rag-studio

CVE-2026-44022 Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.73.0 until 2.91.0, he LaTeX backend's handling of \includegraphics, \input, and \include commands lacked path containment validation. Attackers could craft malicious LaTeX documents with path traversal sequences to read arbitrary files from the file system accessible to the process, include sensitive files in the converted document output, or potentially access configuration files, credentials, or other sensitive data This vulnerability is fixed in 2.91.0.

cloudera-ai-rag-studio

CVE-2026-44023 Docling Core defines core data types and transformations for the document processing application Docling. In versions 1.5.0 and above, prior to 2.74.1, docling-core did not sufficiently restrict remote request destinations and could resolve a server-provided Content-Disposition to a local path in an unsafe manner. In applications that accept untrusted URLs, this could allow SSRF attacks targeting local files outside the user-defined cache directory. This issue has been fixed in version 2.74.1.

cloudera-ai-rag-studio

CVE-2026-44162 fluent-plugin-s3 is an Amazon S3 input and output plugin for Fluentd. From 0.7.0 to 1.8.4, the in_s3 input plugin reads the entire decompressed payload of gzip, lzma2, and lzop objects into memory without enforcing a decompression_size_limit. An attacker with permission to upload objects to the monitored S3 bucket can provide a highly compressed object that expands excessively when Fluentd processes it. The resulting memory exhaustion can cause the operating system to terminate the Fluentd process and disrupt all log collection on the affected node. This issue is fixed in version 1.8.5.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-44223 vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-44235 rabbitmq-c is a C-language AMQP client library for RabbitMQ. Prior to 0.16.0, a malicious AMQP server can send an undersized HEADER or METHOD frame during client login and cause unsigned size_t underflow in amqp_handle_input() in librabbitmq/amqp_connection.c. The parser subtracts HEADER_SIZE, fixed per-frame fields, and FOOTER_SIZE from state->target_size without first checking the minimum frame length. The wrapped encoded.len value is passed through amqp_decode_properties() to amqp_decode_table_internal(), where it defeats bounds checks and causes an out-of-bounds read and process crash. An on-path attacker can also trigger the issue when AMQP traffic is not protected by TLS with certificate validation. The demonstrated impact is denial of service, with no reliable memory disclosure or code execution shown. This issue is fixed in version 0.16.0.

cloudera-ai-rag-studio
kserve_huggingfaceserver
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-44236 rabbitmq-c is a C-language AMQP client library for RabbitMQ. Prior to 0.16.0, a malicious AMQP server can send an undersized connection.tune.frame_max value during amqp_login(), and rabbitmq-c accepts the value in amqp_login_inner() in librabbitmq/amqp_socket.c. amqp_tune_connection() in librabbitmq/amqp_connection.c uses frame_max to reallocate the outbound buffer without enforcing AMQP_FRAME_MIN_SIZE. Immediate serialization of connection.tune-ok through amqp_frame_to_bytes() writes beyond the undersized heap allocation, causing memory corruption and likely denial of service. An on-path attacker can also trigger the flaw against plaintext AMQP traffic. Code execution is theoretically possible but was not demonstrated. This issue is fixed in version 0.16.0.

cloudera-ai-rag-studio
kserve_huggingfaceserver
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
runtimedataviz

CVE-2026-44240 basic-ftp is an FTP client for Node.js. Prior to 5.3.1, basic-ftp is vulnerable to client-side denial of service when parsing FTP control-channel multiline responses. A malicious or compromised FTP server can send an unterminated multiline response during the initial FTP banner phase, before authentication. The client keeps appending attacker-controlled data into FtpContext._partialResponse and repeatedly reparses the accumulated buffer without enforcing a maximum control response size. As a result, an application using basic-ftp can remain stuck in connect() while memory and CPU usage grow under attacker-controlled input. This can lead to process-level denial of service, container OOM kills, worker restarts, queue backlog, or service degradation in applications that automatically connect to FTP endpoints. This vulnerability is fixed in 5.3.1.

cdsw-web

CVE-2026-44288 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs includes a minimal UTF-8 decoder that accepted overlong UTF-8 byte sequences and decoded them to their canonical characters instead of replacing them. An attacker who can provide protobuf binary data decoded through the affected UTF-8 path may be able to bypass application-level checks that inspect raw bytes before protobuf string decoding. For example, bytes that do not contain certain ASCII characters could decode to strings containing those characters. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44289 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs could recurse without a depth limit while decoding nested protobuf data. This affected both skipping unknown group fields and generated decoding of nested message fields. A crafted protobuf binary payload could cause the JavaScript call stack to be exhausted during decoding. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44290 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs allowed certain schema option paths to traverse through inherited object properties while applying options. A crafted protobuf schema or JSON descriptor could cause option handling to write to properties on global JavaScript constructors, corrupting process-wide built-in functionality. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44291 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs used plain objects with inherited prototypes for internal type lookup tables used by generated encode and decode functions. If Object.prototype had already been polluted, those lookup tables could resolve attacker-controlled inherited properties as valid protobuf type information. This could cause attacker-controlled strings to be emitted into generated JavaScript code. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44292 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs generated message constructors copied enumerable properties from a provided properties object without filtering the __proto__ key. If an application constructed a message from an attacker-controlled plain object, an own enumerable __proto__ property could alter the prototype of that individual message instance. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44293 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs generated JavaScript for toObject conversion could include an unsafe expression derived from a schema-controlled bytes field default value. A crafted descriptor with a non-string default value for a bytes field could cause attacker-controlled code to be emitted into the generated conversion function. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44294 protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs generated JavaScript property accessors from schema-controlled field and oneof names. Certain control characters in field names were not escaped before being embedded into generated function bodies. A crafted schema or JSON descriptor could therefore cause generated encode, decode, verify, or conversion functions to fail during compilation. This vulnerability is fixed in 7.5.6 and 8.0.2.

cdsw-web

CVE-2026-44390 NLnet Labs Unbound up to and including version 1.25.0 has a vulnerability when handling replies with very large RRsets that Unbound needs to perform name compression for. Malicious upstream responses with very large RRsets with records that don't share a suffix above the root can cause Unbound to spend a considerable time applying name compression to downstream replies. This can lead to degraded performance and eventually denial of service in well orchestrated attacks. An adversary can exploit the vulnerability by querying Unbound for the specially crafted contents of a malicious zone with very large RRsets. Before Unbound replies to the query it will try to apply name compression which was an unbounded operation that could lock the CPU until the whole packet was complete. A compression limit was introduced in 1.21.1 for this but it didn't account for the case where records would not share any suffix above the root. That causes Unbound to go in a different code path because of the compression tree lookup failure and eventually not increment the compression counter for those operations. Unbound 1.25.1 contains a patch with a fix that increments the compression counter regardless of the compression tree lookup. This is a complement fix to CVE-2024-8508.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-44486 Axios is a promise based HTTP client for the browser and Node.js. Prior to 0.32.0 and 1.16.0, Axios’ Node.js HTTP adapter can leak proxy credentials to a redirect target in affected versions. When a request is sent through an authenticated proxy, Axios may add a Proxy-Authorization header. If Axios then follows a redirect and the redirected request is no longer sent through that proxy, the stale Proxy-Authorization header can remain on the redirected request and be sent to the redirect target. This affects Node.js's use of Axios with automatic redirects enabled and an authenticated proxy configuration. Browser adapters are not affected. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44487 Axios is a promise based HTTP client for the browser and Node.js. Prior to 0.32.0 and 1.16.0, Axios’s Node.js HTTP adapter may forward a Proxy-Authorization header to a redirected origin during specific proxy-to-direct redirect flows. This affects Node.js usage, where an initial HTTP request is sent through an authenticated HTTP proxy, redirects are followed, and the redirected URL is no longer proxied. Under affected redirect shapes, the final origin can receive the proxy credential that was intended only for the outbound proxy. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44488 Axios is a promise based HTTP client for the browser and Node.js. Axios versions 1.7.0 through 1.15.x did not enforce configured request and response size limits when requests were sent with the fetch adapter. Applications that selected adapter: 'fetch', or ran in environments where axios resolved to the fetch adapter, could receive or send bodies larger than maxContentLength or maxBodyLength despite those limits being explicitly configured. This can cause resource exhaustion in server-side usage when a malicious or compromised server returns an oversized response, when an attacker can supply a large data: URL, or when an application forwards attacker-controlled request bodies through axios while relying on maxBodyLength as a boundary. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44489 Axios is a promise based HTTP client for the browser and Node.js. From 1.15.2 to before 1.16.0, nested objects created by utils.merge() (e.g., config.proxy) are still constructed as plain {} with Object.prototype in their chain. The setProxy() function at lib/adapters/http.js:209-223 reads proxy.username, proxy.password, and proxy.auth without hasOwnProperty checks. When Object.prototype.username is polluted, setProxy() constructs a Proxy-Authorization header with attacker-controlled credentials and injects it into every proxied HTTP request. This vulnerability is fixed in 1.16.0.

cdsw-web

CVE-2026-44490 Axios is a promise based HTTP client for the browser and Node.js. Prior to 0.32.0 and 1.16.0, axios exposes two read-side prototype-pollution gadgets. When Object.prototype is polluted by an upstream dependency in the same process (e.g. lodash _.merge / CVE-2018-16487), axios silently picks up the polluted values. (1) lib/utils.js line 406 builds merge()'s accumulator as result = {}, so result[targetKey] (line 414) walks Object.prototype and the polluted bucket's own keys are copied into the merged headers and ride out on the wire. (2) lib/core/mergeConfig.js line 26 builds the hasOwnProperty descriptor as a plain-object literal. Object.defineProperty reads descriptor.get/descriptor.set via the prototype chain, so a polluted Object.prototype.get or Object.prototype.set makes the call throw TypeError synchronously on every axios request. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44492 Axios is a promise based HTTP client for the browser and Node.js. Prior to 0.32.0 and 1.16.0, Axios does not normalise IPv4-mapped IPv6 addresses. When NO_PROXY lists an IPv4 address such as 127.0.0.1 or 169.254.169.254, a request URL using the IPv4-mapped IPv6 form (::ffff:7f00:1, ::ffff:a9fe:a9fe) still routes through the configured proxy. Node.js resolves these addresses to the underlying IPv4 host, so the request reaches the internal service via the proxy rather than being blocked. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44494 Axios is a promise based HTTP client for the browser and Node.js. From 1.0.0 to before 1.16.0, the Axios library is vulnerable to a Prototype Pollution "Gadget" attack that allows any Object.prototype pollution in the application's dependency tree to be escalated into a full Man-in-the-Middle (MITM) attack — intercepting, reading, and modifying all HTTP traffic including authentication credentials. The HTTP adapter at lib/adapters/http.js:670 reads config.proxy via standard property access, which traverses the prototype chain. Because proxy is not present in Axios defaults, the merged config object has no own proxy property, making it trivially injectable via prototype pollution. Once injected, setProxy() routes all HTTP requests through the attacker's proxy server. This vulnerability is fixed in 1.16.0.

cdsw-web

CVE-2026-44496 Axios is a promise based HTTP client for the browser and Node.js. Axios versions before 0.32.0 on the 0.x line and before 1.16.0 on the 1.x line build a regular expression from the configured XSRF cookie name without escaping regex metacharacters. In standard browser environments, an attacker who can influence the cookie name passed to axios can cause expensive regex backtracking while axios reads document.cookie. The practical impact is client-side availability degradation, such as freezing the affected browser tab while axios prepares a request. The issue does not affect ordinary Node.js HTTP adapter usage, React Native, or web workers, where axios does not read document.cookie. This vulnerability is fixed in 0.32.0 and 1.16.0.

cdsw-web

CVE-2026-44543 Local Path Provisioner provides a way for the Kubernetes users to utilize the local storage in each node. Prior to 0.0.36, a malicious user with permission to edit the local-path-config ConfigMap in the local-path-storage namespace can manipulate the helperPod.yaml template used by rancher/local-path-provisioner. The helperPod.yaml template is loaded by the provisioner and used to create HelperPods during PVC provisioning and cleanup operations. However, the template is not sufficiently validated before use. Security-sensitive fields such as securityContext.privileged, hostPath volumes, and Linux capabilities can be injected into the template. When a PVC operation triggers HelperPod creation, the provisioner creates the HelperPod using the attacker-controlled template. This can result in a privileged pod running on the target node with the host root filesystem mounted. This may allow the attacker to access sensitive host files, read ServiceAccount tokens from other pods on the same node, access other tenants' local-path volume data, or modify files on the host node. This vulnerability is fixed in 0.0.36.

local-path-provisioner

CVE-2026-44604 A command injection vulnerability was discovered in the `rpmuncompress` utility of RPM. When extracting certain archive formats (ZIP, 7z, GEM) to a specified destination directory, the tool inserts the archive's top-level folder name into a shell command without properly sanitizing it. A specially crafted archive containing shell metacharacters in its folder name can execute arbitrary commands as the user running the extraction.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-44608 NLnet Labs Unbound 1.14.0 up to and including version 1.25.0 has a locking inconsistency vulnerability that when certain conditions are met (multi-threaded, RPZ XFR reload, RPZ zone with 'rpz-nsip'/'rpz-nsdname' triggers) it could result in heap use-after-free and eventual crash. An adversary can exploit the vulnerability if conditions are first met on a vulnerable Unbound, i.e., multi-threaded, an RPZ zone with 'rpz-nsip'/'rpz-nsdname' triggers and an ongoing XFR for that RPZ zone. Local RPZ files do not trigger the vulnerability. If the timing is right and an XFR happens at the same time another thread needs to read that RPZ zone, the reader may not hold the lock long enough and the thread applying the XFR may free objects that the reader is about to walk causing the use-after-free. Unbound 1.25.1 contains a patch with a fix to the locking code.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-44708 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.2.1, the mistune math plugin renders inline math ($...$) and block math ($$...$$) by concatenating the raw user-supplied content directly into the HTML output without any HTML escaping. This occurs even when the parser is explicitly created with escape=True, which is supposed to guarantee that all user-controlled text is sanitised before reaching the DOM. This vulnerability is fixed in 3.2.1.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-44843 LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.85 and 1.3.3, LangChain contains older runtime code paths that deserialize run inputs, run outputs, or other application-controlled payloads using overly broad object allowlists. These paths may call load() with allowed_objects="all". This does not enable arbitrary Python object deserialization, but it does allow any trusted LangChain-serializable object to be revived, which is broader than these runtime paths require. As a result, attacker-supplied LangChain serialized constructor dictionaries may cause trusted runtime paths to instantiate classes with untrusted constructor arguments. This vulnerability is fixed in 0.3.85 and 1.3.3.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

CVE-2026-44896 Mistune is a Python Markdown parser with renderers and plugins. In 3.2.0 and realier, in src/mistune/directives/image.py, the render_figure() function concatenates figclass and figwidth options directly into HTML attributes without escaping. This allows attribute injection and XSS even when HTMLRenderer(escape=True) is used, because these values bypass the inline renderer.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-44897 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.2.1, HTMLRenderer.heading() builds the opening <hN> tag by string-concatenating the id attribute value directly into the HTML — with no call to escape(), safe_entity(), or any other sanitisation function. A double-quote character " in the id value terminates the attribute, allowing an attacker to inject arbitrary additional attributes (event handlers, src=, href=, etc.) into the heading element. This vulnerability is fixed in 3.2.1.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-44898 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.2.1, render_toc_ul() builds a <ul> table-of-contents tree from a list of (level, id, text) tuples. Both the id value (used as href="#<id>") and the text value (used as the visible link label) are inserted into <a> tags via a plain Python format string — with no HTML escaping applied to either value. When heading IDs are derived from user-supplied heading text (the standard use-case for readable slug anchors), an attacker can craft a heading whose text breaks out of the href="#..." attribute context, injecting arbitrary HTML tags including <script> blocks directly into the rendered TOC. This vulnerability is fixed in 3.2.1.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-44899 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.2.1, the Image directive plugin validates the :width: and :height: options with a regex compiled as _num_re = re.compile(r"^\d+(?:\.\d*)?"). When the validated value is not a plain integer, render_block_image() inserts it directly into a style="width:...;" or style="height:...;" attribute. Because the value was accepted by the prefix-only regex, any CSS after the leading digits reaches the style= attribute verbatim and without escaping. This vulnerability is fixed in 3.2.1.

cloudera-ai-agent-studio
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-workbench-python3.14-hardened

CVE-2026-44902 opentelemetry-js is the OpenTelemetry JavaScript Client. Prior to 0.217.0, a single malformed HTTP request crashes any Node.js process running the OpenTelemetry JS Prometheus exporter. The metrics endpoint (default 0.0.0.0:9464) has no error handling around URL parsing, so a request with an invalid URI causes an uncaught TypeError that terminates the process. This vulnerability is fixed in 0.217.0.

cdsw-web

CVE-2026-45134 LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. Prior to LangSmith SDK Python 0.8.0 and JS/TS 0.6.0, the LangSmith SDK's prompt pull methods (pull_prompt / pull_prompt_commit in Python, pullPrompt / pullPromptCommit in JS/TS) fetch and deserialize prompt manifests from the LangSmith Hub. These manifests may contain serialized LangChain objects and model configuration that affect runtime behavior. When pulling a public prompt by owner/name identifier, the manifest content is controlled by an external party, but prior versions of the SDK did not distinguish this from pulling a prompt within the caller's own organization. This vulnerability is fixed in LangSmith SDK Python 0.8.0 and JS/TS 0.6.0.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

CVE-2026-45135 Caddy is an extensible server platform that uses TLS by default. From 2.7.0 until 2.11.3, the FastCGI transport's splitPos() in modules/caddyhttp/reverseproxy/fastcgi/fastcgi.go misuses golang.org/x/text/search with search.IgnoreCase when the request path contains a non-ASCII byte. Two distinct flaws in that fallback let an attacker mislead Caddy's FastCGI splitting into treating a non-.php (or other configured split_path extension) file as a script. In any deployment where the attacker can place content into a file served via FastCGI (uploads, file storage, etc.), this can be escalated to remote code execution by crafting a URL whose path triggers either flaw. This vulnerability is fixed in 2.11.3.

cdwdataviz
runtimedataviz

CVE-2026-45149 The brace-expansion library generates arbitrary strings containing a common prefix and suffix. From 5.0.0 to before 5.0.6, the max option was being applied too late. When expanding a single large numeric range like {1..10000000}, the sequence generation loop generates all 10 million intermediate elements before the max limit is applied With max=10, the output is correctly limited to 10 items, but the process still allocates ~505 MB and spends ~800ms building the full intermediate array. This vulnerability is fixed in 5.0.6.

cloudera-ai-rag-studio

CVE-2026-45360 Apache Airflow's scheduler-side deadline-reference decoder (`SerializedCustomReference.deserialize_reference`) imported and dispatched arbitrary class paths drawn from DAG-author-controlled serialized state without an allowlist or plugin-registry gate. A DAG author whose code reaches the scheduler — the default on single-host deployments where the DAG bundle is importable from the scheduler process — could embed a custom `DeadlineReference` whose serialized form named an attacker-controlled module path, causing the scheduler to `import_string(...)` and instantiate that class with a live SQLAlchemy session attached. Affects deployments where DAG-author code is less trusted than the scheduler process. Users are advised to upgrade to `apache-airflow` 3.2.2 or later.

dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-airflow-connections-7.1.9.1078
dex-airflow-connections-7.3.1.709
dex-airflow-connections-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0

CVE-2026-45382 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.0.19, `decoder_context::decode_slice_unit_tiles` (libde265/decctx.cc:920) reads `pps.CtbAddrRStoTS[ctbAddrRS]` at line 966 where `ctbAddrRS = ctbY * ctbsWidth + ctbX` is computed from PPS-supplied `colBd[]`/`rowBd[]` arrays without validating the result against `CtbAddrRStoTS.size() == sps->PicSizeInCtbsY`. A malformed PPS that passes `set_derived_values` but encodes geometry inconsistent with the SPS produces a `ctbAddrRS` past the allocation, causing a 4-byte heap-buffer-overflow READ. Version 1.0.19 fixes the issue.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-45383 libde265 is an open source implementation of the h.265 video codec. Versions prior to 1.0.19 have a heap buffer overflow (out-of-bounds READ) exists in `decoder_context::decode_slice_unit_WPP()` in `libde265/decctx.cc`. When decoding a WPP (Wavefront Parallel Processing) HEVC slice, `ctbAddrRS` is computed as `ctbRow * ctbsWidth` inside the entry-point loop. If the PPS/SPS headers are crafted so that this value exceeds `pps.CtbAddrRStoTS.size()`, the subsequent array access `pps.CtbAddrRStoTS[ctbAddrRS]` reads past the end of the allocated vector, triggering a heap-buffer-overflow confirmed by AddressSanitizer. Version 1.0.19 patches the issue.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-45623 PostCSS takes a CSS file and provides an API to analyze and modify its rules by transforming the rules into an Abstract Syntax Tree. In versions 8.5.11 and prior, the PreviousMap parses the /*# sourceMappingURL=PATH */ comment from any CSS string passed to process() and dereferences PATH against the local filesystem with no scheme, allowlist, or traversal check. An attacker who controls the CSS input can cause the host process to read any file readable by Node and leak the first ~10 bytes of its content through the resulting JSON.parse SyntaxError message. The bug also yields a precise file-existence oracle and a controllable-read primitive that may be combined with large-file targets for DoS. The behaviour is triggered with PostCSS's default options — no from, no map, no plugins required — and is therefore reachable from any pipeline that runs untrusted CSS through PostCSS (CMS themes, user-uploaded styles, browser-extension/userstyle processors, build pipelines for third-party packages, blog comment renderers, etc.). This issue has been fixed in version 8.5.12.

cloudera-ai-agent-studio

CVE-2026-45692 Caddy is an extensible server platform that uses TLS by default. From 2.4.0 until 2.11.3, the authorization layer and the /config traversal layer do not agree on what object the path refers to. In this case, a path authorized for one config object is accepted, but then resolves to a different config object during traversal. This happens because the authorization layer uses string prefix matching and the /config traversal layer parses array indices numerically using strconv.Atoi(). This vulnerability is fixed in 2.11.3.

cdwdataviz
runtimedataviz

CVE-2026-45850 In the Linux kernel, the following vulnerability has been resolved: ipvs: skip ipv6 extension headers for csum checks Protocol checksum validation fails for IPv6 if there are extension headers before the protocol header. iph->len already contains its offset, so use it to fix the problem.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-45898 In the Linux kernel, the following vulnerability has been resolved: RDMA/iwcm: Fix workqueue list corruption by removing work_list The commit e1168f0 ("RDMA/iwcm: Simplify cm_event_handler()") changed the work submission logic to unconditionally call queue_work() with the expectation that queue_work() would have no effect if work was already pending. The problem is that a free list of struct iwcm_work is used (for which struct work_struct is embedded), so each call to queue_work() is basically unique and therefore does indeed queue the work. This causes a problem in the work handler which walks the work_list until it's empty to process entries. This means that a single run of the work handler could process item N+1 and release it back to the free list while the actual workqueue entry is still queued. It could then get reused (INIT_WORK...) and lead to list corruption in the workqueue logic. Fix this by just removing the work_list. The workqueue already does this for us. This fixes the following error that was observed when stress testing with ucmatose on an Intel E830 in iWARP mode: [ 151.465780] list_del corruption. next->prev should be ffff9f0915c69c08, but was ffff9f0a1116be08. (next=ffff9f0a15b11c08) [ 151.466639] ------------[ cut here ]------------ [ 151.466986] kernel BUG at lib/list_debug.c:67! [ 151.467349] Oops: invalid opcode: 0000 [#1] SMP NOPTI [ 151.467753] CPU: 14 UID: 0 PID: 2306 Comm: kworker/u64:18 Not tainted 6.19.0-rc4+ #1 PREEMPT(voluntary) [ 151.468466] Hardware name: QEMU Ubuntu 24.04 PC (i440FX + PIIX, 1996), BIOS 1.16.3-debian-1.16.3-2 04/01/2014 [ 151.469192] Workqueue: 0x0 (iw_cm_wq) [ 151.469478] RIP: 0010:__list_del_entry_valid_or_report+0xf0/0x100 [ 151.469942] Code: c7 58 5f 4c b2 e8 10 50 aa ff 0f 0b 48 89 ef e8 36 57 cb ff 48 8b 55 08 48 89 e9 48 89 de 48 c7 c7 a8 5f 4c b2 e8 f0 4f aa ff <0f> 0b 66 2e 0f 1f 84 00 00 00 00 00 0f 1f 40 00 90 90 90 90 90 90 [ 151.471323] RSP: 0000:ffffb15644e7bd68 EFLAGS: 00010046 [ 151.471712] RAX: 000000000000006d RBX: ffff9f0915c69c08 RCX: 0000000000000027 [ 151.472243] RDX: 0000000000000000 RSI: 0000000000000000 RDI: ffff9f0a37d9c600 [ 151.472768] RBP: ffff9f0a15b11c08 R08: 0000000000000000 R09: c0000000ffff7fff [ 151.473294] R10: 0000000000000001 R11: ffffb15644e7bba8 R12: ffff9f092339ee68 [ 151.473817] R13: ffff9f0900059c28 R14: ffff9f092339ee78 R15: 0000000000000000 [ 151.474344] FS: 0000000000000000(0000) GS:ffff9f0a847b5000(0000) knlGS:0000000000000000 [ 151.474934] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 151.475362] CR2: 0000559e233a9088 CR3: 000000020296b004 CR4: 0000000000770ef0 [ 151.475895] PKRU: 55555554 [ 151.476118] Call Trace: [ 151.476331] <TASK> [ 151.476497] move_linked_works+0x49/0xa0 [ 151.476792] __pwq_activate_work.isra.46+0x2f/0xa0 [ 151.477151] pwq_dec_nr_in_flight+0x1e0/0x2f0 [ 151.477479] process_scheduled_works+0x1c8/0x410 [ 151.477823] worker_thread+0x125/0x260 [ 151.478108] ? __pfx_worker_thread+0x10/0x10 [ 151.478430] kthread+0xfe/0x240 [ 151.478671] ? __pfx_kthread+0x10/0x10 [ 151.478955] ? __pfx_kthread+0x10/0x10 [ 151.479240] ret_from_fork+0x208/0x270 [ 151.479523] ? __pfx_kthread+0x10/0x10 [ 151.479806] ret_from_fork_asm+0x1a/0x30 [ 151.480103] </TASK>

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-45930 In the Linux kernel, the following vulnerability has been resolved: net: mctp: ensure our nlmsg responses are initialised Syed Faraz Abrar (@farazsth98) from Zellic, and Pumpkin (@u1f383) from DEVCORE Research Team working with Trend Micro Zero Day Initiative report that a RTM_GETNEIGH will return uninitalised data in the pad bytes of the ndmsg data. Ensure we're initialising the netlink data to zero, in the link, addr and neigh response messages.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-45966 In the Linux kernel, the following vulnerability has been resolved: apparmor: fix NULL pointer dereference in __unix_needs_revalidation When receiving file descriptors via SCM_RIGHTS, both the socket pointer and the socket's sk pointer can be NULL during socket setup or teardown, causing NULL pointer dereferences in __unix_needs_revalidation(). This is a regression in AppArmor 5.0.0 (kernel 6.17+) where the new __unix_needs_revalidation() function was added without proper NULL checks. The crash manifests as: BUG: kernel NULL pointer dereference, address: 0x0000000000000018 RIP: aa_file_perm+0xb7/0x3b0 (or +0xbe/0x3b0, +0xc0/0x3e0) Call Trace: apparmor_file_receive+0x42/0x80 security_file_receive+0x2e/0x50 receive_fd+0x1d/0xf0 scm_detach_fds+0xad/0x1c0 The function dereferences sock->sk->sk_family without checking if either sock or sock->sk is NULL first. Add NULL checks for both sock and sock->sk before accessing sk_family.

ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-45993 In the Linux kernel, the following vulnerability has been resolved: LoongArch: Add spectre boundry for syscall dispatch table The LoongArch syscall number is directly controlled by userspace, but does not have a array_index_nospec() boundry to prevent access past the syscall function pointer tables.

cmlserving-triton-runtime
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-46039 In the Linux kernel, the following vulnerability has been resolved: rxgk: Fix potential integer overflow in length check Fix potential integer overflow in rxgk_extract_token() when checking the length of the ticket. Rather than rounding up the value to be tested (which might overflow), round down the size of the available data.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-46118 In the Linux kernel, the following vulnerability has been resolved: pseries/papr-hvpipe: Fix null ptr deref in papr_hvpipe_dev_create_handle() commit 6d3789d347a7 ("papr-hvpipe: convert papr_hvpipe_dev_create_handle() to FD_PREPARE()"), changed the create handle to FD_PREPARE(), but it caused kernel null-ptr-deref because after call to retain_and_null_ptr(src_info), src_info is re-used for adding it to the global list. Getting the following kernel panic in papr_hvpipe_dev_create_handle() when trying to add src_info to the list. Kernel attempted to write user page (0) - exploit attempt? (uid: 0) BUG: Kernel NULL pointer dereference on write at 0x00000000 Faulting instruction address: 0xc0000000001b44a0 Oops: Kernel access of bad area, sig: 11 [#1] ... Call Trace: papr_hvpipe_dev_ioctl+0x1f4/0x48c (unreliable) sys_ioctl+0x528/0x1064 system_call_exception+0x128/0x360 system_call_vectored_common+0x15c/0x2ec Now, the error handling with FD_PREPARE's file cleanup and __free(kfree) auto cleanup is getting too convoluted. This is mainly because we need to ensure only 1 user get the srcID handle. To simplify this, we allocate prepare the src_info in the beginning and add it to the global list under a spinlock after checking that no duplicates exist. This simplify the error handling where if the FD_ADD fails, we can simply remove the src_info from the list and consume any pending msg in hvpipe to be cleared, after src_info became visible in the global list.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-46194 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-46203 In the Linux kernel, the following vulnerability has been resolved: spi: cadence-quadspi: fix unclocked access on unbind Make sure that the controller is runtime resumed before disabling it during driver unbind to avoid an unclocked register access. This issue was flagged by Sashiko when reviewing a controller deregistration fix.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-46252 In the Linux kernel, the following vulnerability has been resolved: regulator: core: fix locking in regulator_resolve_supply() error path If late enabling of a supply regulator fails in regulator_resolve_supply(), the code currently triggers a lockdep warning: WARNING: drivers/regulator/core.c:2649 at _regulator_put+0x80/0xa0, CPU#6: kworker/u32:4/596 ... Call trace: _regulator_put+0x80/0xa0 (P) regulator_resolve_supply+0x7cc/0xbe0 regulator_register_resolve_supply+0x28/0xb8 as the regulator_list_mutex must be held when calling _regulator_put(). To solve this, simply switch to using regulator_put(). While at it, we should also make sure that no concurrent access happens to our rdev while we clear out the supply pointer. Add appropriate locking to ensure that. While the code in question will be removed altogether in a follow-up commit, I believe it is still beneficial to have this corrected before removal for future reference.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-46316 In the Linux kernel, the following vulnerability has been resolved: KVM: arm64: vgic-its: Drop the translation cache reference only for the erased entry vgic_its_invalidate_cache() walks the per-ITS translation cache with xa_for_each() and drops the cache's reference on each entry with vgic_put_irq(). It puts the iterated pointer, though, rather than the value returned by xa_erase(). The function is called from contexts that do not exclude one another: the ITS command handlers hold its_lock, the GITS_CTLR write path holds cmd_lock, and the path that clears EnableLPIs in a redistributor's GICR_CTLR holds neither. Two or more of them can drain the same cache concurrently, and if each one observes the same entry, erases it and then puts it, the single reference the cache holds on that entry is dropped more than once. The entry can then be freed while an ITE still maps it. xa_erase() is atomic and returns the previous entry, so put only the entry that this context actually removed. The cache reference is then dropped exactly once per entry even when the invalidations run concurrently, and the behavior is unchanged when only one context runs.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-46321 In the Linux kernel, the following vulnerability has been resolved: tun: free page on short-frame rejection in tun_xdp_one() tun_xdp_one() returns -EINVAL on a frame shorter than ETH_HLEN without freeing the page that vhost_net_build_xdp() allocated for it. tun_sendmsg() discards that -EINVAL and still returns total_len, so vhost_tx_batch() takes the success path and never frees the page; each short frame in a batch leaks one page-frag chunk. A local process that can open /dev/net/tun and /dev/vhost-net can hit this path: it attaches a tun/tap device as the vhost-net backend and feeds TX descriptors whose length minus the virtio-net header is below ETH_HLEN. Each kick leaks the page-frag chunks for that batch, and a tight submission loop exhausts host memory and triggers an OOM panic. Free the page before returning -EINVAL, matching the XDP-program error path in the same function.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-46862 Vulnerability in the MySQL Router product of Oracle MySQL (component: Router: General). Supported versions that are affected are 8.4.0-8.4.9 and 9.0.0-9.7.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via TLS to compromise MySQL Router. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Router. CVSS 3.1 Base Score 7.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-46863 Vulnerability in the MySQL Server, MySQL Cluster product of Oracle MySQL (component: Server: Connection Handling). Supported versions that are affected are MySQL Server: 8.4.0-8.4.9, 9.0.0-9.7.0; MySQL Cluster: 8.0.11-8.0.46, 8.4.0-8.4.9 and 9.0.0-9.7.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise MySQL Server, MySQL Cluster. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of MySQL Server, MySQL Cluster. CVSS 3.1 Base Score 7.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-47101 LiteLLM prior to 1.83.14 allows an authenticated internal_user to create API keys with access to routes that their role does not permit. When generating a key, the allowed_routes field is stored without verifying that the specified routes fall within the user's own permissions. A key created with access to admin-only routes can then be used to reach those routes successfully, bypassing the role-based access controls that would otherwise block the request, enabling full privilege escalation from internal_user to proxy_admin.

cloudera-ai-rag-studio
nim-deepseek-r1-v1.7.3

CVE-2026-47102 LiteLLM prior to 1.83.10 allows a user to modify their own user_role via the /user/update endpoint. While the endpoint correctly restricts users to updating only their own account, it does not restrict which fields may be changed. A user who can reach this endpoint can set their role to proxy_admin, gaining full administrative access to LiteLLM including all users, teams, keys, models, and prompt history. Users with the org_admin role have legitimate access to this endpoint and can exploit this vulnerability without chaining any additional flaw.

cloudera-ai-rag-studio
nim-deepseek-r1-v1.7.3

CVE-2026-47178 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.19.0 through 1.21.2, a crafted HEIF file (uncompressed `unci` codec, tiled, component-interleaved, 4:2:0) triggers a heap out-of-bounds write in libheif's uncompressed tile decoder. The write overwrites the C++ vtable pointer of an adjacent `unc_decoder_component_interleave` object; the next virtual call dispatches to an attacker-chosen address. Version 1.22.0 patches the issue.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-47214 Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. Prior to 2.94.0, the HTML backend has unsafe URI and path handling. This vulnerability is fixed in 2.94.0.

cloudera-ai-rag-studio

CVE-2026-47247 libheif is a HEIF and AVIF file format decoder and encoder. Prior to version 1.22.0, two bugs in libheif chain to leak process heap memory as visible pixel values in decoded grid images. An attacker who uploads a crafted AVIF/HEIC file to any server-side image processor (WordPress, Sharp/libvips, ImageMagick, etc.) can recover heap data - including library function pointers sufficient to defeat ASLR, or any other secret - from the publicly-downloadable transcoded JPEG/PNG/WebP output. Local attack vectors are also possible. Version 1.22.0 fixes the issue.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-47709 libheif is a HEIF and AVIF file format decoder and encoder. Versions prior to 1.22.0 crashes in the public C API `heif_image_handle_get_image_tiling()` when a malformed uncompressed HEIF image item has an associated `uncC` property but no associated `ispe` property. In debug builds this trips the `ispe && uncC` assertion in `ImageItem_uncompressed::get_heif_image_tiling()`. In a release/NDEBUG ASan build, the same file causes a null pointer read at address `0xa8`. Version 1.22.0 fixes the issue.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-47712 Dulwich is a pure-Python implementation of the Git file formats and protocols. Starting in version 0.24.0 and prior to version 1.2.5, dulwich.porcelain.format_patch(outdir=...) derives each patch filename from the commit's subject line. Prior to this fix, get_summary only replaced spaces with dashes - path separators (/, \), parent-directory components (..), and other filename-hostile characters (e.g. :) were preserved verbatim and passed straight into os.path.join(outdir, f"{i:04d}-{summary}.patch"). A malicious commit subject could therefore direct the generated patch file outside the requested outdir. This is fixed in Dulwich 1.2.5. Users should upgrade to 1.2.5 or later. dulwich.patch.get_summary now mirrors git's format_sanitized_subject: only `[A-Za-z0-9._]` are kept, runs of other characters collapse to a single -, consecutive . collapse to a single ., trailing ./- are stripped, and the result is length-limited. This makes the returned string safe to embed as a filename component, so format_patch can no longer be steered out of outdir via the commit subject. Until upgrading, callers that pass untrusted commits to porcelain.format_patch can use stdout=True and write the patch to a destination they control, rather than letting format_patch choose the filename; validate the chosen path before opening - e.g. compare os.path.realpath(returned_path) against os.path.realpath(outdir) and reject any patch whose resolved path is not inside outdir; and/or pre-screen commits and refuse to format any whose subject's first line contains /, \, .., or other characters that are not safe on the target filesystem.

kserve_huggingfaceserver

CVE-2026-47714 libheif is a HEIF and AVIF file format decoder and encoder. In versions 1.21.2 and prior, the inline mask parsing code in `libheif/region.cc` contains an integer overflow. Both `width` and `height` are `unsigned int` (32-bit) values parsed from the HEIF file. Their product can exceed `UINT32_MAX`, wrapping to a small value before the division by 8. This causes an undersized buffer allocation, leading to out-of-bounds memory access when the mask data is later interpreted as a `width x height` bitmap. Version 1.22.0 patches the issue.

ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-47734 Dulwich is a pure-Python implementation of the Git file formats and protocols. Starting in version 0.1.0 and prior to version 1.2.5, a client with push access could push a tiny crafted thin pack (~174 bytes) whose delta header declares a huge dest_size. When dulwich ingested it via add_thin_pack / apply_delta, it would allocate hundreds of MB of memory based on that attacker-controlled size, with no relationship to the actual bytes received. Operators running a Dulwich-based Git server that exposes git-receive-pack (i.e. accepts pushes) - for example via dulwich.server functionality, the HTTP smart server, or anything built on ReceivePackHandler - are impacted. The issue is patched in 1.2.5. add_thin_pack now accepts a max_input_size keyword (bytes; 0/None = unlimited, matching git's semantics), and ReceivePackHandler reads receive.maxInputSize from the repository config and passes it through. Wire reads are counted and a PackInputTooLarge exception is raised once the cap is exceeded - equivalent to git index-pack --max-input-size. Users should upgrade to Dulwich 1.2.5 or later and set receive.maxInputSize in their server's repository config to a sane bound for their environment. On unpatched versions, receive.maxInputSize has no effect, so it cannot be used as a workaround. Until upgrading, operators should restrict dulwich-receive-pack (push) access to trusted, authenticated clients only, or disable it entirely on servers that only need to serve fetches and/or run the server under an OS-level memory limit (e.g. ulimit, cgroups/MemoryMax, or a container memory limit) so a malicious push is killed rather than taking down the host.

kserve_huggingfaceserver

CVE-2026-47838 SubjectDnX509PrincipalExtractor does not correctly handle certain malformed X.509 certificate CN values, which can lead to reading the wrong value for the username. In a carefully crafted certificate, this can lead to an attacker impersonating another user. Affected versions: Spring Security 5.7.0 through 5.7.24; 5.8.0 through 5.8.26; 6.3.0 through 6.3.17; 6.4.0 through 6.4.17; 6.5.0 through 6.5.10.

thunderhead-consoleauthenticationcdp

CVE-2026-48029 libheif is a HEIF and AVIF file format decoder and encoder. Versions 1.19.0 through 1.21.2 have a heap OOB read in ImageItem_Grid::decode_grid_tile via irot-induced tile-coordinate underflow. Version 1.22.0 fixes the issue.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-48142 NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_charset_module module. When content is served or proxied through a location block with both source_charset utf-8; and a charset directive (for example, charset koi8-r;) configured, remote, unauthenticated attackers can send requests (in conjunction with conditions beyond their control) to cause a heap buffer over-read in the NGINX worker process, leading to limited disclosure of memory or a restart. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3
webhook-certgen

CVE-2026-48779 ws is an open source WebSocket client and server for Node.js. All versions from 1.1.0 up to (but not including) 5.2.5, from 6.0.0 up to 6.2.4, from 7.0.0 up to 7.5.11, and from 8.0.0 up to 8.21.0 are affected by a memory exhaustion DoS vulnerability. A peer can send a high volume of exceptionally small fragments and data chunks, with modest network traffic, to force the remote peer into allocating and holding structural wrappers that consume far more memory than the default documented message-size limit, leading to process termination due to OOM. This issue has been fixed in versions 5.2.5, 6.2.4, 7.5.11, and 8.21.0.

cdsw-web

CVE-2026-48801 linkify-it is a links recognition library with full Unicode support. Prior to 5.0.1, LinkifyIt.prototype.match, the package's primary public API, has O(N²) algorithmic complexity for inputs containing many fuzzy links or emails because the JavaScript-level scan loop re-slices input and re-runs unanchored regex searches on progressively shorter tails. Any service that synchronously renders untrusted Markdown with linkify:true on a request hot path can inherit a worker-process denial of service triggerable by a tens-of-KB request body. This issue is fixed in version 5.0.1.

cloudera-ai-agent-studio

CVE-2026-48816 sigstore-js provides JavaScript libraries for interacting with Sigstore services. Prior to 3.1.1, @sigstore/verify derives a transparency-log timestamp from tlogEntries[].integratedTime for bundle v0.2 inclusionProof-only entries even though the inclusion proof path does not cryptographically bind integratedTime, allowing an attacker who can supply an untrusted bundle to influence certificate validity and timestampThreshold verification decisions. This issue is fixed in version 3.1.1.

cloudera-ai-rag-studio

CVE-2026-48864 A flaw was found in libsolv. This heap buffer overflow occurs during the decompression of attacker-controlled compressed data within `.solv` files due to insufficient input validation. An attacker can provide a specially crafted `.solv` file, which, when processed by a vulnerable application, can lead to out-of-bounds memory access. This could result in information disclosure, alteration of program execution, or a denial of service.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-48988 markdown-it is a Markdown parser. Versions 14.1.1 and below contain a denial-of-service vulnerability when typographer: true is enabled, due to quadratic (O(n^2)) processing in the smartquotes rule. The issue stems from repeatedly modifying strings with replaceAt(), which performs O(n) slicing and concatenation per quote character. This can cause excessive CPU consumption when parsing quote-heavy, user-supplied markdown and may let attackers degrade or disrupt service availability. Although typographer is disabled by default, many production apps enable it for smart typography, making the issue relevant. This issue has been fixed in version 14.2.0.

cloudera-ai-agent-studio

CVE-2026-49295 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.0.20, a crafted H.265 bitstream can cause an out-of-bounds array write in `decoder_context::process_reference_picture_set()` (`libde265/decctx.cc:1376`). The root cause is a missing aggregate bound check on predicted short-term reference picture set entries. Individual list sizes are validated, but the combined count after predicted RPS construction can exceed the 16-entry `PocStFoll` array, writing at index 16. Version 1.0.20 patches the issue.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-49337 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.0.20, a crafted sequence of H.265 NAL units causes `decoder_context::read_slice_NAL()` (`libde265/decctx.cc:481`) to attach slice headers to a finished picture object that has no active image unit, resulting in attacker-controlled unbounded heap growth. The retained headers are never freed until the picture is released, which may not happen during continuous streaming. Version 1.0.20 patches the issue.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-49346 libde265 is an open source implementation of the h.265 video codec. Prior to version 1.1.0, a crafted H.265 bitstream with large SPS dimensions and 16-bit bit depth causes a signed integer overflow in `de265_image_get_buffer()` (`libde265/image.cc:128`). The overflow wraps the plane allocation size to a small value (~1 KB), but the subsequent `fill_image()` call computes the real size using `size_t`, writing ~4 GB into the undersized heap buffer. Version 1.1.0 patches the issue.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-49468 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.84.0, a Host-header parsing flaw in the LiteLLM proxy could, under specific conditions, allow unauthenticated access to protected management routes. The auth layer derived the effective route from request.url.path in litellm/proxy/auth/auth_utils.py::get_request_route(), which Starlette reconstructs from the Host header. A crafted Host could therefore make the auth gate evaluate a different route from the one FastAPI dispatched. This vulnerability is fixed in 1.84.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
nim-deepseek-r1-v1.7.3

CVE-2026-49851 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, Mistune is vulnerable to a CPU exhaustion DoS due to superlinear (approximately O(n²)) behavior in parse_link_text. When parsing Markdown containing many consecutive [ characters, parse_link_text repeatedly scans the input using a regex search inside a loop. Each iteration re-scans a large portion of the remaining string, resulting in quadratic-time behavior. An attacker-controlled Markdown input can therefore trigger excessive CPU usage with a very small payload. This vulnerability is fixed in 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-50142 libheif is a HEIF and AVIF file format decoder and encoder. From 1.19.0 until 1.23.0, a crafted HEIF sequence accepted by heif_context_read_from_memory() with the msf1 sequence brand can cause unbounded heap allocation. In libheif/sequences/seq_boxes.cc, Box_stsz::parse() applies max_sequence_frames only to variable-size samples, so fixed-size mode accepts an attacker-controlled sample_count without a bound. In libheif/sequences/track.cc, Track::load() also adds current_sample_idx and samples_per_chunk in 32-bit arithmetic, allowing the consistency check to be bypassed by wraparound. The resulting values reach the Chunk::Chunk() allocation path, which can consume gigabytes of memory and crash or stall the process through memory exhaustion. This issue is fixed in version 1.23.0.

ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-50274 Datadog dd-trace-go is a Go client library for Datadog application performance monitoring, profiling, and security monitoring. Prior to 2.8.1, Datadog tracing libraries that implement W3C baggage propagation parse incoming baggage HTTP headers without enforcing DD_TRACE_BAGGAGE_MAX_ITEMS or DD_TRACE_BAGGAGE_MAX_BYTES limits on the extract path. A remote, unauthenticated attacker can send a request whose baggage header contains an arbitrarily large number of comma-separated key-value pairs or a single very large value, causing unbounded CPU and memory consumption and enabling a remote denial of service against HTTP services with baggage propagation enabled. This issue is fixed in version 2.8.1.

traefik

CVE-2026-52726 Dulwich is a pure-Python implementation of the Git file formats and protocols. Starting in version 0.23.2 and prior to version 1.2.5, `dulwich.porcelain.submodule_update`, and by extension `porcelain.clone(..., recurse_submodules=True)`, materializes attacker-controlled submodule paths from a crafted upstream repository without path validation. A malicious `.gitmodules` plus a matching tree gitlink whose `path` is `.git/hooks` (or any other directory inside the parent repository's `.git` directory) causes the attacker's submodule tree contents to be written directly into the victim's `.git/hooks/` directory, preserving executable mode bits. The dropped executables are then run by any subsequent `git` or `dulwich` command that invokes the matching hook, resulting in arbitrary code execution. This is the dulwich equivalent of the upstream Git fixes for CVE-2024-32002 / CVE-2024-32004, which were never propagated into dulwich's separately implemented submodule porcelain. Version 1.2.5 patches the issue.

kserve_huggingfaceserver

CVE-2026-52844 Caddy is an extensible server platform that uses TLS by default. Prior to 2.11.4, on Windows, Caddy path matchers treat /private\secret.txt as outside /private/*, but file_server later resolves the same request path as private\secret.txt on disk. An unauthenticated remote client can bypass Caddy path-scoped auth/deny routes protecting /private/*. This vulnerability is fixed in 2.11.4.

cdwdataviz
runtimedataviz

CVE-2026-52845 Caddy is an extensible server platform that uses TLS by default. Prior to 2.11.4, forward_auth copy_headers deletes the exact client-supplied identity header before copying the trusted value from the auth gateway. But when the request later goes through php_fastcgi, Caddy normalizes HTTP headers into CGI variables by replacing - with _. This lets a client send an underscore alias that survives the forward_auth delete step but becomes the same PHP/FastCGI variable. Result: a remote client can inject or sometimes override identity/group headers trusted by PHP/FastCGI applications behind Caddy. This vulnerability is fixed in 2.11.4.

cdwdataviz
runtimedataviz

CVE-2026-52846 Caddy is an extensible server platform that uses TLS by default. Prior to 2.11.4, Caddy’s stripHTML template function cannot reliably remove all HTML tags from input strings. Certain malformed HTML, such as <<>img src=x onerror=alert()>, can bypass the tag-stripping logic, potentially leaving dangerous content in the output if it is later rendered as HTML. This may allow client-side XSS in cases where untrusted strings are rendered unsafely. This vulnerability is fixed in 2.11.4.

cdwdataviz
runtimedataviz

CVE-2026-52913 In the Linux kernel, the following vulnerability has been resolved: batman-adv: v: stop OGMv2 on disabled interface When a batadv_hard_iface is disabled, its mesh_iface pointer is set to NULL. However, batadv_v_ogm_send_meshif() may still dispatch OGMs via batadv_v_ogm_queue_on_if() for interfaces that have since lost their mesh_iface association. This results in a NULL pointer dereference when batadv_v_ogm_queue_on_if() unconditionally calls netdev_priv() on the now NULL hard_iface->mesh_iface to retrieve the batadv_priv. It is necessary to ensure that the batadv_v_ogm_queue_on_if() checks that it is using the same mesh_iface for which batadv_v_ogm_send_meshif() was called.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52917 In the Linux kernel, the following vulnerability has been resolved: sctp: diag: reject stale associations in dump_one path The SCTP exact sock_diag lookup can hold a transport reference, block on lock_sock(sk), and then resume after sctp_association_free() has marked the association dead and freed its bind address list. When that happens, inet_assoc_attr_size() and inet_diag_msg_sctpasoc_fill() can still dereference association state that is no longer valid for reporting. In particular, inet_diag_msg_sctpasoc_fill() may read an empty bind-address list as a real sctp_sockaddr_entry and trigger an out-of-bounds read from unrelated association memory. Reject the association after taking the socket lock if it has been reaped or detached from the endpoint, and report the lookup as stale. This keeps the exact dump-one path from formatting torn association state.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52923 In the Linux kernel, the following vulnerability has been resolved: ipc: limit next_id allocation to the valid ID range The checkpoint/restore sysctl path can request the next SysV IPC id through ids->next_id. ipc_idr_alloc() currently forwards that request to idr_alloc() with an open-ended upper bound. If the valid tail of the SysV IPC id space is full, the allocation can spill beyond ipc_mni. The returned SysV IPC id still uses the normal index encoding, so later lookup and removal can target the wrong slot. This leaves the real IDR entry behind and breaks the IDR state for the object. The bug is in ipc_idr_alloc() in the checkpoint/restore path. 1. ids->next_id is passed to: idr_alloc(&ids->ipcs_idr, new, ipcid_to_idx(next_id), 0, ...) 2. The zero upper bound makes the allocation effectively open-ended. Once the valid SysV IPC tail is occupied, idr_alloc() can spill past ipc_mni and allocate an entry beyond the valid IPC id range. 3. The new object id is still encoded with the narrower SysV IPC index width: new->id = (new->seq << ipcmni_seq_shift()) + idx 4. Later removal goes through ipc_rmid(), which uses: ipcid_to_idx(ipcp->id) That truncates the real IDR index. An object actually stored at a high index can then be removed as if it lived at a low in-range index. 5. For shared memory, shm_destroy() frees the current object anyway, but the real high IDR slot is left behind as a dangling pointer. 6. A subsequent walk of /proc/sysvipc/shm reaches the stale IDR entry and dereferences freed memory. Prevent this by bounding the requested allocation to ipc_mni so the checkpoint/restore path fails once the valid range is exhausted.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52928 In the Linux kernel, the following vulnerability has been resolved: af_unix: Reject SIOCATMARK on non-stream sockets SIOCATMARK reports whether the receive queue is at the urgent mark for MSG_OOB. In AF_UNIX, MSG_OOB is supported only for SOCK_STREAM sockets. SOCK_DGRAM and SOCK_SEQPACKET reject MSG_OOB in sendmsg() and recvmsg(), so they should not support SIOCATMARK either. Return -EOPNOTSUPP for non-stream sockets before checking the receive queue.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52934 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tvlv: reject oversized TVLV packets batadv_tvlv_container_ogm_append() builds a TVLV packet section from the tvlv.container_list. The total size of this section is computed by batadv_tvlv_container_list_size(), which sums the sizes of all registered containers. The return type and accumulator in batadv_tvlv_container_list_size() were u16. If the accumulated size exceeds U16_MAX, the value wraps around, causing the subsequent allocation in batadv_tvlv_container_ogm_append() to be undersized. The memcpy-style copy that follows would then write beyond the end of the allocated buffer, corrupting kernel memory. Fix this by widening the return type of batadv_tvlv_container_list_size() to size_t. In batadv_tvlv_container_ogm_append(), check the computed length against U16_MAX before proceeding, and bail out as if the allocation had failed when the limit is exceeded.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52939 In the Linux kernel, the following vulnerability has been resolved: net/rds: fix NULL deref in rds_ib_send_cqe_handler() on masked atomic completion rds_ib_xmit_atomic() always programs a masked atomic opcode (IB_WR_MASKED_ATOMIC_CMP_AND_SWP or IB_WR_MASKED_ATOMIC_FETCH_AND_ADD) for every RDS atomic cmsg. But the completion-side switch in rds_ib_send_unmap_op() only handles the non-masked opcodes, so a masked atomic completion falls through to default and returns rm == NULL while send->s_op is left set. rds_ib_send_cqe_handler() then dereferences the NULL rm via rm->m_final_op, oopsing in softirq context. An unprivileged AF_RDS sendmsg() of an atomic cmsg over an active RDS/IB connection triggers it; on hardware that natively accepts masked atomics (mlx4, mlx5) no extra setup is needed. RDS/IB: rds_ib_send_unmap_op: unexpected opcode 0xd in WR! Oops: general protection fault [#1] SMP KASAN KASAN: null-ptr-deref in range [0x0000000000000190-0x0000000000000197] RIP: rds_ib_send_cqe_handler+0x25c/0xb10 (net/rds/ib_send.c:282) Call Trace: <IRQ> rds_ib_send_cqe_handler (net/rds/ib_send.c:282) poll_scq (net/rds/ib_cm.c:274) rds_ib_tasklet_fn_send (net/rds/ib_cm.c:294) tasklet_action_common (kernel/softirq.c:943) handle_softirqs (kernel/softirq.c:573) run_ksoftirqd (kernel/softirq.c:479) </IRQ> Kernel panic - not syncing: Fatal exception in interrupt Handle the masked atomic opcodes in the same case as the non-masked ones: they map to the same struct rds_message.atomic union member, so the existing container_of()/rds_ib_send_unmap_atomic() body is correct for them.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52942 In the Linux kernel, the following vulnerability has been resolved: netfilter: nf_log: validate MAC header was set before dumping it The fallback path of dump_mac_header() guards the MAC header access only with "skb->mac_header != skb->network_header", without checking skb_mac_header_was_set(). When the MAC header is unset, mac_header is 0xffff, so the test passes and skb_mac_header(skb) returns skb->head + 0xffff, ~64 KiB past the buffer; the loop then reads dev->hard_header_len bytes out of bounds into the kernel log. This is reachable via the netdev logger: nf_log_unknown_packet() calls dump_mac_header() unconditionally, and an skb sent through AF_PACKET with PACKET_QDISC_BYPASS reaches the egress hook with mac_header still unset (__dev_queue_xmit(), which would reset it, is bypassed). Add the skb_mac_header_was_set() check the ARPHRD_ETHER path already uses, and replace the open-coded MAC header length test with skb_mac_header_len(). Only skbs with an unset MAC header are affected; valid ones are dumped as before. BUG: KASAN: slab-out-of-bounds in dump_mac_header (net/netfilter/nf_log_syslog.c:831) Read of size 1 at addr ffff88800ea49d3f by task exploit/148 Call Trace: kasan_report (mm/kasan/report.c:595) dump_mac_header (net/netfilter/nf_log_syslog.c:831) nf_log_netdev_packet (net/netfilter/nf_log_syslog.c:938 net/netfilter/nf_log_syslog.c:963) nf_log_packet (net/netfilter/nf_log.c:260) nft_log_eval (net/netfilter/nft_log.c:60) nft_do_chain (net/netfilter/nf_tables_core.c:285) nft_do_chain_netdev (net/netfilter/nft_chain_filter.c:307) nf_hook_slow (net/netfilter/core.c:619) nf_hook_direct_egress (net/packet/af_packet.c:257) packet_xmit (net/packet/af_packet.c:280) packet_sendmsg (net/packet/af_packet.c:3114) __sys_sendto (net/socket.c:2265)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52943 In the Linux kernel, the following vulnerability has been resolved: net: skbuff: fix missing zerocopy reference in pskb_carve helpers pskb_carve_inside_header() and pskb_carve_inside_nonlinear() both copy the old skb_shared_info header into a new buffer via memcpy(), which includes the destructor_arg pointer (uarg) for MSG_ZEROCOPY skbs. Neither function calls net_zcopy_get() for the new shinfo, creating an unaccounted holder: every skb_shared_info with destructor_arg set will call skb_zcopy_clear() once when freed, but the corresponding net_zcopy_get() was never called for the new copy. Repeated calls drive uarg->refcnt to zero prematurely, freeing ubuf_info_msgzc while TX skbs still hold live destructor_arg pointers. KASAN reports use-after-free on a freed ubuf_info_msgzc: BUG: KASAN: slab-use-after-free in skb_release_data+0x77b/0x810 Read of size 8 at addr ffff88801574d3e8 by task poc/220 Call Trace: skb_release_data+0x77b/0x810 kfree_skb_list_reason+0x13e/0x610 skb_release_data+0x4cd/0x810 sk_skb_reason_drop+0xf3/0x340 skb_queue_purge_reason+0x282/0x440 rds_tcp_inc_free+0x1e/0x30 rds_recvmsg+0x354/0x1780 __sys_recvmsg+0xdf/0x180 Allocated by task 219: msg_zerocopy_realloc+0x157/0x7b0 tcp_sendmsg_locked+0x2892/0x3ba0 Freed by task 219: ip_recv_error+0x74a/0xb10 tcp_recvmsg+0x475/0x530 The skb consuming the late access still referenced the same uarg via shinfo->destructor_arg copied by pskb_carve_inside_nonlinear() without a refcount bump. This has been verified to be reliably exploitable: a working proof-of-concept achieves full root privilege escalation from an unprivileged local user on a default kernel configuration. The fix follows the pattern of pskb_expand_head() which has the same memcpy/cloned structure. For pskb_carve_inside_header(), net_zcopy_get() is placed after skb_orphan_frags() succeeds, so the orphan error path needs no cleanup. For pskb_carve_inside_nonlinear(), net_zcopy_get() is placed after all failure points and just before skb_release_data(), so no error path needs cleanup at all -- matching pskb_expand_head() more closely and avoiding the need for a balancing net_zcopy_put().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-52947 In the Linux kernel, the following vulnerability has been resolved: net: qrtr: fix refcount saturation and potential UAF in qrtr_port_remove In qrtr_port_remove(), the socket reference count is decremented via __sock_put() before the port is removed from the qrtr_ports XArray and before the RCU grace period elapses. This breaks the fundamental RCU update paradigm. It exposes a race window where a concurrent RCU reader (such as qrtr_reset_ports() or qrtr_port_lookup()) can obtain a pointer to the socket from the XArray, and attempt to call sock_hold() on a socket whose reference count has already dropped to zero. This exact race condition was hit during syzkaller fuzzing, leading to the following refcount saturation warning and a potential Use-After-Free: refcount_t: saturated; leaking memory. WARNING: CPU: 3 PID: 1273 at lib/refcount.c:22 refcount_warn_saturate+0xae/0x1d0 Modules linked in: qrtr(+) bochs drm_shmem_helper ... Call Trace: <TASK> qrtr_reset_ports net/qrtr/af_qrtr.c:768 [inline] [qrtr] __qrtr_bind.isra.0+0x48b/0x570 net/qrtr/af_qrtr.c:805 [qrtr] qrtr_bind+0x17d/0x210 net/qrtr/af_qrtr.c:901 [qrtr] kernel_bind+0xe4/0x120 net/socket.c:3592 qrtr_ns_init+0x1a6/0x380 net/qrtr/ns.c:715 [qrtr] qrtr_proto_init+0x3b/0xff0 net/qrtr/af_qrtr.c:169 [qrtr] do_one_initcall+0xf5/0x5e0 init/main.c:1283 ... </TASK> Fix this by deferring the reference count decrement until after the xa_erase() and the synchronize_rcu() complete. (Note: The v1 of this patch incorrectly replaced __sock_put() with sock_put(). As Simon Horman pointed out, the callers of qrtr_port_remove() still hold a reference to the socket, so freeing the socket memory here would lead to a subsequent UAF in the caller. Thus, the __sock_put() is kept, but only repositioned to close the RCU race.)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53027 In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: fix missing run load for vcn0 in attr_data_get_block_locked() When a compressed or sparse attribute has its clusters frame-aligned, vcn is rounded down to the frame start using cmask, which can result in vcn != vcn0. In this case, vcn and vcn0 may reside in different attribute segments. The code already handles the case where vcn is in a different segment by loading its runs before allocation. However, it fails to load runs for vcn0 when vcn0 resides in a different segment than vcn. This causes run_lookup_entry() to return SPARSE_LCN for vcn0 since its segment was never loaded into the in-memory run list, triggering the WARN_ON(1). Fix this by adding a missing check for vcn0 after the existing vcn segment check. If vcn0 falls outside the current segment range [svcn, evcn1), find and load the attribute segment containing vcn0 before performing the run lookup. The following scenario triggers the bug: attr_data_get_block_locked() vcn = vcn0 & cmask <- vcn != vcn0 after frame alignment load runs for vcn segment <- vcn0 segment not loaded! attr_allocate_clusters() <- allocation succeeds run_lookup_entry(vcn0) <- vcn0 not in run -> SPARSE_LCN WARN_ON(1) <- bug fires here!

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53132 In the Linux kernel, the following vulnerability has been resolved: vsock/virtio: fix potential unbounded skb queue virtio_transport_inc_rx_pkt() checks vvs->rx_bytes + len > vvs->buf_alloc. virtio_transport_recv_enqueue() skips coalescing for packets with VIRTIO_VSOCK_SEQ_EOM. If fed with packets with len == 0 and VIRTIO_VSOCK_SEQ_EOM, a very large number of packets can be queued because vvs->rx_bytes stays at 0. Fix this by estimating the skb metadata size: (Number of skbs in the queue) * SKB_TRUESIZE(0)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53133 In the Linux kernel, the following vulnerability has been resolved: RDMA/umem: Fix truncation for block sizes >= 4G When the iommu is used the linearization of the mapping can give a single block that is very large split across multiple SG entries. When __rdma_block_iter_next() reassembles the split SG entries it is overflowing the 32 bit stack values and computed the wrong DMA addresses for blocks after the truncation. Use the right types to hold DMA addresses.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53135 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Fix NULL deref and buffer over-read in SDP debugfs [Why & How] dp_sdp_message_debugfs_write() dereferences connector->base.state->crtc without checking for NULL. A connector can be connected but not bound to any CRTC (e.g. after hot-plug before the next atomic commit), causing a kernel crash when writing to the sdp_message debugfs node. The function also ignores the user-provided size argument and always passes 36 bytes to copy_from_user(), reading past the user buffer when size < 36. Fix both issues by: - Returning -ENODEV when connector->base.state or state->crtc is NULL - Clamping write_size to min(size, sizeof(data)) (cherry picked from commit 6ab4c36a522842ff70474a1c0af2e40e50fc8300)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53136 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Clamp VBIOS HDMI retimer register count to array size [Why & How] The VBIOS integrated info tables (v1_11 and v2_1) contain HdmiRegNum and Hdmi6GRegNum fields that are used as loop bounds when copying retimer I2C register settings into fixed-size arrays (dp*_ext_hdmi_reg_settings[9] and dp*_ext_hdmi_6g_reg_settings[3]). These u8 fields are not validated before use, so a malformed VBIOS can specify values up to 255, causing an out-of-bounds heap write during driver probe. Clamp each register count to the destination array size using min_t() before the copy loops, in both get_integrated_info_v11() and get_integrated_info_v2_1(). (cherry picked from commit 5a7f0ef90195940c54b0f5bb85b87da55f038c69)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53137 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Clamp HDMI HDCP2 rx_id_list read to buffer size [Why & How] During HDCP 2.x repeater authentication over HDMI, the driver reads the sink's RxStatus register and extracts a 10-bit message size field (max value 1023). This value is used as the read length for the ReceiverID list without being clamped to the size of the destination buffer rx_id_list[177]. A malicious HDMI repeater could advertise a message size larger than the buffer, causing an out-of-bounds write during the I2C read. Clamp the read length in mod_hdcp_read_rx_id_list() to the size of the rx_id_list buffer, matching the approach already used in the DP branch. (cherry picked from commit 229212219e4247d9486f8ba41ef087358490be09)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53140 In the Linux kernel, the following vulnerability has been resolved: drm/v3d: Fix vaddr leak when indirect CSD has zeroed workgroups v3d_rewrite_csd_job_wg_counts_from_indirect() maps both the indirect buffer and the workgroup buffer and is expected to release them before returning. When any of the workgroup counts read from the buffer is zero, the function bailed out early and skipped the cleanup, leaking the vaddr mappings of both BOs. Jump to the cleanup path instead of returning directly, so the mappings are always dropped.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53144 In the Linux kernel, the following vulnerability has been resolved: drm/amdkfd: fix NULL dereference in get_queue_ids() When usr_queue_id_array is NULL and num_queues is non-zero, get_queue_ids() returns NULL. The callers check only IS_ERR() on the return value; since IS_ERR(NULL) == false the check passes, and suspend_queues() calls q_array_invalidate() which immediately dereferences NULL while iterating num_queues times. Userspace can trigger this via kfd_ioctl_set_debug_trap() by supplying num_queues > 0 with a zero queue_array_ptr, causing a kernel panic. A NULL usr_queue_id_array with num_queues == 0 is a legitimate no-op (q_array_invalidate never executes, and resume_queues already guards all queue_ids dereferences behind a NULL check). Return ERR_PTR(-EINVAL) only when num_queues is non-zero and the pointer is absent; both callers already propagate IS_ERR() returns correctly to userspace. (cherry picked from commit f165a82cdf503884bb1797771c61b2fcc72113d4)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53146 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: Limit XDomain response copy to actual frame size tb_xdomain_copy() copies req->response_size bytes from the received packet buffer regardless of the actual frame size. When a short response arrives, this reads past the valid frame data in the DMA pool buffer into stale contents from previous transactions. Use the minimum of frame size and expected response size for the copy length.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53148 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: Clamp XDomain response data copy to allocation size tb_xdp_properties_request() derives the per-packet copy length from the response header without checking that it fits in the previously allocated data buffer. A malicious peer can set its length field larger than the declared data_length, causing memcpy to write past the kcalloc allocation. Clamp the per-packet copy length so that the cumulative offset never exceeds data_len.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53149 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: Bound root directory content to block size __tb_property_parse_dir() does not check that content_offset + content_len fits within block_len for the root directory case. When rootdir->length equals or exceeds block_len - 2, the entry loop reads past the allocated property block. Add a bounds check after computing content_offset and content_len to reject directories whose content extends past the block.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53150 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: Reject zero-length property entries in validator tb_property_entry_valid() accepts entries with length == 0 for DIRECTORY, DATA, and TEXT types. A zero-length TEXT entry passes validation but causes an underflow in the null-termination logic: property->value.text[property->length * 4 - 1] = '\0'; When property->length is 0 this writes to offset -1 relative to the allocation. Reject zero-length entries early in the validator since they have no valid representation in the XDomain property protocol.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53154 In the Linux kernel, the following vulnerability has been resolved: mm/hugetlb: restore reservation on error in hugetlb folio copy paths Two sites in mm/hugetlb.c allocate a hugetlb folio via alloc_hugetlb_folio() (consuming a VMA reservation) and then call copy_user_large_folio(), which became int-returning in commit 1cb9dc4b475c ("mm: hwpoison: support recovery from HugePage copy-on-write faults") and can now fail (e.g. -EHWPOISON on a hwpoisoned source page). On the failure path, folio_put() restores the global hugetlb pool count through free_huge_folio(), but the per-VMA reservation map entry is left marked consumed: - hugetlb_mfill_atomic_pte() resubmission path (UFFDIO_COPY) - copy_hugetlb_page_range() fork-time CoW path when hugetlb_try_dup_anon_rmap() fails (rare: pinned hugetlb anon folio under fork) User-visible effect: on UFFDIO_COPY into a private hugetlb VMA where the resubmission copy fails, the reservation for that address is leaked from the VMA's reserve map. A subsequent fault at the same address takes the no-reservation path, and under hugetlb pool pressure the task is SIGBUSed at an address it had previously reserved. The fork-time CoW path leaks the same way in the child VMA's reserve map, though it requires the much rarer combination of pinned hugetlb anon page + hwpoisoned source. Add the missing restore_reserve_on_error() call before folio_put() on both error paths.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53157 In the Linux kernel, the following vulnerability has been resolved: net: phonet: free phonet_device after RCU grace period phonet_device_destroy() removes a phonet_device from the per-net device list with list_del_rcu(), but frees it immediately. RCU readers walking the same list can still hold a pointer to the object after it has been removed, leading to a slab-use-after-free. Use kfree_rcu(), matching the lifetime rule already used by phonet_address_del() for the same object type.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53158 In the Linux kernel, the following vulnerability has been resolved: misc: fastrpc: Fix NULL pointer dereference in rpmsg callback A NULL pointer dereference was observed on Hawi at boot when the DSP sends a glink message before fastrpc_rpmsg_probe() has completed initialization: Unable to handle kernel NULL pointer dereference at virtual address 0000000000000178 pc : _raw_spin_lock_irqsave+0x34/0x8c lr : fastrpc_rpmsg_callback+0x3c/0xcc [fastrpc] ... Call trace: _raw_spin_lock_irqsave+0x34/0x8c (P) fastrpc_rpmsg_callback+0x3c/0xcc [fastrpc] qcom_glink_native_rx+0x538/0x6a4 qcom_glink_smem_intr+0x14/0x24 [qcom_glink_smem] The faulting address 0x178 corresponds to the lock variable inside struct fastrpc_channel_ctx, confirming that cctx is NULL when fastrpc_rpmsg_callback() attempts to take the spinlock. There are two issues here. First, dev_set_drvdata() is called before spin_lock_init() and idr_init(), leaving a window where the callback can retrieve a valid cctx pointer but operate on an uninitialized spinlock. Second, the rpmsg channel becomes live as soon as the driver is bound, so fastrpc_rpmsg_callback() can fire before dev_set_drvdata() is called at all, resulting in dev_get_drvdata() returning NULL. Fix both issues by moving all cctx initialization ahead of dev_set_drvdata() so the structure is fully initialized before it becomes visible to the callback, and add a NULL check in fastrpc_rpmsg_callback() as a guard against any remaining window.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53159 In the Linux kernel, the following vulnerability has been resolved: misc: fastrpc: fix DMA address corruption due to find_vma misuse fastrpc_get_args() uses find_vma() to look up the VMA for a user-provided pointer and compute a DMA address offset. When the address falls in a gap before the returned VMA, (ptr & PAGE_MASK) - vma->vm_start underflows, corrupting the DMA address sent to the DSP. Replace find_vma() with vma_lookup(), which returns NULL when the address is not contained within any VMA.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53160 In the Linux kernel, the following vulnerability has been resolved: misc: fastrpc: fix use-after-free race in fastrpc_map_create fastrpc_map_lookup returns a raw pointer after releasing fl->lock. The caller fastrpc_map_create then calls fastrpc_map_get (kref_get_unless_zero) on this unprotected pointer. A concurrent MEM_UNMAP can free the map between the lock release and the kref operation, resulting in a use-after-free on the freed slab object. Restore the take_ref parameter to fastrpc_map_lookup so the reference is acquired atomically under fl->lock before the pointer is exposed to the caller.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53161 In the Linux kernel, the following vulnerability has been resolved: misc: fastrpc: fix use-after-free of fastrpc_user in workqueue context There is a race between fastrpc_device_release() and the workqueue that processes DSP responses. When the user closes the file descriptor, fastrpc_device_release() frees the fastrpc_user structure. Concurrently, an in-flight DSP invocation can complete and fastrpc_rpmsg_callback() schedules context cleanup via schedule_work(&ctx->put_work). If the workqueue runs fastrpc_context_free() in parallel with or after fastrpc_device_release() has freed the user structure, it dereferences the freed fastrpc_user. Depending on the state of the context at the time of the race, any one of the following accesses can be hit: 1. fastrpc_buf_free() calls fastrpc_ipa_to_dma_addr(buf->fl->cctx, ...) to strip the SID bits from the stored IOVA before passing the physical address to dma_free_coherent(). 2. fastrpc_free_map() reads map->fl->cctx->vmperms[0].vmid to reconstruct the source permission bitmask needed for the qcom_scm_assign_mem() call that returns memory from the DSP VM back to HLOS. 3. fastrpc_free_map() acquires map->fl->lock to safely remove the map node from the fl->maps list. The resulting use-after-free manifests as: pc : fastrpc_buf_free+0x38/0x80 [fastrpc] lr : fastrpc_context_free+0xa8/0x1b0 [fastrpc] fastrpc_context_free+0xa8/0x1b0 [fastrpc] fastrpc_context_put_wq+0x78/0xa0 [fastrpc] process_one_work+0x180/0x450 worker_thread+0x26c/0x388 Add kref-based reference counting to fastrpc_user. Have each invoke context take a reference on the user at allocation time and release it when the context is freed. Release the initial reference in fastrpc_device_release() at file close. Move the teardown of the user structure — freeing pending contexts, maps, mmaps, and the channel context reference — into the kref release callback fastrpc_user_free(), so that it runs only when the last reference is dropped, regardless of whether that happens at device close or after the final in-flight context completes.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53166 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

python-runtime

CVE-2026-53182 In the Linux kernel, the following vulnerability has been resolved: wifi: nl80211: reject oversized EMA RNR lists nl80211_parse_rnr_elems() stores the parsed element count in a u8-backed cfg80211_rnr_elems::cnt field and uses that count to size the flexible array allocation. Reject nested NL80211_ATTR_EMA_RNR_ELEMS input once the count reaches 255, before incrementing it again. This keeps the parser aligned with the data structure it fills and matches the existing bound check used by nl80211_parse_mbssid_elems().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53183 In the Linux kernel, the following vulnerability has been resolved: mptcp: allow subflow rcv wnd to shrink In MPTCP connection, the `window` field in the TCP header refers to the MPTCP-level rcv_nxt and it's right edge should not move backward. Such constraint is enforced at DSS option generation time. At the same time, the TCP stack ensures independently that the TCP-level rcv wnd right's edge does not move backward. That in turn causes artificial inflating of the MPTCP rcv window when the incoming data is acked at the TCP level and is OoO in the MPTCP sequence space (or lands in the backlog). As a consequence, the incoming traffic can exceed the receiver rcvbuf size even when the sender is not misbehaving. Prevent such scenario forcibly allowing the TCP subflow to shrink the TCP-level rcv wnd regardless of the current netns setting.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53184 In the Linux kernel, the following vulnerability has been resolved: udp: clear skb->dev before running a sockmap verdict On the UDP receive path skb->dev is repurposed as dev_scratch (the truesize/state cache set by udp_set_dev_scratch()), through the union { struct net_device *dev; unsigned long dev_scratch; } in sk_buff. When a UDP socket is in a sockmap, sk_data_ready is sk_psock_verdict_data_ready(), which calls udp_read_skb() -> recv_actor() (sk_psock_verdict_recv) to run the attached SK_SKB verdict program in softirq. If that program calls a socket-lookup helper (bpf_sk_lookup_tcp/udp, bpf_skc_lookup_tcp), bpf_skc_lookup() does: if (skb->dev) caller_net = dev_net(skb->dev); skb->dev still holds the dev_scratch value (a non-NULL integer), so dev_net() dereferences it as a struct net_device * and the kernel takes a general protection fault on a non-canonical address in softirq: Oops: general protection fault, probably for non-canonical address 0x1010000800004a0 CPU: 1 UID: 0 PID: 1406 Comm: syz.2.19 Not tainted 7.1.0-rc6 #1 PREEMPT(full) RIP: 0010:bpf_skc_lookup net/core/filter.c:7033 [inline] RIP: 0010:bpf_sk_lookup+0x45/0x160 net/core/filter.c:7047 Call Trace: <IRQ> bpf_prog_4675cb904b7071f8+0x12e/0x14e bpf_prog_run_pin_on_cpu+0xc6/0x1f0 sk_psock_verdict_recv+0x1ba/0x350 udp_read_skb+0x31a/0x370 sk_psock_verdict_data_ready+0x2e3/0x600 __udp_enqueue_schedule_skb+0x4c8/0x650 udpv6_queue_rcv_one_skb+0x3ec/0x740 udp6_unicast_rcv_skb+0x11d/0x140 ip6_protocol_deliver_rcu+0x61e/0x950 ip6_input_finish+0xa9/0x150 NF_HOOK+0x286/0x2f0 ip6_input+0x117/0x220 NF_HOOK+0x286/0x2f0 __netif_receive_skb+0x85/0x200 process_backlog+0x374/0x9a0 __napi_poll+0x4f/0x1c0 net_rx_action+0x3b0/0x770 handle_softirqs+0x15a/0x460 do_softirq+0x57/0x80 </IRQ> The rmem charge that dev_scratch accounted for is released by skb_recv_udp() on dequeue, just above, so the scratch is dead by the time recv_actor() runs. Clear skb->dev so bpf_skc_lookup() falls back to sock_net(skb->sk), which skb_set_owner_sk_safe() set just above.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53186 In the Linux kernel, the following vulnerability has been resolved: RDMA/srp: bound SRP_RSP sense copy by the received length srp_process_rsp() copies sense data from rsp->data + resp_data_len, where resp_data_len is the full 32-bit value supplied by the SRP target and is never checked against the number of bytes actually received (wc->byte_len). The copy length is bounded to SCSI_SENSE_BUFFERSIZE, so at most 96 bytes are copied, but the source offset is not bounded. A malicious or compromised SRP target on the InfiniBand/RoCE fabric that the initiator has logged into can return an SRP_RSP with SRP_RSP_FLAG_SNSVALID set and a large resp_data_len. The receive buffer is allocated at the target-chosen max_ti_iu_len, so the source of the sense copy lands past the bytes actually received; with resp_data_len near 0xFFFFFFFF it is gigabytes past the buffer and the read faults. Copy the sense data only if it has not been truncated, that is, only if the response header, the response data, and the sense region fit within the bytes actually received; otherwise drop the sense and log. The in-tree iSER and NVMe-RDMA receive paths already bound their parse by wc->byte_len; this brings ib_srp into line with them.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53190 In the Linux kernel, the following vulnerability has been resolved: drm/virtio: fix dma_fence refcount leak on error in virtio_gpu_dma_fence_wait() dma_fence_unwrap_for_each() internally calls dma_fence_unwrap_first() which does cursor->chain = dma_fence_get(head), taking an extra reference. On normal loop completion, dma_fence_unwrap_next() releases this via dma_fence_chain_walk() -> dma_fence_put(). When virtio_gpu_do_fence_wait() fails and the function returns early from inside the loop, the cursor->chain reference is never released. This is the only caller in the entire kernel that does an early return inside dma_fence_unwrap_for_each. Add dma_fence_put(itr.chain) before the early return.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53202 In the Linux kernel, the following vulnerability has been resolved: accel/ivpu: Fix signed integer truncation in IPC receive Fix potential buffer overflow where firmware-supplied data_size is cast to signed int before being used in min_t(). Large unsigned values (>= 0x80000000) become negative, causing unsigned wraparound and oversized memcpy operations that can overflow the stack buffer. Change min_t(int, ...) to min() as both values are unsigned and can be handled by min() without explicit cast.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53205 In the Linux kernel, the following vulnerability has been resolved: accel/ivpu: Add bounds checks for firmware log indices Add validation that read and write indices in the firmware log buffer are within valid bounds (< data_size) before using them. If out-of-bounds indices are encountered (from firmware), clamp them to safe values instead of proceeding with invalid offsets. This prevents potential out-of-bounds buffer access when firmware supplies invalid log indices.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53209 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: hci_sync: reject oversized Broadcast Announcement prepend Existing advertising instances can already hold the maximum extended advertising payload. When hci_adv_bcast_annoucement() prepends the Broadcast Announcement service data to that payload, the combined data may no longer fit in the temporary buffer used to rebuild the advertising data. Reject that case before copying the existing payload and report the failure through the device log. This keeps the existing advertising data intact and avoids overrunning the temporary buffer.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53210 In the Linux kernel, the following vulnerability has been resolved: tee: shm: fix shm leak in register_shm_helper() register_shm_helper() allocates shm before calling iov_iter_npages(). If iov_iter_npages() returns 0, the function jumps to err_ctx_put and leaks shm. This can be triggered by TEE_IOC_SHM_REGISTER with struct tee_ioctl_shm_register_data where length is 0. Jump to err_free_shm instead.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53213 In the Linux kernel, the following vulnerability has been resolved: drm/vc4: fix krealloc() memory leak Don't just overwrite the original pointer passed to krealloc() with its return value without checking latter: MEM = krealloc(MEM, SZ, GFP); If krealloc() returns NULL, that erases the pointer to the still allocated memory, hence leaks this memory. Instead, use a temporary variable, check it's not NULL and only then assign it to the original pointer: TMP = krealloc(MEM, SZ, GFP); if (!TMP) return; MEM = TMP; While on it, use krealloc_array().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53214 In the Linux kernel, the following vulnerability has been resolved: ipv6: Fix a potential NPD in cleanup_prefix_route() addrconf_get_prefix_route() can return the fib6_null_entry sentinel entry which has a NULL fib6_table pointer. Therefore, before setting the route's expiration time, check that we are not working with this entry, as otherwise a NPD will be triggered [1]. Note that the other callers of addrconf_get_prefix_route() are not susceptible to this bug: 1. addrconf_prefix_rcv(): Requests a route with the 'RTF_ADDRCONF | RTF_PREFIX_RT' flags which are not set on fib6_null_entry. 2. modify_prefix_route(): Fixed by commit a747e02430df ("ipv6: avoid possible NULL deref in modify_prefix_route()"). 3. __ipv6_ifa_notify(): Calls ip6_del_rt() which specifically checks for fib6_null_entry and returns an error. [1] Oops: general protection fault, probably for non-canonical address 0xdffffc0000000006: 0000 [#1] SMP KASAN KASAN: null-ptr-deref in range [0x0000000000000030-0x0000000000000037] [...] Call Trace: <TASK> __kasan_check_byte (mm/kasan/common.c:573) lock_acquire.part.0 (kernel/locking/lockdep.c:5842 (discriminator 1)) _raw_spin_lock_bh (kernel/locking/spinlock.c:182 (discriminator 1)) cleanup_prefix_route (net/ipv6/addrconf.c:1280) ipv6_del_addr (net/ipv6/addrconf.c:1342) inet6_addr_del.isra.0 (net/ipv6/addrconf.c:3119) inet6_rtm_deladdr (net/ipv6/addrconf.c:4812) rtnetlink_rcv_msg (net/core/rtnetlink.c:6997) netlink_rcv_skb (net/netlink/af_netlink.c:2555) netlink_unicast (net/netlink/af_netlink.c:1344) netlink_sendmsg (net/netlink/af_netlink.c:1899) __sock_sendmsg (net/socket.c:802 (discriminator 4)) ____sys_sendmsg (net/socket.c:2698) ___sys_sendmsg (net/socket.c:2752) __sys_sendmsg (net/socket.c:2784) do_syscall_64 (arch/x86/entry/syscall_64.c:63 arch/x86/entry/syscall_64.c:94) entry_SYSCALL_64_after_hwframe (arch/x86/entry/entry_64.S:121)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53216 In the Linux kernel, the following vulnerability has been resolved: net: mvpp2: limit XDP frame size to the RX buffer mvpp2 has short and long BM pools, and short pool buffers can be smaller than PAGE_SIZE. The XDP path nevertheless initializes every xdp_buff with PAGE_SIZE as frame size. XDP helpers use frame_sz to validate tail growth and to derive the hard end of the data area. Advertising PAGE_SIZE for short buffers can let bpf_xdp_adjust_tail() grow a packet past the real allocation, corrupting memory or later tripping skb tailroom checks. Initialize the XDP buffer with bm_pool->frag_size so XDP tailroom matches the actual buffer backing the packet.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53217 In the Linux kernel, the following vulnerability has been resolved: net: mvpp2: sync RX data at the hardware packet offset mvpp2 programs the RX queue packet offset, so hardware writes received data at dma_addr + MVPP2_SKB_HEADROOM. The current CPU sync starts at dma_addr and only covers rx_bytes + MVPP2_MH_SIZE bytes, which syncs the unused headroom and misses the same number of bytes at the packet tail. On non-coherent DMA systems this can leave the CPU reading stale cache contents for the end of the received frame. Use dma_sync_single_range_for_cpu() with MVPP2_SKB_HEADROOM as the range offset so the sync covers the Marvell header and packet data actually written by hardware.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53223 In the Linux kernel, the following vulnerability has been resolved: net: guard timestamp cmsgs to real error queue skbs skb_is_err_queue() treats PACKET_OUTGOING as the sole marker for an skb from sk_error_queue. That assumption is not true for AF_PACKET sockets: outgoing packet taps are also delivered to packet sockets with skb->pkt_type == PACKET_OUTGOING, but their skb->cb is owned by AF_PACKET instead of struct sock_exterr_skb. If such an skb is received with timestamping enabled, the generic timestamp cmsg path can read AF_PACKET control-buffer state as sock_exterr_skb::opt_stats. With SO_RXQ_OVFL enabled, the packet drop counter overlaps opt_stats. An odd drop count makes the path emit SCM_TIMESTAMPING_OPT_STATS with skb->len and skb->data. For non-linear skbs this copies past the linear head and can trigger hardened usercopy or disclose adjacent heap contents. Keep skb_is_err_queue() local to net/socket.c, but make it verify that the PACKET_OUTGOING marker is paired with the sock_rmem_free destructor installed by sock_queue_err_skb(). AF_PACKET receive skbs use normal receive ownership and no longer pass as error-queue skbs, while legitimate sk_error_queue entries keep the PACKET_OUTGOING marker and sock_rmem_free ownership.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53239 In the Linux kernel, the following vulnerability has been resolved: xfrm: policy: fix use-after-free on inexact bin in xfrm_policy_bysel_ctx() Fix the race by pruning the bin while still holding xfrm_policy_lock, before dropping it. Use __xfrm_policy_inexact_prune_bin() directly since the lock is already held. The wrapper xfrm_policy_inexact_prune_bin() becomes unused and is removed. Race: CPU0 (XFRM_MSG_DELPOLICY) CPU1 (XFRM_MSG_NEWSPDINFO) ========================== ========================== xfrm_policy_bysel_ctx(): spin_lock_bh(xfrm_policy_lock) bin = xfrm_policy_inexact_lookup() __xfrm_policy_unlink(pol) spin_unlock_bh(xfrm_policy_lock) xfrm_policy_kill(ret) // wide window, lock not held xfrm_hash_rebuild(): spin_lock_bh(xfrm_policy_lock) __xfrm_policy_inexact_flush(): kfree_rcu(bin) // bin freed spin_unlock_bh(xfrm_policy_lock) xfrm_policy_inexact_prune_bin(bin) // UAF: bin is freed

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53242 In the Linux kernel, the following vulnerability has been resolved: ALSA: PCM: Fix wait queue list corruption in snd_pcm_drain() on linked streams snd_pcm_drain() uses init_waitqueue_entry which does not clear entry.prev/next, and add_wait_queue with a conditional remove_wait_queue that is skipped when to_check is no longer in the group after concurrent UNLINK. The orphaned wait entry remains on the unlinked substream sleep queue. On the next drain iteration, add_wait_queue adds the entry to a new queue while still linked on the old one, corrupting both lists. A subsequent wake_up dereferences NULL at the func pointer (mapped from the spinlock at offset 0 of the misinterpreted wait_queue_head_t), causing a kernel panic. Replace init_waitqueue_entry/add_wait_queue/conditional remove_wait_queue with init_wait_entry/prepare_to_wait/ finish_wait. init_wait_entry clears prev/next via INIT_LIST_HEAD on each iteration and sets autoremove_wake_function which auto-removes the entry on wake-up. finish_wait safely handles both the already-removed and still-queued cases.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53251 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: ISO: Fix not releasing hdev reference on iso_conn_big_sync hci_get_route() returns a reference-counted hci_dev pointer via hci_dev_hold(). The function exits normally or with an error without ever releasing it.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53252 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: fix memory leak in error path of hci_alloc_dev() Early failures in Bluetooth HCI UART configuration leak SRCU percpu memory. When device initialization fails before hci_register_dev() completes, the HCI_UNREGISTER flag is never set. As a result, when the device reference count reaches zero, bt_host_release() evaluates this flag as false and falls back to a direct kfree(hdev). Because hci_release_dev() is bypassed, the SRCU struct initialized early in hci_alloc_dev() is never cleaned up, resulting in a leak of percpu memory. Fix the leak by explicitly calling cleanup_srcu_struct() in the fallback (unregistered) branch of bt_host_release() before freeing the device.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53261 In the Linux kernel, the following vulnerability has been resolved: devlink: Release nested relation on devlink free devlink relation state is normally released from devl_unregister(), which calls devlink_rel_put(). This misses devlink instances that get a nested relation before registration and then fail probe before devl_register() is reached. That flow can happen for SFs. The child devlink gets linked to its parent before registration, then a later probe error calls devlink_free() directly. Since the instance was never registered, devl_unregister() is not called and devlink->rel is leaked. Release any pending relation from devlink_free() as well. The registered path is unchanged because devl_unregister() already clears devlink->rel before devlink_free() runs.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime

CVE-2026-53264 In the Linux kernel, the following vulnerability has been resolved: net/sched: act_api: use RCU with deferred freeing for action lifecycle When NEWTFILTER and DELFILTER are run concurrently it is possible to create a race with an associated action. Let's illustrate with CPU0 running NEWTFILTER and CPU1 running DELFILTER: 0: mutex_lock() <-- holds the idr lock 0: rcu_read_lock() 0: p = idr_find(idr, index) <-- action p is valid (RCU protects IDR) 0: mutex_unlock() <-- releases the idr lock 1: refcount_dec_and_mutex_lock() <-- refcnt 1->0, mutex held 1: idr_remove(idr, index) <-- Action removed from IDR 1: mutex_unlock() <-- mutex released allowing us to delete the action 1: tcf_action_cleanup(p); kfree(p) <-- Kfrees p immediately, no deferral 0: refcount_inc_not_zero(&p->tcfa_refcnt) <-- ouch, UAF p points to freed memory This patch fixes the race condition between NEWTFILTER and DELFILTER by adding struct rcu_head to tc_action used in the deferral and introducing a call_rcu() in the delete path to defer the final kfree(). Note: this is a revert of commit d7fb60b9cafb ("net_sched: get rid of tcfa_rcu") but also modernization/simplification to directly use kfree_rcu(). Let's illustrate the new restored code path: 0: rcu_read_lock() 1: refcount_dec_and_mutex_lock() <-- refcnt 1->0, mutex held 1: idr_remove(idr, index) 1: mutex_unlock() 1: call_rcu(&p->tcfa_rcu, tcf_action_rcu_free) <-- defer kfree after grace period 0: p = idr_find(idr, index) 0: refcount_inc_not_zero(&p->tcfa_refcnt) <-- fails, refcnt already 0 1: rcu_read_unlock() <-- release so freeing can run after grace period After CPU1 calls idr_remove(), the object is no longer reachable through the IDR. CPU0's subsequent idr_find() will return NULL, and even if it still held a stale pointer, the immediate kfree() is now deferred until after the RCU grace period, so no UAF can occur.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53266 In the Linux kernel, the following vulnerability has been resolved: netfilter: bridge: make ebt_snat ARP rewrite writable The ebtables SNAT target keeps the Ethernet source address rewrite behind skb_ensure_writable(skb, 0). This is intentional: at the bridge ebtables hooks the Ethernet header is addressed through skb_mac_header()/eth_hdr(), while skb->data points at the Ethernet payload. Asking skb_ensure_writable() for ETH_HLEN bytes would check the payload, not the Ethernet header, and would reintroduce the small packet regression fixed by commit 63137bc5882a. However, the optional ARP sender hardware address rewrite is different. It writes through skb_store_bits() at an offset relative to skb->data: skb_store_bits(skb, sizeof(struct arphdr), info->mac, ETH_ALEN) skb_header_pointer() only safely reads the ARP header; it does not make the later sender hardware address range writable. If that range is still held in a nonlinear skb fragment backed by a splice-imported file page, skb_store_bits() maps the frag page and copies the new MAC address directly into it. Ensure the ARP SHA range is writable before reading the ARP header and before calling skb_store_bits().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53268 In the Linux kernel, the following vulnerability has been resolved: netfilter: conntrack_irc: fix possible out-of-bounds read When parsing fails after we've matched the command string we should bail out instead of trying to match a different command. This helper should be deprecated, given prevalence of TLS I doubt it has any relevance in 2026.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53269 In the Linux kernel, the following vulnerability has been resolved: netfilter: synproxy: add mutex to guard hook reference counting As the synproxy infrastructure register netfilter hooks on-demand when a user adds the first iptables target or nftables expression, if done concurrently they can race each other. Introduce a mutex to serialize the refcount control blocks access from both frontends. While a per namespace mutex might be more efficient, it is not needed for target/expression like SYNPROXY.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53273 In the Linux kernel, the following vulnerability has been resolved: tee: optee: prevent use-after-free when the client exits before the supplicant Commit 70b0d6b0a199 ("tee: optee: Fix supplicant wait loop") made the client wait as killable so it can be interrupted during shutdown or after a supplicant crash. This changes the original lifetime expectations: the client task can now terminate while the supplicant is still processing its request. If the client exits first it removes the request from its queue and kfree()s it, while the request ID remains in supp->idr. A subsequent lookup on the supplicant path then dereferences freed memory, leading to a use-after-free. Serialise access to the request with supp->mutex: * Hold supp->mutex in optee_supp_recv() and optee_supp_send() while looking up and touching the request. * Let optee_supp_thrd_req() notice that the client has terminated and signal optee_supp_send() accordingly. With these changes the request cannot be freed while the supplicant still has a reference, eliminating the race.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53275 In the Linux kernel, the following vulnerability has been resolved: ipv6: mcast: Fix use-after-free when processing MLD queries When processing an MLD query, a pointer to the multicast group address is retrieved when initially parsing the packet. This pointer is later dereferenced without being reloaded despite the fact that the skb header might have been reallocated following the pskb_may_pull() calls, leading to a use-after-free [1]. Fix by copying the multicast group address when the packet is initially parsed. [1] BUG: KASAN: slab-use-after-free in __mld_query_work (net/ipv6/mcast.c:1512) Read of size 8 at addr ffff8881154b8e90 by task kworker/4:1/118 Workqueue: mld mld_query_work Call Trace: <TASK> dump_stack_lvl (lib/dump_stack.c:94 lib/dump_stack.c:120) print_address_description.constprop.0 (mm/kasan/report.c:378) print_report (mm/kasan/report.c:482) kasan_report (mm/kasan/report.c:595) __mld_query_work (net/ipv6/mcast.c:1512) mld_query_work (net/ipv6/mcast.c:1563) process_one_work (kernel/workqueue.c:3314) worker_thread (kernel/workqueue.c:3397 kernel/workqueue.c:3478) kthread (kernel/kthread.c:436) ret_from_fork (arch/x86/kernel/process.c:158) ret_from_fork_asm (arch/x86/entry/entry_64.S:245) </TASK> [...] Freed by task 118: kasan_save_stack (mm/kasan/common.c:57) kasan_save_track (mm/kasan/common.c:78) kasan_save_free_info (mm/kasan/generic.c:584) __kasan_slab_free (mm/kasan/common.c:253 mm/kasan/common.c:285) kfree (./include/linux/kasan.h:235 mm/slub.c:2689 mm/slub.c:6251 mm/slub.c:6566) pskb_expand_head (net/core/skbuff.c:2335) __pskb_pull_tail (net/core/skbuff.c:2878 (discriminator 4)) __mld_query_work (net/ipv6/mcast.c:1495 (discriminator 1)) mld_query_work (net/ipv6/mcast.c:1563) process_one_work (kernel/workqueue.c:3314) worker_thread (kernel/workqueue.c:3397 kernel/workqueue.c:3478) kthread (kernel/kthread.c:436) ret_from_fork (arch/x86/kernel/process.c:158) ret_from_fork_asm (arch/x86/entry/entry_64.S:245)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3
python-runtime

CVE-2026-53325 In the Linux kernel, the following vulnerability has been resolved: agp/amd64: Fix broken error propagation in agp_amd64_probe() A NULL pointer dereference was observed in the AMD64 AGP driver when running in a virtualized environment (e.g. qemu/kvm) without a physical AMD northbridge. The crash occurs in amd64_fetch_size() when attempting to dereference the pointer returned by node_to_amd_nb(0). The root cause of this crash is broken error propagation in agp_amd64_probe(): When no AMD northbridges are found, cache_nbs() correctly returns -ENODEV. However, the probe function erroneously checks the return value against exactly -1, rather than < 0. As a result, the hardware absence error is masked, allowing the driver to improperly proceed with initialization. It eventually calls agp_add_bridge(), which invokes amd64_fetch_size(). Since the hardware does not exist, node_to_amd_nb(0) returns NULL, leading to a General Protection Fault (GPF) when accessing its ->misc member. Fix the issue by correcting the error check in agp_amd64_probe() to abort properly when cache_nbs() returns any negative error code. This prevents the driver from erroneously proceeding without hardware, thereby avoiding the subsequent NULL pointer dereference at its source.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53329 In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Use krealloc_array() in dal_vector_reserve() [Why & How] dal_vector_reserve() computes the allocation size as "capacity * vector->struct_size" using uint32_t arithmetic, which can silently wrap to a small value on overflow. This would cause krealloc to return a smaller buffer than expected, leading to heap overflows on subsequent vector appends. Replace krealloc() with krealloc_array() which performs an internal overflow check and returns NULL on wrap, preventing the issue. (cherry picked from commit 37668568641ccc4cc1dbca4923d0a16609dd5707)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53331 In the Linux kernel, the following vulnerability has been resolved: slimbus: qcom-ngd-ctrl: Avoid ABBA on tx_lock/ctrl->lock During the SSR/PDR down notification the tx_lock is taken with the intent to provide synchronization with active DMA transfers. But during this period qcom_slim_ngd_down() is invoked, which ends up in slim_report_absent(), which takes the slim_controller lock. In multiple other codepaths these two locks are taken in the opposite order (i.e. slim_controller then tx_lock). The result is a lockdep splat, and a possible deadlock: rprocctl/449 is trying to acquire lock: ffff00009793e620 (&ctrl->lock){+.+.}-{4:4}, at: slim_report_absent (drivers/slimbus/core.c:322) slimbus but task is already holding lock: ffff00009793fb50 (&ctrl->tx_lock){+.+.}-{4:4}, at: qcom_slim_ngd_ssr_pdr_notify (drivers/slimbus/qcom-ngd-ctrl.c:1475) slim_qcom_ngd_ctrl which lock already depends on the new lock. Possible unsafe locking scenario: CPU0 CPU1 ---- ---- lock(&ctrl->tx_lock); lock(&ctrl->lock); lock(&ctrl->tx_lock); lock(&ctrl->lock); The assumption is that the comment refers to the desire to not call qcom_slim_ngd_exit_dma() while we have an ongoing DMA TX transaction. But any such transaction is initiated and completed within a single qcom_slim_ngd_xfer_msg(). Prior to calling qcom_slim_ngd_exit_dma() the slim_controller is torn down, all child devices are notified that the slimbus is gone and the child devices are removed. Stop taking the tx_lock in qcom_slim_ngd_ssr_pdr_notify() to avoid the deadlock.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53332 In the Linux kernel, the following vulnerability has been resolved: slimbus: qcom-ngd-ctrl: Register callbacks after creating the ngd When the remoteproc starts in parallel with the NGD driver being probed, or the remoteproc is already up when the PDR lookup is being registered, or in the theoretical event that we get an interrupt from the hardware, these callbacks will operate on uninitialized data. This result in issues to boot the affected boards. One such example can be seen in the following fault, where qcom_slim_ngd_ssr_pdr_notify() schedules work on the NULL ngd_up_work. [ 21.858578] ------------[ cut here ]------------ [ 21.858745] WARNING: kernel/workqueue.c:2338 at __queue_work+0x5e0/0x790, CPU#2: kworker/2:2/116 ... [ 21.859251] Call trace: [ 21.859255] __queue_work+0x5e0/0x790 (P) [ 21.859265] queue_work_on+0x6c/0xf0 [ 21.859273] qcom_slim_ngd_ssr_pdr_notify+0x110/0x150 [slim_qcom_ngd_ctrl] [ 21.859304] qcom_slim_ngd_ssr_notify+0x24/0x40 [slim_qcom_ngd_ctrl] [ 21.859318] notifier_call_chain+0xa4/0x230 [ 21.859329] srcu_notifier_call_chain+0x64/0xb8 [ 21.859338] ssr_notify_start+0x40/0x78 [qcom_common] [ 21.859355] rproc_start+0x130/0x230 [ 21.859367] rproc_boot+0x3d4/0x518 ... Move the enablement of interrupts, and the registration of SSR and PDR until after the NGD device has been registered. This could be further refined by moving initialization to the control driver probe and by removing the platform driver model from the picture.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53336 In the Linux kernel, the following vulnerability has been resolved: nvmem: layouts: onie-tlv: fix hang on unknown types The EEPROM on my board has a vendor specific entry of type 0x41. When stumbling upon that, this driver hangs in an endless loop. Fix it by keep incrementing the offset on unknown entries, so the loop will eventually stop.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53339 In the Linux kernel, the following vulnerability has been resolved: i2c: qcom-cci: Fix NULL pointer dereference in cci_remove() On all modern platforms Qualcomm CCI controller provides two I2C masters, and on particular boards only one I2C master may be initialized, and in such cases the device unbinding or driver removal causes a NULL pointer dereference, because cci_halt() is called for all two I2C masters, but a completion is initialized only for the single enabled master: % rmmod i2c-qcom-cci Unable to handle kernel NULL pointer dereference at virtual address 0000000000000000 <snip> Call trace: __wait_for_common+0x194/0x1a8 (P) wait_for_completion_timeout+0x20/0x2c cci_remove+0xc4/0x138 [i2c_qcom_cci] platform_remove+0x20/0x30 device_remove+0x4c/0x80 device_release_driver_internal+0x1c8/0x224 driver_detach+0x50/0x98 bus_remove_driver+0x6c/0xbc driver_unregister+0x30/0x60 platform_driver_unregister+0x14/0x20 qcom_cci_driver_exit+0x18/0x1008 [i2c_qcom_cci] ....

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53343 In the Linux kernel, the following vulnerability has been resolved: ARM: 9475/1: entry: use byte load for KASAN VMAP stack shadow Commit 44e9a3bb76e5 ("ARM: 9430/1: entry: Do a dummy read from VMAP shadow") added a dummy read from the KASAN VMAP stack shadow in __switch_to(). The read uses ldr, but the KASAN shadow address is byte-granular and is not guaranteed to be word aligned. ARMv5 faults unaligned word loads. With CONFIG_KASAN_VMALLOC and CONFIG_VMAP_STACK enabled, ARM926/VersatilePB crashes in __switch_to() with an alignment exception before reaching init. Use ldrb for the dummy shadow access. The code only needs to fault in the shadow mapping if the stack shadow is missing, so a byte load is sufficient and matches the granularity of KASAN shadow memory.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53347 In the Linux kernel, the following vulnerability has been resolved: drm/virtio: Fix driver removal with disabled KMS DRM atomic and modesetting aren't initialized if virtio-gpu driver built with disabled KMS, leading to access of uninitialized data on driver removal/unbinding and crashing kernel. Fix it by skipping shutting down atomic core with unavailable KMS.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53355 In the Linux kernel, the following vulnerability has been resolved: net: rds: clear i_sends on setup unwind The RDS IB connection teardown path is written so it can run during partial startup and on repeated shutdown attempts. It uses NULL pointers to distinguish resources that are still owned from resources that have already been released. When rds_ib_setup_qp() fails after allocating i_sends but before allocating i_recvs, the sends_out path frees i_sends without clearing the pointer. A later shutdown pass can still treat that stale pointer as a live send ring allocation. Clear i_sends after vfree() in the error unwind path so the existing shutdown logic continues to use the correct ownership state.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53356 In the Linux kernel, the following vulnerability has been resolved: drm/i915/gem: Fix phys BO pread/pwrite with offset sg_page() returns struct page pointer not (void *) so the scaling of pread/pwrite is wrong for phys BO and wrong parts of BO would be accessed if non-zero offset is used. Last impacted platform with overlay or cursor planes using phys mapping was Gen3/945G/Lakeport. (cherry picked from commit 3e49a2f85070b2fb672c1e0fdba281a4ea3aebe6)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53361 In the Linux kernel, the following vulnerability has been resolved: af_unix: Set gc_in_progress to true in unix_gc(). Igor Ushakov reported that unix_gc() could run with gc_in_progress being false if the work is scheduled while running: Thread 1 Thread 2 Thread 3 -------- -------- -------- unix_schedule_gc() unix_schedule_gc() `- if (!gc_in_progress) `- if (!gc_in_progress) |- gc_in_progress = true | `- queue_work() | unix_gc() <----------------/ | | |- gc_in_progress = true ... `- queue_work() | | `- gc_in_progress = false | | unix_gc() <---------------------------------------------' | ... /* gc_in_progress == false */ | `- gc_in_progress = false unix_peek_fpl() relies on gc_in_progress not to confuse GC by MSG_PEEK. Let's set gc_in_progress to true in unix_gc().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53362 In the Linux kernel, the following vulnerability has been resolved: ipv6: account for fraggap on the paged allocation path In __ip6_append_data(), when the paged-allocation branch is taken (MSG_MORE / NETIF_F_SG / large fraglen), alloclen and pagedlen are computed as alloclen = fragheaderlen + transhdrlen; pagedlen = datalen - transhdrlen; datalen already includes fraggap (datalen = length + fraggap). When fraggap is non-zero, this is not the first skb and transhdrlen is zero. The fraggap bytes carried over from the previous skb are copied just past the fragment headers in the new skb's linear area. The linear area is therefore undersized by fraggap bytes while pagedlen is overstated by the same amount, and the copy writes past skb->end into the trailing skb_shared_info. An unprivileged user can trigger this via a UDPv6 socket using MSG_MORE together with MSG_SPLICE_PAGES. The bad accounting was introduced by commit 773ba4fe9104 ("ipv6: avoid partial copy for zc"). Before commit ce650a166335 ("udp6: Fix __ip6_append_data()'s handling of MSG_SPLICE_PAGES"), the negative copy value caused -EINVAL to be returned. That later commit allowed MSG_SPLICE_PAGES to proceed in this case, making the corruption triggerable. The non-paged branch sets alloclen to fraglen, which already accounts for fraggap because datalen does. Bring the paged branch in line by adding fraggap to alloclen and subtracting it from pagedlen. After this adjustment, copy no longer collapses to -fraggap on the paged path, so remove the stale comment describing that old arithmetic. Since a negative copy is no longer expected for a valid MSG_SPLICE_PAGES case, remove the MSG_SPLICE_PAGES exception from the negative copy check.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53365 In the Linux kernel, the following vulnerability has been resolved: vsock/virtio: fix zerocopy completion for multi-skb sends When a large message is fragmented into multiple skbs, the zerocopy uarg is only allocated and attached to the last skb in the loop. Non-final skbs carry pinned user pages with no completion tracking, so the kernel has no way to notify userspace when those pages are safe to reuse. If the loop breaks early the uarg is never allocated at all, leaking pinned pages with no completion notification. Fix this by following the approach used by TCP: allocate the zerocopy uarg (if not provided by the caller) before the send loop and attach it to every skb via skb_zcopy_set(), which takes a reference per skb. Each skb's completion properly decrements the refcount, and the notification only fires after the last skb is freed. On failure, if no data was sent, the uarg is cleanly aborted via net_zcopy_put_abort(). This issue was initially discovered by sashiko while reviewing commit 1cb36e252211 ("vsock/virtio: fix MSG_ZEROCOPY pinned-pages accounting") but was pre-existing.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53366 In the Linux kernel, the following vulnerability has been resolved: ipv4: account for fraggap on the paged allocation path In __ip_append_data(), when the paged-allocation branch is taken, alloclen and pagedlen are computed as alloclen = fragheaderlen + transhdrlen; pagedlen = datalen - transhdrlen; datalen already includes fraggap, but the fraggap bytes carried over from the previous skb are copied into the new skb's linear area at offset transhdrlen by the subsequent skb_copy_and_csum_bits(). The linear area is therefore undersized by fraggap bytes while pagedlen is overstated by the same amount. The non-paged branch sets alloclen to fraglen, which already accounts for fraggap because datalen does. Bring the paged branch in line by adding fraggap to alloclen and subtracting it from pagedlen. After this adjustment, copy no longer collapses to -fraggap on the paged path, so remove the stale comment describing that old arithmetic.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53381 In the Linux kernel, the following vulnerability has been resolved: virtiofs: fix UAF on submount umount iput() called from fuse_release_end() can Oops if the super block has already been destroyed. Normally this is prevented by waiting for num_waiting to go down to zero before commencing with super block shutdown. This only works, however, for the last submount instance, as the wait counter is per connection, not per superblock. Revert to using synchronous release requests for the auto_submounts case, which is virtiofs only at this time.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53382 In the Linux kernel, the following vulnerability has been resolved: media: vidtv: fix NULL pointer dereference in vidtv_mux_push_si syzbot reported a general protection fault in vidtv_psi_ts_psi_write_into [1]. vidtv_mux_get_pid_ctx() can return NULL, but vidtv_mux_push_si() does not check for this before dereferencing the returned pointer to access the continuity counter. This leads to a general protection fault when accessing a near-NULL address. The root cause is that vidtv_mux_pid_ctx_init() does not check the return value of vidtv_mux_create_pid_ctx_once() for PMT section PIDs. If the allocation fails, the PID context is never created, but init returns success. The subsequent vidtv_mux_push_si() call then gets NULL from vidtv_mux_get_pid_ctx() and crashes. Fix both the root cause (add error check in vidtv_mux_pid_ctx_init for PMT PIDs) and add defensive NULL checks in vidtv_mux_push_si for all vidtv_mux_get_pid_ctx() calls. [1] Oops: general protection fault, probably for non-canonical address 0xdffffc0000000000: 0000 [#1] SMP KASAN PTI KASAN: null-ptr-deref in range [0x0000000000000000-0x0000000000000007] Workqueue: events vidtv_mux_tick RIP: 0010:vidtv_psi_ts_psi_write_into+0x54a/0xbc0 drivers/media/test-drivers/vidtv/vidtv_psi.c:197 Call Trace: <TASK> vidtv_psi_table_header_write_into drivers/media/test-drivers/vidtv/vidtv_psi.c:799 [inline] vidtv_psi_pmt_write_into+0x3b2/0xa70 drivers/media/test-drivers/vidtv/vidtv_psi.c:1231 vidtv_mux_push_si+0x932/0xe80 drivers/media/test-drivers/vidtv/vidtv_mux.c:196 vidtv_mux_tick+0xe9b/0x1480 drivers/media/test-drivers/vidtv/vidtv_mux.c:408

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53383 In the Linux kernel, the following vulnerability has been resolved: ksmbd: reject non-VALID session in compound request branch smb2_check_user_session() takes a shortcut for any operation that is not the first in a COMPOUND request: it reuses work->sess (the session bound by the first operation) and validates only the SessionId, then returns "valid". It never re-checks work->sess->state == SMB2_SESSION_VALID, and a SessionId of 0xFFFFFFFFFFFFFFFF (ULLONG_MAX, the MS-SMB2 related-operation value) skips even the id comparison. The standalone path (ksmbd_session_lookup_all() plus the SESSION_SETUP state machine) does enforce the VALID state; the compound branch bypasses all of it. A SESSION_SETUP carrying only an NTLM Type-1 (NtLmNegotiate) blob publishes a fresh SMB2_SESSION_IN_PROGRESS session whose sess->user is still NULL (->user is assigned later, by ntlm_authenticate()). Used as operation 1 of a COMPOUND with operation 2 = TREE_CONNECT (related, SessionId=ULLONG_MAX, \\host\IPC$), the tree-connect then runs on that IN_PROGRESS session and reaches ksmbd_ipc_tree_connect_request(), which dereferences user_name(sess->user) with sess->user == NULL (transport_ipc.c:687/701/704) -> remote NULL-pointer dereference and a kernel Oops that wedges the ksmbd worker for all clients. Reject any non-first compound operation that lands on a session which is not SMB2_SESSION_VALID, mirroring the validity the standalone lookup path enforces. SESSION_SETUP itself legitimately runs on an IN_PROGRESS session, but it is never carried as a non-first compound operation, so multi-leg authentication is unaffected by this check.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53385 In the Linux kernel, the following vulnerability has been resolved: vc_screen: fix null-ptr-deref in vcs_notifier() during concurrent vcs_write A KASAN null-ptr-deref was observed in vcs_notifier(): BUG: KASAN: null-ptr-deref in vcs_notifier+0x98/0x130 Read of size 2 at addr qmp_cmd_name: qmp_capabilities, arguments: {} The issue is a race condition in vcs_write(). When the console_lock is temporarily dropped (to copy data from userspace), the vc_data pointer obtained from vcs_vc() may become stale. After re-acquiring the lock, vcs_vc() is called again to re-validate the pointer. If the vc has been deallocated in the meantime, vcs_vc() returns NULL, and the while loop breaks (with written > 0). However, after the loop, vcs_scr_updated(vc) is still called with the now-NULL vc pointer, leading to a null pointer dereference in the notifier chain (vcs_notifier dereferences param->vc). Fix this by adding a NULL check for vc before calling vcs_scr_updated().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53386 In the Linux kernel, the following vulnerability has been resolved: iio: adc: ti-ads1298: add bounds check to pga_settings index ads1298_pga_settings has 7 elements but ADS1298_MASK_CH_PGA can yield values 0-7. If it yields a value >= 7, this causes an out-of-bounds array access. Add a bounds check and return -EINVAL if the index is out of range. Note that the remaining value b111 is reserved so should not be seen in a correctly functioning system.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53389 In the Linux kernel, the following vulnerability has been resolved: net/tcp-ao: fix use-after-free of key in del_async path In tcp_ao_delete_key(), the del_async path skips the current_key and rnext_key validity checks present in the synchronous path, assuming these pointers are always NULL on LISTEN sockets. However, if a key was added with set_current=1/set_rnext=1 while the socket was in CLOSE state, current_key and rnext_key will be non-NULL after listen() transitions the socket to LISTEN. When such a key is deleted with del_async=1, hlist_del_rcu() and call_rcu() free the key without clearing the dangling pointers. After the RCU grace period, getsockopt(TCP_AO_INFO) dereferences current_key->sndid and rnext_key->rcvid from freed slab memory. Clear current_key and rnext_key in the del_async path when they reference the key being deleted.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53390 In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix out-of-bounds read in smb_check_perm_dacl() The permission-check ACE walk in smb_check_perm_dacl() validates the ACE header size and caps sid.num_subauth at SID_MAX_SUB_AUTHORITIES, but it never checks that ace->size is actually large enough to contain num_subauth sub-authorities before compare_sids() dereferences them. CIFS_SID_BASE_SIZE covers the SID header up to but excluding the sub_auth[] array, and offsetof(struct smb_ace, sid) is the ACE header, so the existing guards only guarantee the 8-byte SID base, i.e. zero sub-authorities. compare_sids() then reads ace->sid.sub_auth[i] for i < min(local_sid->num_subauth, ace->sid.num_subauth). The local comparison SIDs (sid_everyone, sid_unix_NFS_mode, and the id_to_sid() result) always have at least one sub-authority, and an attacker controls the ACE revision and authority bytes (which lie within the in-bounds SID base), so they can match one of those SIDs and force the sub_auth read. A crafted ACE with size == 16 and num_subauth >= 1 placed at the tail of the security descriptor therefore causes a heap out-of-bounds read of up to SID_MAX_SUB_AUTHORITIES * sizeof(__le32) bytes past the pntsd allocation. The security descriptor is loaded by ksmbd_vfs_get_sd_xattr() into a buffer sized exactly to the on-disk data (kzalloc(sd_size) in ndr_decode_v4_ntacl()), so the read lands past the allocation. The malformed descriptor can be stored verbatim via SMB2_SET_INFO (the DACL is not normalised before being written to the security.NTACL xattr) and the read fires on a subsequent SMB2_CREATE access check, making this reachable by an authenticated client on a share that uses ACL xattrs. Add the missing num_subauth-versus-ace_size check, mirroring the identical guards already present in the sibling parsers parse_dacl() and smb_inherit_dacl().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-53391 In the Linux kernel, the following vulnerability has been resolved: NFSv4/pNFS: reject zero-length r_addr in nfs4_decode_mp_ds_addr nfs4_decode_mp_ds_addr() decodes the r_netid and r_addr opaques of a netaddr4 from a GETDEVICEINFO multipath-DS body, then immediately calls strrchr(buf, '.') to locate the port separator. Both decodes use xdr_stream_decode_string_dup(), and the current code checks only "nlen < 0" / "rlen < 0" before dereferencing the returned string. When the on-wire opaque has length zero, xdr_stream_decode_opaque_inline() returns 0 and xdr_stream_decode_string_dup() falls through to its "*str = NULL; return ret" tail, leaving buf NULL with a return value of 0. The "< 0" check does not catch this, and the next line is strrchr(NULL, '.'), a kernel NULL pointer dereference reachable from any pNFS-flexfile client mounted against a malicious or compromised metadata server. Reject the zero-length cases explicitly so the decoder fails with -EBADMSG (treated as a malformed GETDEVICEINFO body) instead of panicking the client.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53392 In the Linux kernel, the following vulnerability has been resolved: NFSv4/flexfiles: reject zero filehandle version count ff_layout_alloc_lseg() decodes the filehandle-version array count from the flexfiles layout body. The value is used as the count for kzalloc_objs(), and the current code only rejects NULL. A zero count yields ZERO_SIZE_PTR, which can be stored in dss_info->fh_versions even though later flexfiles paths assume that at least one filehandle version exists. Reject fh_count == 0 before the allocation, matching the existing zero version_count validation in the flexfiles GETDEVICEINFO parser. A QEMU/KASAN run with a malformed flexfiles layout hit: KASAN: null-ptr-deref in range [0x0000000000000010-0x0000000000000017] RIP: 0010:ff_layout_encode_ff_layoutupdate.isra.0+0x15f/0x750 ff_layout_encode_layoutreturn+0x683/0x970 nfs4_xdr_enc_layoutreturn+0x278/0x3a0 Kernel panic - not syncing: Fatal exception The patched kernel rejects the malformed layout without KASAN/oops/panic, and a valid fh_count=1 regression still opens, reads, and unmounts cleanly.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53393 In the Linux kernel, the following vulnerability has been resolved: nfsd: reset write verifier on deferred writeback errors nfsd_vfs_write() and nfsd_commit() both call filemap_check_wb_err() to detect deferred writeback errors, but neither rotates the server's write verifier (nn->writeverf) when this check fails. Every other durable-storage-failure path in these functions calls commit_reset_write_verifier() before returning an error. The missing rotation means clients holding UNSTABLE write data under the current verifier will COMMIT, receive the unchanged verifier back, and conclude their data is durable — silently dropping data that failed writeback. This violates the UNSTABLE+COMMIT durability contract (RFC 1813 §3.3.7, RFC 8881 §18.32). Add commit_reset_write_verifier() calls at both filemap_check_wb_err() error sites, matching the pattern used by adjacent error paths in the same functions. The helper already filters -EAGAIN and -ESTALE internally, so the calls are unconditionally safe.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53397 In the Linux kernel, the following vulnerability has been resolved: nfsd: fix posix_acl leak on SETACL decode failure nfsaclsvc_decode_setaclargs() and nfs3svc_decode_setaclargs() each call nfs_stream_decode_acl() twice, first for NFS_ACL and then for NFS_DFACL. Each successful call transfers ownership of a freshly allocated posix_acl into argp->acl_access or argp->acl_default. If the first call succeeds but the second fails, the decoder returns false and argp->acl_access is left dangling. ACLPROC2_SETACL.pc_release was wired to nfssvc_release_attrstat and ACLPROC3_SETACL.pc_release was wired to nfs3svc_release_fhandle. Both only call fh_put() and have no knowledge of the ACL fields on argp. The posix_acl_release() pairs sat at the out: labels inside nfsacld_proc_setacl() and nfsd3_proc_setacl(), but svc_process() skips pc_func when pc_decode returns false, so that cleanup is unreachable on decode failure: svc_process_common() pc_decode() /* decode_setaclargs: false */ /* pc_func skipped */ pc_release() /* fh_put only -- ACLs leaked */ The orphaned posix_acl is leaked for the lifetime of the server. Fix by adding nfsaclsvc_release_setacl() and nfs3svc_release_setacl(), which release both argp->acl_access and argp->acl_default in addition to fh_put(), and wiring them as pc_release for their respective SETACL procedures. pc_release runs on every path svc_process() takes after decode, including decode failure, so the posix_acl_release() pairs are removed from the proc functions' out: labels to keep ownership in one place. This matches the existing release_getacl() pattern used by the sibling GETACL procedures.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53398 In the Linux kernel, the following vulnerability has been resolved: NFSD: Fix SECINFO_NO_NAME decode error cleanup nfsd4_decode_secinfo_no_name() currently initializes sin_exp after decoding sin_style. If the XDR stream is truncated, the decoder returns nfserr_bad_xdr before sin_exp is initialized. Since commit 3fdc54646234 ("NFSD: Reduce amount of struct nfsd4_compoundargs that needs clearing"), the inline iops array is not cleared between RPC calls. A failed SECINFO_NO_NAME decode can therefore leave sin_exp holding stale union contents from a previous operation. The error response path still invokes nfsd4_secinfo_no_name_release(), which calls exp_put() on a non-NULL sin_exp. Initialize sin_exp before the first failable decode step, matching nfsd4_decode_secinfo().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53399 In the Linux kernel, the following vulnerability has been resolved: nfsd: release layout stid on setlease failure nfs4_alloc_stid() publishes the new stid into cl->cl_stateids via idr_alloc_cyclic() under cl_lock before returning to nfsd4_alloc_layout_stateid(). When nfsd4_layout_setlease() then fails, the error path frees the layout stateid directly with kmem_cache_free() without ever calling idr_remove(), leaving the IDR slot pointing at freed slab memory. Any subsequent IDR walker (states_show, client teardown) dereferences the dangling pointer. The correct teardown for an IDR-published stid is nfs4_put_stid(), which removes the IDR slot under cl_lock, dispatches sc_free (nfsd4_free_layout_stateid) to release ls->ls_file via nfsd4_close_layout(), and drops the nfs4_file reference in its tail. A second issue blocks that switch: nfsd4_free_layout_stateid() unconditionally inspects ls->ls_fence_work via delayed_work_pending() under ls_lock, but INIT_DELAYED_WORK(&ls->ls_fence_work, ...) currently runs only after the setlease call. On the setlease-failure path the destructor would touch an uninitialized delayed_work. nfsd4_alloc_layout_stateid() nfs4_alloc_stid() /* idr_alloc_cyclic under cl_lock */ nfsd4_layout_setlease() /* fails */ nfs4_put_stid() nfsd4_free_layout_stateid() delayed_work_pending(&ls->ls_fence_work) /* needs INIT */ nfsd4_close_layout() /* nfsd_file_put(ls->ls_file) */ put_nfs4_file() Fix by hoisting the ls_fenced / ls_fence_delay / INIT_DELAYED_WORK initialization above the nfsd4_layout_setlease() call, and replace the manual nfsd_file_put + put_nfs4_file + kmem_cache_free cleanup with a single nfs4_put_stid(stp).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53402 In the Linux kernel, the following vulnerability has been resolved: fbdev: fbcon: fix out-of-bounds read in err_out of fbcon_do_set_font() When fbcon_do_set_font() fails (e.g., due to a memory allocation failure inside vc_resize() under heavy memory pressure), it jumps to the `err_out` label to roll back the console state. However, the current rollback logic forgets to restore the `hi_font` state, leading to a severe state machine corruption. Earlier in the function, `set_vc_hi_font()` might be called to change `vc->vc_hi_font_mask` and mutate the screen buffer. If `vc_resize()` subsequently fails, the `err_out` path restores `vc_font.charcount` but entirely skips rolling back the `vc_hi_font_mask` and the screen buffer. This mismatch leaves the terminal in a desynchronized state. Because `vc_hi_font_mask` remains set, the VT subsystem will still accept character indices greater than 255 from userspace and write them to the screen buffer. Subsequent rendering calls (e.g., `fbcon_putcs()`) will then use these inflated indices to access the reverted, 256-character font array, leading to a deterministic out-of-bounds read and potential kernel memory disclosure. Fix this by adding the missing rollback logic for the `hi_font` mask and screen buffer in the error path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-53622 No description available.

traefik

CVE-2026-54234 vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, a frontend-legal multi-request speculative decoding workload can cause the rejection sampler to produce a recovered token equal to the model vocabulary size boundary value, which is then converted to negative one when the engine selects the next live token for a request and is written back into the drafter's input ids; that out-of-vocabulary value is later consumed by the model's embedding and attention path and crashes the engine worker with a GPU device-side assertion. The same triggering request sequence is reachable through the public gRPC Generate and Abort endpoints, so a remote client that can send generation requests can crash the shared engine worker, aborting concurrent requests and causing a service-wide denial of service for other clients of the deployment until the worker is restarted. This issue is fixed in version 0.24.0.

kserve_huggingfaceserver
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-54240 libde265 is an open source implementation of the h.265 video codec. Versions prior to 1.1.1 use signed 32-bit arithmetic to calculate pixel offsets, allowing a crafted HEVC stream with large image dimensions to trigger an integer overflow and cause out-of-bounds heap reads or writes, potentially disclosing data, corrupting memory, or crashing the decoder. Version 1.1.1 contains a patch.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-54241 libde265 is an open source implementation of the h.265 video codec. Versions prior to 1.1.1 use signed 32-bit arithmetic to calculate the sample adaptive offset input-buffer size, allowing a crafted HEVC stream with large dimensions and 16-bit luma samples to cause an integer overflow, an undersized allocation, and an out-of-bounds heap read that may expose heap data in decoded output or crash the decoder. Version 1.1.1 contains a patch.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-54285 opentelemetry-js is the OpenTelemetry JavaScript Client. Prior to 2.8.0, W3CBaggagePropagator.extract() in @opentelemetry/core does not enforce size limits when parsing inbound baggage HTTP headers. The W3C Baggage specification recommends a maximum of 8,192 bytes and 180 entries; these limits were only enforced on the outbound (inject()) path, not on the inbound (extract()) path. Parsing oversized baggage causes memory allocation proportional to the header size without any cap. This vulnerability is fixed in 2.8.0.

cdsw-web

CVE-2026-54500 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.3, Oj.load in :object mode reads uninitialized stack memory (and, for long keys, reads out of bounds) when parsing a JSON object whose key is 254 bytes or longer. The interned bytes can surface to the caller, disclosing process stack memory. In ext/oj/intern.c, form_attr() handles the long-key path by allocating a heap buffer, `b`, populating it with the attribute name, and then freeing it — but it passed the uninitialized stack buffer buf (not b) to rb_intern3(). rb_intern3 therefore reads len + 1 bytes of uninitialized stack memory. When the key length is >= 256, it also reads out of bounds past the 256-byte buf. The resulting bytes are interned and can reach the caller via the produced Symbol or via the EncodingError message raised on invalid UTF-8, leaking process stack contents. This issue has been fixed in version 3.17.3.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54502 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2, Oj.dump is vulnerable to a stack-based buffer overflow when a large :indent value is provided by the developer. fill_indent in dump.h calls memset(indent_str, ' ', (size_t)opts->indent) without validating the size. When opts->indent is set to INT_MAX (2,147,483,647), the (size_t) cast preserves the large value and memset writes 2 GB into the stack-allocated out buffer (4,184 bytes), corrupting the stack and crashing the process. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54522 MessagePack for Ruby is an implementation of the MessagePack binary serialization format. Prior to 1.8.2, MessagePack::Buffer#clear in ext/msgpack/buffer.c leaves rmem_last, rmem_end, and rmem_owner stale after _msgpack_buffer_shift_chunk returns an rmem page to the shared pool, allowing a subsequent Buffer#write and a second MessagePack::Buffer to alias the page and disclose or corrupt cross-buffer data. This issue is fixed in version 1.8.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54527 JupyterLab Git is a Git extension for JupyterLab. From 0.30.0b3 before 0.54.0, the PlainTextDiff.ts createHeader() method passes Git filenames directly to innerHTML when rendering renamed files in commit history, allowing a crafted filename to execute JavaScript when a victim views the rename diff in the Git History tab. This issue is fixed in version 0.54.0.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-54528 JupyterLab Git is a Git extension for JupyterLab. Prior to 0.54.0, jupyterlab-git uses fnmatch.fnmatchcase() in GitHandler.prepare() in jupyterlab_git/handlers.py to enforce excluded_paths, allowing an authenticated user on a case-insensitive filesystem to vary URL path casing and read excluded directories. This issue is fixed in version 0.54.0.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-54592 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.3, Oj::Doc#each_child, when invoked recursively over a deeply nested JSON document, overflows a fixed-size stack buffer and aborts the process, leading to DoS. In a two-step chain in ext/oj/fast.c, doc_each_child increments doc->where past the where_path[MAX_STACK = 100] array with no bounds check and never restores it (the doc->where-- is missing), so calling each_child recursively from inside the yield block drives doc->where beyond the array. On the next entry the function copies the path into the 800-byte stack-local buffer save_path[MAX_STACK] using wlen = doc->where - doc->where_path, so when the previous recursive call left doc->where past where_path[100] the wlen exceeds MAX_STACK and the memcpy overflows save_path on the C stack; because the Oj::Doc parser imposes no JSON nesting-depth limit (relying on a C-stack pressure check), deeply nested attacker input reaches this path. This issue has been fixed in version 3.17.3.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54621 datamodel-code-generator generates Python data models from schema definitions. Prior to 0.60.1, GraphQL Union description values in src/datamodel_code_generator/model/template/UnionTypeStatement.jinja2 and src/datamodel_code_generator/model/template/UnionTypeStatement.py312.jinja2 are rendered into Python comments without neutralizing carriage returns in Python # comments, allowing attacker-controlled GraphQL schema content to inject Python code into generated models that runs when imported. This issue is fixed in version 0.60.1.

model-registry

CVE-2026-54653 datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.17.0 until 0.60.2, datamodel-code-generator preserves attacker-controlled default_factory values in src/datamodel_code_generator/parser/jsonschema.py through JsonSchemaObject.init and get_field_extras and emits them into Field(default_factory=...) or field(default_factory=...), allowing Python expression execution when the generated model is imported. This issue is fixed in version 0.60.2.

model-registry

CVE-2026-54654 datamodel-code-generator generates Python data models from schema definitions. From 0.14.1 until 0.60.2, the --extra-template-data comment field is rendered into Python comments in src/datamodel_code_generator/model/template/TypeAliasAnnotation.jinja2, src/datamodel_code_generator/model/template/TypedDict.jinja2, src/datamodel_code_generator/model/template/dataclass.jinja2, src/datamodel_code_generator/model/template/msgspec.Struct.jinja2, src/datamodel_code_generator/model/template/pydantic/BaseModel.jinja2, and src/datamodel_code_generator/model/template/pydantic_v2/BaseModel.jinja2 without neutralizing carriage returns in Python # comments, allowing an attacker-controlled comment value to inject Python code into generated models that runs when imported. This issue is fixed in version 0.60.2.

model-registry

CVE-2026-54690 datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.9.1 until 0.61.0, datamodel-code-generator silently dereferences attacker-controlled JSON Schema $ref HTTP or HTTPS URLs in src/datamodel_code_generator/parser/jsonschema.py through _get_ref_body, and the --allow-remote-refs gate can warn instead of blocking, allowing server-side request forgery through src/datamodel_code_generator/http.py. This issue is fixed in version 0.61.0.

model-registry

CVE-2026-54691 datamodel-code-generator generates Python data models from schema definitions. From 0.9.1 until 0.61.0, src/datamodel_code_generator/http.py http.get_body accepts --url targets and redirect chain targets without host/IP validation, allowing server-side request forgery against loopback, private, link-local, metadata, and other network-accessible resources. This issue is fixed in version 0.61.0.

model-registry

CVE-2026-54696 Ruby JSON is a JSON implementation for Ruby. Versions 2.9.0 through 2.19.8 are vulnerable to heap buffer overflow when the JSON generator is provided with an oversized streamed object. When streaming to an IO JSON.dump(obj, io) and JSON::State#generate(obj, io) can write past the internal JSON generator buffer when a streamed object contains an attacker-controlled string near 16 KB. Exploitation would result in a reliable process crash/denial of service. This issue has been fixed in version 2.19.9.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54712 OpenTelemetry Java Instrumentation provides OpenTelemetry auto-instrumentation and instrumentation libraries for Java. In versions prior to 2.27.0, the RMI context propagation payload reader limits the number of context entries but does not limit the aggregate size of the strings read from the stream. An attacker who can reach an RMI endpoint on an instrumented JVM can send an oversized context propagation payload. This can cause excessive memory allocation while the JVM reads the payload, potentially leading to denial of service. The issue affects only deployments where RMI instrumentation is enabled and an RMI endpoint is network-reachable. This issue has been fixed in version 2.27.0.

databus-producer
dex_thunderhead-dbuswxmclient
obs_agent

CVE-2026-54761 No description available.

traefik

CVE-2026-54762 No description available.

traefik

CVE-2026-54896 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2, when in object mode, Oj.dump is vulnerable to a heap buffer overflow when serializing Exception objects with a large :indent value. The serializer allocates a buffer sized for the object's attributes but does not account for the indent bytes added on each write. With indent: 5000, the accumulation of 5,000-byte indent strings overflows the 13,150-byte heap allocation, corrupting adjacent heap memory. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54897 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. Prior to 3.17.2, Oj::Doc iterators (each_value, each_child, each_leaf) were vulnerable to a heap use-after-free. When a Ruby block yielded during iteration calls doc.close or d.close, the document's heap memory is freed while the C iterator is still running. When control returns from the block, the iterator reads from the freed region, producing a use-after-free accessible from pure Ruby. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54898 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2,Oj::Parser#parse is vulnerable to a heap use-after-free when a SAJ/SAJ2 callback mutates the input JSON string during parsing. The C engine holds a raw const byte * pointer into the Ruby string's internal buffer. If a callback (e.g. hash_start) resizes the string — for example by calling String#replace with a longer value — Ruby reallocates the string buffer and frees the old one. The C parser's pointer is left dangling; the next character read at parser.c:607 is a use-after-free. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54899 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. Prior to version 3.17.2, disabling symbol_keys on a reused Oj::Parser instance triggers a heap use-after-free. When symbol_keys is toggled from true to false, opt_symbol_keys_set frees the internal key cache (cache_free) but does not clear the pointer. The next parse call reads from the freed cache via cache_intern, producing a use-after-free. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54900 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2, when in usual mode with create_id enabled, Oj::Parser#parse is vulnerable to heap corruption via a negative-size memcpy. When a JSON object key is exactly 65,535 bytes long, an integer truncation in form_attr (usual.c:63) converts the length to -1 before passing it to memcpy. This causes memcpy to copy SIZE_MAX bytes (interpreted as a huge size_t), corrupting heap memory and crashing the process. The issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54901 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2, Oj::Parser in usual mode does not mark array_class and hash_class references during garbage collection, leading to Use-After-Free. If GC runs after the class is assigned but before a parse, the class object is reclaimed, leaving the parser holding a dangling VALUE. The subsequent parse call dereferences the freed object, producing a segfault. This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54902 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. Prior to version 3.17.2, is vulnerable to Use-After-Free when in SAJ mode. The Oj::Parser does not protect cached object keys (≥ 35 bytes) from garbage collection, and a Ruby callback that triggers GC inside hash_end can cause the key string to be reclaimed while the C parser still holds a pointer to it. The subsequent access to the freed string VALUE results in a segfault, confirmed by an RIP pointing to address 0x4242 (a canary-style pattern suggesting control over the freed memory's content). This issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54903 Oj (Optimized JSON) is a JSON parser and Object marshaller packaged as a Ruby gem. In versions prior to 3.17.2, Oj.load is vulnerable to heap corruption when parsing a JSON string longer than 2 GB. An integer overflow in buf_append_string (buf.h:61) converts the string length to a large negative size_t, causing memcpy to copy an astronomically large amount of data out of bounds. This crashes the process and can corrupt adjacent heap memory. The issue has been fixed in version 3.17.2.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54904 concurrent-ruby is a modern concurrency tools for Ruby. Prior to 1.3.7, Concurrent::AtomicReference#update can enter a permanent busy retry loop when the current value is Float::NAN. The issue is caused by the interaction between AtomicReference#update, which retries until compare_and_set(old_value, new_value) succeeds; Numeric compare_and_set, which checks old == old_value before attempting the underlying atomic swap.; and Ruby NaN semantics, where Float::NAN == Float::NAN is always false. As a result, once an AtomicReference contains Float::NAN, calling #update repeatedly evaluates the caller's block and never returns. In services that store externally derived numeric values in an AtomicReference, this can cause CPU exhaustion or permanent request/job hangs. This vulnerability is fixed in 1.3.7.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54905 concurrent-ruby is a modern concurrency tools for Ruby. Prior to 1.3.7, Concurrent::ReentrantReadWriteLock can incorrectly grant a write lock after one thread acquires the read lock 32,768 times. The lock stores a thread's local read and write hold counts in one integer. The low 15 bits are used for the read hold count, and bit 15 is used as WRITE_LOCK_HELD. After 32,768 reentrant read acquisitions, the local read count crosses into the write-lock bit. try_write_lock then treats the thread as already holding a write lock and returns true without setting the global RUNNING_WRITER bit. This breaks the core mutual-exclusion guarantee: the caller is told it has a write lock, but other threads can still hold or acquire read locks at the same time. This vulnerability is fixed in 1.3.7.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54906 concurrent-ruby is a modern concurrency tools for Ruby. Prior to 1.3.7, Concurrent::ReadWriteLock#release_write_lock does not verify that the calling thread acquired the write lock. Any thread with access to the lock object can release an active write lock held by another thread. A second writer can then enter its critical section while the first writer is still running. Concurrent::ReadWriteLock#release_read_lock also decrements the shared counter even when no read lock is held. Calling it on a fresh lock changes the counter from 0 to -1, after which normal read acquisition raises Concurrent::ResourceLimitError. This is a synchronization correctness issue in the public Concurrent::ReadWriteLock API. This vulnerability is fixed in 1.3.7.

cdw-kube-fluentd-operator
logrouter-config-reloader

CVE-2026-54911 No description available.

hue

CVE-2026-55199 libssh2 through 1.11.1, fixed in commit 1762685, contains a pre-authentication denial of service vulnerability in the SSH_MSG_EXT_INFO handler in src/packet.c that allows a malicious SSH server to cause a client CPU exhaustion loop by sending a crafted extension count value. A malicious server can set nr_extensions to 0xFFFFFFFF during key exchange, causing the client to spin in a tight CPU loop for over 60 seconds because return values from _libssh2_get_string() are unchecked and the session timeout does not apply to CPU-bound loops.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-55200 libssh2 through 1.11.1, fixed in commit 7acf3df contains an out-of-bounds write vulnerability in ssh2_transport_read() that fails to enforce upper bounds on packet_length field. Remote attackers can send crafted SSH packets with excessively large packet_length values to corrupt heap memory and achieve remote code execution.

kserve_huggingfaceserver
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-r4.5-freshline

CVE-2026-55389 datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. Prior to 0.62.0, datamodel-code-generator resolves JSON Schema $ref targets in src/datamodel_code_generator/parser/jsonschema.py through is_url and _get_ref_body without containing file:// or ../ traversal references to the input directory and without honoring --no-allow-remote-refs, allowing arbitrary local file reads. This issue is fixed in version 0.62.0.

model-registry

CVE-2026-55391 datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. Prior to 0.63.0, datamodel-code-generator validates a URL host once in src/datamodel_code_generator/http.py through get_body, _validate_url_for_fetch, and _get_ips_from_host, but then lets httpx resolve the host again for the connection, allowing DNS rebinding to bypass allow_private_network=False and reach internal services. This issue is fixed in version 0.63.0.

model-registry

CVE-2026-55403 datamodel-code-generator generates Python data models from schema definitions. Prior to 0.63.0, src/datamodel_code_generator/http.py get_body reuses Authorization, Cookie, and Proxy-Authorization headers when following cross-origin redirects while fetching remote schemas, allowing credentials scoped to one schema host to be leaked to another redirect target. This issue is fixed in version 0.63.0.

model-registry

CVE-2026-55415 datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.11.6 until 0.64.0, datamodel-code-generator allows attacker-controlled x-python-import or customTypePath schema extensions to reach src/datamodel_code_generator/parser/jsonschema.py and generated import handling through Import.from_full_path and Imports.create_line in src/datamodel_code_generator/imports.py, allowing a newline to break out of an import statement and execute Python code when the generated model is imported. This issue is fixed in version 0.64.0.

model-registry

CVE-2026-55654 A flaw was found in OpenSSH. This vulnerability, a heap out-of-bounds read, occurs during the cleanup of GSSAPI (Generic Security Service Application Programming Interface) indicators when a trailing NULL termination is missing in the auth-indicators array. A remote attacker, under specific configurations involving GSSAPI authentication and a Kerberos environment, could exploit this to cause the SSH authentication path to crash or abort. This leads to a denial of service (DoS), impacting the availability of the SSH service.

cloudera-ai-rag-studio
cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime
runtimedataviz

CVE-2026-55655 A flaw was found in OpenSSH. A local unprivileged attacker on a Linux client host can hijack client-side X11 forwarding connections. This is possible by pre-binding the preferred abstract X socket name when X11 forwarding is enabled and a local UNIX-domain X socket is used. A successful attack can compromise the confidentiality of forwarded X11 traffic, including sensitive window contents and input, and may allow some manipulation of the forwarded session.

cloudera-ai-rag-studio
cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4
python-runtime
runtimedataviz

CVE-2026-58050 libssh2 through 1.11.1 reads an attacker-controlled 32-bit attribute count from a publickey-subsystem response and uses it in the allocation num_attrs * sizeof(libssh2_publickey_attribute) without bounds checking, so on 32-bit platforms the multiplication overflows to an undersized buffer. A malicious SSH server can then drive the attribute-parsing loop to write past the allocation, causing a heap buffer overflow in a connecting libssh2 client.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-58051 libssh2 through 1.11.1 grows its publickey list with SSH2_REALLOC but does not zero-initialize new entries before parsing populates them, so a parse failure reaching the cleanup path leaves libssh2_publickey_list_free operating on an uninitialized entry. A malicious SSH server offering the publickey subsystem can use a malformed response to make cleanup free an uninitialized, attacker-influenceable attrs pointer in a connecting libssh2 client.

ml-runtime-pbj-workbench-r4.5-standard

CVE-2026-59819 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.10-stable, LiteLLM's /health/test_connection endpoint resolved request-supplied environment and OIDC file references in litellm_params, allowing a proxy administrator or another privileged caller with permission to test model connections to read files from the local filesystem via an oidc/file/ reference. This issue is fixed in version 1.83.10-stable.

cloudera-ai-rag-studio
nim-deepseek-r1-v1.7.3

CVE-2026-59820 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.7-stable, LiteLLM Skills archive extraction did not sufficiently validate file paths from uploaded skill ZIP archives, allowing an authenticated user with access to LiteLLM LLM API routes or a key whose allowed_routes includes /v1/skills, anthropic_routes, or llm_api_routes to upload a crafted skill archive containing path traversal entries that could be written outside the intended extraction or staging directory. This issue is fixed in version 1.83.7-stable.

nim-deepseek-r1-v1.7.3

CVE-2026-59821 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.82.0-stable, LiteLLM's Custom Code Guardrails production create and update paths did not apply the same sandboxing and validation used by the test endpoint, allowing a privileged user with access to create or update guardrails to submit custom Python code that executed in the LiteLLM proxy environment and could expose secrets available to the process. This issue is fixed in version 1.82.0-stable.

nim-deepseek-r1-v1.7.3

CVE-2026-59822 LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.84.0, LiteLLM's MCP Streamable HTTP endpoint allowed an unauthenticated attacker to use a fabricated Authorization header to trigger an OAuth2 passthrough fallback path that replaced failed LiteLLM key validation with an empty UserAPIKeyAuth() object, allowing requests to reach MCP tooling without a valid LiteLLM key. This issue is fixed in version 1.84.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
nim-deepseek-r1-v1.7.3

CVE-2026-59844 A flaw was found in libssh. A remote authenticated client can issue SSH_FXP_READ requests with an arbitrarily large length, causing a libssh SFTP server to allocate excessive memory and potentially exhaust it through repeated requests.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

CVE-2026-59892 OpenTelemetry JavaScript is the OpenTelemetry JavaScript client. Prior to 2.9.0, @opentelemetry/propagator-jaeger decodes incoming uber-trace-id and uberctx-* HTTP header values with decodeURIComponent() without handling decode errors, allowing an unauthenticated remote attacker to send a malformed percent-encoded value that throws an uncaught URIError and terminates a Node.js process using JaegerPropagator as the active propagator. This issue is fixed in version 2.9.0.

cdsw-web

CVE-2026-59922 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, a run of closed tilde, equals-sign, or caret marker pairs around a character causes quadratic work in src/mistune/plugins/formatting.py when the strikethrough, mark, or insert plugin scans for matching markers from each possible start position, allowing denial of service through CPU exhaustion. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59923 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, HTMLRenderer.safe_url() does not block percent-encoded javascript URIs, allowing attacker-supplied Markdown links or images to bypass URL protections and execute script in rendered HTML. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59924 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, Include.parse() joins and normalizes user-supplied include paths without verifying that the result remains within the intended markdown directory, allowing crafted include paths to access files outside that directory when markdown files are processed using md.read(). This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59925 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, long sequences of well-formed double-asterisk or triple-asterisk emphasis pairs around a character cause quadratic work in src/mistune/inline_parser.py because the parser scans forward for matching close markers from every potential opening run, allowing denial of service in default Mistune parsing. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59926 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.2.1, render_admonition() in src/mistune/directives/admonition.py concatenates the Admonition directive :class: option into the HTML class attribute without escaping, allowing attribute injection and cross-site scripting even when HTMLRenderer escape mode is enabled. This issue is fixed in version 3.2.1.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59927 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, the Include directive in src/mistune/directives/include.py detects only direct self-includes and not indirect cycles, allowing two markdown files that include each other to trigger unbounded recursion, raise RecursionError, and crash the rendering request. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59928 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, a Markdown document containing many repeated or distinct reference-link definitions causes quadratic work in src/mistune/block_parser.py and the ref_links environment dictionary handling, allowing denial of service through CPU exhaustion. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59929 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, the safe_url filter in src/mistune/renderers/html.py blocks only javascript:, vbscript:, file:, and data: schemes, allowing legacy or chained schemes such as feed:, view-source:, jar:, livescript:, mocha:, ms-its:, mk:, and res: to reach rendered href and src attributes and potentially execute script in affected user agents. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-59930 Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, the toc plugin and TableOfContents directive generate heading IDs as predictable toc_N values without slugifying the heading text, allowing attacker-controlled id="toc_N" content to collide with generated anchors and redirect same-page navigation, CSS selectors, or JavaScript handlers. This issue is fixed in version 3.3.0.

cloudera-ai-agent-studio
cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-hardened
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-python3.14-hardened
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
runtimedataviz

CVE-2026-63793 In the Linux kernel, the following vulnerability has been resolved: ntfs: serialize volume label accesses Protect vol->volume_label with a mutex and snaphost the label before copy_to_user. This prevent a use-after-free when FS_IOC_SETFSLABEL replaces the vol->volume_label and FS_IOC_GETTSLABEL reads it concurrently.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63795 In the Linux kernel, the following vulnerability has been resolved: 9p: avoid putting oldfid in p9_client_walk() error path When p9_client_walk() is called with clone set to false, fid aliases oldfid. If the walk subsequently fails after the request has been sent, the error path jumps to clunk_fid, which currently calls p9_fid_put(fid) unconditionally. This drops a reference to oldfid even though ownership of oldfid remains with the caller. If this is the last reference, oldfid can be clunked and destroyed while the caller still expects it to be valid. A later use or put of oldfid can then trigger a use-after-free or refcount underflow. Fix this by only putting fid in the clunk_fid error path when it does not alias oldfid, matching the existing guard in the error path below. This can be triggered when a multi-component walk is split into multiple p9_client_walk() calls and a later non-cloning walk fails. A reproducer and refcount warning logs are available on request.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63796 In the Linux kernel, the following vulnerability has been resolved: ocfs2: reject oversized group bitmap descriptors ocfs2_validate_gd_parent() only bounds bg_bits against the parent allocator's chain geometry. A malicious descriptor can still claim a bg_size/bg_bits pair that exceeds the bitmap bytes that physically fit in the group descriptor block, so later bitmap scans and bit updates can run past bg_bitmap. Add a physical-cap check based on ocfs2_group_bitmap_size() for the parent allocator type and reject descriptors whose bg_size or bg_bits exceed that capacity. Keep the existing chain geometry check so both the on-disk bitmap layout and the allocator metadata must agree before the descriptor is used. Validation reproduced this kernel report: KASAN use-after-free in _find_next_bit+0x7f/0xc0 Read of size 8 Call trace: dump_stack_lvl+0x66/0xa0 (?:?) print_report+0xd0/0x630 (?:?) _find_next_bit+0x7f/0xc0 (?:?) srso_alias_return_thunk+0x5/0xfbef5 (?:?) __virt_addr_valid+0x188/0x2f0 (?:?) kasan_report+0xe4/0x120 (?:?) ocfs2_find_max_contig_free_bits+0x35/0x70 (fs/ocfs2/suballoc.c:1375) ocfs2_block_group_set_bits+0x472/0x4b0 (fs/ocfs2/suballoc.c:1457) ocfs2_cluster_group_search+0x16b/0x440 (fs/ocfs2/suballoc.c:86) ocfs2_bg_discontig_fix_result+0x1ef/0x230 (fs/ocfs2/suballoc.c:1786) ocfs2_search_chain+0x8f8/0x10a0 (fs/ocfs2/suballoc.c:1886) get_page_from_freelist+0x70e/0x2370 (?:?) lock_release+0xc6/0x290 (?:?) do_raw_spin_unlock+0x9a/0x100 (?:?) kasan_unpoison+0x27/0x60 (?:?) __bfs+0x147/0x240 (?:?) get_page_from_freelist+0x83d/0x2370 (?:?) ocfs2_claim_suballoc_bits+0x38c/0xe70 (fs/ocfs2/suballoc.c:96) sched_domains_numa_masks_clear+0x70/0xd0 (?:?) check_irq_usage+0xe8/0xb70 (?:?) __ocfs2_claim_clusters+0x18d/0x4c0 (fs/ocfs2/suballoc.c:2497) check_path+0x24/0x50 (?:?) rcu_is_watching+0x20/0x50 (?:?) check_prev_add+0xfd/0xd00 (?:?) ocfs2_add_clusters_in_btree+0x17d/0x810 (fs/ocfs2/suballoc.c:?) __folio_batch_add_and_move+0x1f5/0x3d0 (?:?) ocfs2_add_inode_data+0xd9/0x120 (fs/ocfs2/suballoc.c:?) filemap_add_folio+0x105/0x1f0 (?:?) ocfs2_write_begin_nolock+0x29f7/0x2f80 (fs/ocfs2/suballoc.c:3043) ocfs2_read_inode_block+0xb5/0x110 (fs/ocfs2/suballoc.c:?) down_write+0xf5/0x180 (?:?) ocfs2_write_begin+0x180/0x240 (fs/ocfs2/suballoc.c:?) __mark_inode_dirty+0x758/0x9a0 (?:?) inode_to_bdi+0x41/0x90 (?:?) balance_dirty_pages_ratelimited_flags+0xf8/0x1d0 (?:?) generic_perform_write+0x252/0x440 (?:?) mnt_put_write_access_file+0x16/0x70 (?:?) file_update_time_flags+0xe4/0x200 (?:?) ocfs2_file_write_iter+0x80a/0x1320 (fs/ocfs2/suballoc.c:?) lock_acquire+0x184/0x2f0 (?:?) ksys_write+0xd2/0x170 (?:?) apparmor_file_permission+0xf5/0x310 (?:?) read_zero+0x8d/0x140 (?:?) lock_is_held_type+0x8f/0x100 (?:?)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63797 In the Linux kernel, the following vulnerability has been resolved: rpmsg: char: Fix use-after-free on probe error path rpmsg_chrdev_probe() stores the newly allocated eptdev in the default endpoint's priv pointer before calling rpmsg_chrdev_eptdev_add(). If rpmsg_chrdev_eptdev_add() then fails, its error path frees eptdev while the default endpoint may still dispatch callbacks with the stale priv pointer. Avoid publishing eptdev through the default endpoint until rpmsg_chrdev_eptdev_add() succeeds. Messages received before the priv pointer is published should be ignored by rpmsg_ept_cb(). Flow-control updates can hit rpmsg_ept_flow_cb() in the same window, so make both callbacks return success when priv is NULL.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63798 In the Linux kernel, the following vulnerability has been resolved: irqchip/imgpdc: Fix resource leak, add missing chained handler cleanup on remove The driver allocates domain generic chips using irq_alloc_domain_generic_chips() during probe and sets up chained handlers using irq_set_chained_handler_and_data(). However, on driver removal, the generic chips are not freed and the chained handlers are not removed. The generic chips remain on the global gc_list and may later be accessed by generic interrupt chip suspend, resume, or shutdown callbacks after the driver has been removed, potentially resulting in a use-after-free and kernel crash. The chained handlers that were installed in probe for peripheral and syswake interrupts are also left dangling, which can lead to spurious interrupts accessing freed memory. Fix these issues by: - Setting IRQ_DOMAIN_FLAG_DESTROY_GC flag in domain->flags, so the core code automatically removes generic chips when irq_domain_remove() is called - Clearing all chained handlers with NULL in pdc_intc_remove()

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63800 In the Linux kernel, the following vulnerability has been resolved: pNFS: Fix use-after-free in pnfs_update_layout() When hitting the NFS_LAYOUT_RETURN branch in pnfs_update_layout(), the code calls pnfs_prepare_to_retry_layoutget(lo). If it succeeds, pnfs_put_layout_hdr(lo) is called before trace_pnfs_update_layout(), which still references 'lo'. This results in a use-after-free when the tracepoint accesses lo's fields. Fix this by moving the tracepoint call before pnfs_put_layout_hdr(lo).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63801 In the Linux kernel, the following vulnerability has been resolved: tipc: fix slab-use-after-free Read in tipc_aead_decrypt_done tipc_aead_decrypt() goes straight from tipc_bearer_hold(b) to crypto_aead_decrypt(req) without taking a reference on the netns, unlike the encrypt path. When crypto_aead_decrypt() is offloaded asynchronously (e.g. the SIMD aead wrapper queuing to cryptd), the cryptd worker runs tipc_aead_decrypt_done() later. If the bearer's netns is torn down in the meantime, cleanup_net() -> tipc_exit_net() -> tipc_crypto_stop() frees the per-netns tipc_crypto, and the completion then reads it: tipc_aead_decrypt_done() dereferences aead->crypto->stats and aead->crypto->net, and tipc_crypto_rcv_complete() dereferences aead->crypto->aead[] and the node table -- reading freed memory. Decoded KASAN splat (v7.1-rc7, CONFIG_KASAN_INLINE + TIPC + TIPC_CRYPTO): BUG: KASAN: slab-use-after-free in tipc_aead_decrypt_done (net/tipc/crypto.c:999) Read of size 8 at addr ffff8881056258a8 by task kworker/u16:2/51 Workqueue: events_unbound Call Trace: tipc_aead_decrypt_done (net/tipc/crypto.c:999) process_one_work (kernel/workqueue.c:3314) worker_thread (kernel/workqueue.c:3397 kernel/workqueue.c:3478) kthread (kernel/kthread.c:436) ret_from_fork (arch/x86/kernel/process.c:158) ret_from_fork_asm (arch/x86/entry/entry_64.S:245) Allocated by task 169: __kasan_kmalloc (mm/kasan/common.c:398 mm/kasan/common.c:415) tipc_crypto_start (net/tipc/crypto.c:1502) tipc_init_net (net/tipc/core.c:72) ops_init (net/core/net_namespace.c:137) setup_net (net/core/net_namespace.c:446) copy_net_ns (net/core/net_namespace.c:579) create_new_namespaces (kernel/nsproxy.c:132) __x64_sys_unshare (kernel/fork.c:3316) do_syscall_64 (arch/x86/entry/syscall_64.c:63) entry_SYSCALL_64_after_hwframe (arch/x86/entry/entry_64.S:121) Freed by task 8: kfree (mm/slub.c:6566) tipc_exit_net (net/tipc/core.c:119) cleanup_net (net/core/net_namespace.c:704) process_one_work (kernel/workqueue.c:3314) kthread (kernel/kthread.c:436) This is the same class of bug that commit e279024617134 ("net/tipc: fix slab-use-after-free Read in tipc_aead_encrypt_done") fixed for the encrypt side. The encrypt path takes maybe_get_net(aead->crypto->net) before crypto_aead_encrypt() and drops it with put_net() on the synchronous return paths and in tipc_aead_encrypt_done(); the -EINPROGRESS/-EBUSY return keeps the reference for the async callback to release. The decrypt path was left without the equivalent guard. Mirror the encrypt-side fix on the decrypt path: take a net reference before crypto_aead_decrypt() (failing with -ENODEV and the matching bearer put if it cannot be acquired), keep it across the -EINPROGRESS/-EBUSY async return, and drop it with put_net() on the synchronous success/error return and at the end of tipc_aead_decrypt_done(). Reproduced under KASAN on v7.1-rc7: a UDP bearer with a cluster key is flooded with crafted encrypted frames from an unknown peer (driving the cluster-key decrypt path) while the bearer's netns is repeatedly torn down. The completion must run asynchronously to outlive tipc_crypto_stop(); on x86 the stock aesni gcm(aes) now decrypts synchronously, so the async path was exercised via cryptd offload. The unguarded aead->crypto dereference in tipc_aead_decrypt_done() is the unpatched upstream path; tipc_aead_decrypt() still lacks maybe_get_net(aead->crypto->net), so the completion can outlive the free on any config where crypto_aead_decrypt() goes async. Found by 0sec automated security-research tooling (https://0sec.ai).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63802 In the Linux kernel, the following vulnerability has been resolved: blk-cgroup: fix UAF in __blkcg_rstat_flush() When multiple blkgs in the same blkcg are released concurrently, a use-after-free can occur. The race happens when one blkg's __blkcg_rstat_flush() removes another blkg's iostat entries via llist_del_all(). The second blkg sees an empty list and proceeds to free itself while the first is still iterating over its entries. Move the flush from __blkg_release() (RCU callback) to blkg_release() (before call_rcu). This ensures the RCU grace period waits for any concurrent flush's rcu_read_lock() section to complete before freeing.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63806 In the Linux kernel, the following vulnerability has been resolved: KVM: Replace guest-triggerable BUG_ON() in ioeventfd datamatch with get_unaligned() Drop a BUG_ON() that has been reachable since it was first added, way back in 2009, and instead use get_unaligned() to perform potentially-unaligned accesses. For a given store, KVM x86's emulator tracks the entire value in the destination operand, x86_emulate_ctxt.dst. If the destination is memory, and the target splits multiple pages and/or is emulated MMIO, then KVM handles each fragment independently. E.g. on a page split starting at page offset 0xffc, KVM writes 4 bytes to the first page, then the remaining bytes to the second page, using ctxt->dst as the source for both (with appropriate offsets). If the destination splits a page *and* hits emulated MMIO on the second page, then KVM will complete the write to the first page, then emulate the MMIO access to the second page. If there is a datamatch-enabled ioeventfd at offset 0 of the second page, then KVM will process the remainder of the store as a potential ioeventfd signal. Putting it all together, if the guest emits a store that splits a page starting at page offset N, and the second page has a datamatch-enabled ioeventfd at offset 0, then KVM will check for datamatch using &dst.valptr[N] as the source. Due to dst (and thus dst.valptr) being 32-byte aligned, if N is not aligned to @len, the BUG_ON() fires. E.g. with a 16-byte store at page offset 0xffc, to an ioeventfd of len 8, all initial checks in ioeventfd_in_range() will succeed, and the BUG_ON() fires due to @val being 4-byte aligned, but not 8-byte aligned. ------------[ cut here ]------------ kernel BUG at arch/x86/kvm/../../../virt/kvm/eventfd.c:783! Oops: invalid opcode: 0000 [#1] SMP CPU: 0 UID: 1000 PID: 615 Comm: repro Not tainted 7.1.0-rc2-ff238429d1ea #365 PREEMPT Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 0.0.0 02/06/2015 RIP: 0010:ioeventfd_write+0x6c/0x70 [kvm] Call Trace: <TASK> __kvm_io_bus_write+0x85/0xb0 [kvm] kvm_io_bus_write+0x53/0x80 [kvm] vcpu_mmio_write+0x66/0xf0 [kvm] emulator_read_write_onepage+0x12a/0x540 [kvm] emulator_read_write+0x109/0x2b0 [kvm] x86_emulate_insn+0x4f8/0xfb0 [kvm] x86_emulate_instruction+0x181/0x790 [kvm] kvm_mmu_page_fault+0x313/0x630 [kvm] vmx_handle_exit+0x18a/0x590 [kvm_intel] kvm_arch_vcpu_ioctl_run+0xc81/0x1c90 [kvm] kvm_vcpu_ioctl+0x2d5/0x970 [kvm] __x64_sys_ioctl+0x8a/0xd0 do_syscall_64+0xb7/0x890 entry_SYSCALL_64_after_hwframe+0x4b/0x53 RIP: 0033:0x7f19c931a9bf </TASK> Modules linked in: kvm_intel kvm irqbypass ---[ end trace 0000000000000000 ]--- In a perfect world, the fix would be to simply delete the BUG_ON(), as KVM x86 doesn't perform alignment checks on "normal" memory accesses at CPL0. Sadly, C99 ruins all the fun; while the x86 architecture plays nice, dereferencing an unaligned pointer directly is undefined behavior in C, e.g. triggers splats when running with CONFIG_UBSAN_ALIGNMENT=y.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63808 In the Linux kernel, the following vulnerability has been resolved: exfat: fix potential use-after-free in exfat_find_dir_entry() In exfat_find_dir_entry(), the buffer_head obtained from exfat_get_dentry() is released with brelse(bh) before the fall-through TYPE_EXTEND branch reads the directory entry through ep (which points into bh->b_data): brelse(bh); if (entry_type == TYPE_EXTEND) { ... len = exfat_extract_uni_name(ep, entry_uniname); ... } After brelse() drops our reference, nothing guarantees that the underlying page backing bh->b_data remains valid for the subsequent exfat_extract_uni_name() read. This is the same pattern fixed in commit fc961522ddbd ("exfat: Fix potential use after free in exfat_load_upcase_table()"). Move brelse(bh) so it runs after ep is no longer dereferenced on each branch. Confirmed on QEMU x86_64 with CONFIG_KASAN=y + CONFIG_DEBUG_PAGEALLOC=y + CONFIG_PAGE_POISONING=y on linux-next, using a crafted exFAT image (long filename with same-hash collisions forcing the TYPE_EXTEND path). With a debug-only invalidate_bdev() inserted between brelse(bh) and the ep read to make the stale-deref window deterministic, the unpatched kernel faults: BUG: KASAN: use-after-free in exfat_find_dir_entry+0x133b/0x15a0 BUG: unable to handle page fault for address: ffff88801a5fa0c2 Oops: 0000 [#1] SMP DEBUG_PAGEALLOC KASAN NOPTI RIP: 0010:exfat_find_dir_entry+0x1188/0x15a0 With this patch applied, the same instrumented harness completes cleanly under the same sanitizer stack. I have not reproduced a crash on an uninstrumented kernel under ordinary reclaim; the instrumented A/B establishes the lifetime violation and that the patch closes it, not an unaided triggerability claim.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63809 In the Linux kernel, the following vulnerability has been resolved: bpf: use kvfree() for replaced sysctl write buffer proc_sys_call_handler() allocates its temporary sysctl buffer with kvzalloc() and passes it to __cgroup_bpf_run_filter_sysctl(). Since kvzalloc() may fall back to vmalloc() for large allocations, freeing that buffer with kfree() is wrong and can corrupt memory. Use kvfree() to safely handle both kmalloc and kvzalloc()/vmalloc allocations. The bug was first flagged by an experimental analysis tool we are developing for kernel memory-management bugs while analyzing v6.13-rc1. The tool is still under development and is not yet publicly available. Manual inspection confirms that the bug is still present in v7.1-rc5. Reproduced the bug based on v7.1-rc4 in a QEMU x86_64 guest booted with KASAN and CONFIG_FAILSLAB enabled. To exercise the replacement path, the test tree also included the accompanying fix for the stale ret == 1 check in __cgroup_bpf_run_filter_sysctl(). The reproducer confines failslab injections to the proc_sys_call_handler() range, uses stacktrace-depth=32, and injects fail-nth=1 while writing 8191 bytes to /proc/sys/kernel/domainname from a task in the target cgroup. Under that setup, fail-nth=1 triggered the fault: BUG: unable to handle page fault for address: ffffeb0200024d48 #PF: supervisor read access in kernel mode #PF: error_code(0x0000) - not-present page PGD 0 P4D 0 Oops: Oops: 0000 SMP KASAN NOPTI CPU: 2 UID: 0 PID: 209 Comm: repro_proc_sys_ Not tainted 7.1.0-rc4-00686-g97625979a5d4 PREEMPT(lazy) Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 1.15.0-1 04/01/2014 RIP: 0010:kfree+0x6e/0x510 ... Call Trace: <TASK> ? __cgroup_bpf_run_filter_sysctl+0x626/0xc30 __cgroup_bpf_run_filter_sysctl+0x74d/0xc30 ? __pfx___cgroup_bpf_run_filter_sysctl+0x10/0x10 ? srso_return_thunk+0x5/0x5f ? __kvmalloc_node_noprof+0x345/0x870 ? proc_sys_call_handler+0x250/0x480 ? srso_return_thunk+0x5/0x5f proc_sys_call_handler+0x3a2/0x480 ? __pfx_proc_sys_call_handler+0x10/0x10 ? srso_return_thunk+0x5/0x5f ? selinux_file_permission+0x39f/0x500 ? srso_return_thunk+0x5/0x5f ? lock_is_held_type+0x9e/0x120 vfs_write+0x98e/0x1000 ... </TASK> With this fix applied on top of the same test setup, rerunning the reproducer with fail-nth=1 yields no corresponding Oops reports.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63810 In the Linux kernel, the following vulnerability has been resolved: block: Avoid mounting the bdev pseudo-filesystem in userspace The bdev pseudo-filesystem is an internal kernel filesystem with which userspace should not interfere. Unregister it so that userspace cannot even attempt to mount it. This fixes a bug [1] that occurs when attempting to access files, because the system call move_mount() uses pointers declared in the inode_operations structure, which for the bdev pseudo-filesystem are always equal to 0. `inode->i_op = &empty_iops;` [1] BUG: kernel NULL pointer dereference, address: 0000000000000000 #PF: supervisor instruction fetch in kernel mode #PF: error_code(0x0010) - not-present page PGD 23380067 P4D 23380067 PUD 23381067 PMD 0 Oops: 0010 [#1] PREEMPT SMP KASAN NOPTI CPU: 2 PID: 17125 Comm: syz-executor.0 Not tainted 6.1.155-syzkaller-00350-g84221fde2681 #0 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.12.0-1 04/01/2014 RIP: 0010:0x0 Call Trace: <TASK> lookup_open.isra.0+0x700/0x1180 fs/namei.c:3460 open_last_lookups fs/namei.c:3550 [inline] path_openat+0x953/0x2700 fs/namei.c:3780 do_filp_open+0x1c5/0x410 fs/namei.c:3810 do_sys_openat2+0x171/0x4d0 fs/open.c:1318 do_sys_open fs/open.c:1334 [inline] __do_sys_openat fs/open.c:1350 [inline] __se_sys_openat fs/open.c:1345 [inline] __x64_sys_openat+0x13c/0x1f0 fs/open.c:1345 do_syscall_x64 arch/x86/entry/common.c:51 [inline] do_syscall_64+0x35/0x80 arch/x86/entry/common.c:81 entry_SYSCALL_64_after_hwframe+0x6e/0xd8 Found by Linux Verification Center (linuxtesting.org) with Syzkaller.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63812 In the Linux kernel, the following vulnerability has been resolved: f2fs: fix incorrect FI_NO_EXTENT handling in __destroy_extent_node() When __destroy_extent_node() sets the inode flag FI_NO_EXTENT, it does not reset the length of the largest extent to 0 and update the inode folio. Since modifications to the extent tree are disallowed afterward, the cached largest extent may become stale. This can trigger the following error in xfstests generic/388: F2FS-fs (dm-0): sanity_check_extent_cache: inode (ino=1761) extent info [220057, 57, 6] is incorrect, run fsck to fix In the f2fs_drop_inode path, __destroy_extent_node() does not need to guarantee that et->node_cnt is 0, because concurrency with writeback is expected in this path, and writeback may update the extent cache. This patch reverts commit ed78aeebef05 ("f2fs: fix node_cnt race between extent node destroy and writeback"), and remove the unnecessary zero check of et->node_cnt.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63814 In the Linux kernel, the following vulnerability has been resolved: f2fs: validate ACL entry sizes in f2fs_acl_from_disk() f2fs_acl_count() only validates the aggregate ACL xattr length. A malformed ACL can still place ACL_USER or ACL_GROUP in a slot that only contains struct f2fs_acl_entry_short bytes, and f2fs_acl_from_disk() then reads entry->e_id before verifying that a full entry fits. Require a short entry before reading e_tag and e_perm, and require a full entry before reading e_id for ACL_USER and ACL_GROUP. Return -EFSCORRUPTED from these new truncated-entry checks, while keeping the pre-existing -EINVAL paths unchanged. Validation reproduced this kernel report: KASAN slab-out-of-bounds in __f2fs_get_acl+0x6fb/0x7e0 RIP: 0033:0x7f4b835ea7aa The buggy address belongs to the object at ffff888114589960 which belongs to the cache kmalloc-8 of size 8 The buggy address is located 0 bytes to the right of allocated 8-byte region [ffff888114589960, ffff888114589968) Read of size 4 Call trace: dump_stack_lvl+0x66/0xa0 (?:?) print_report+0xce/0x630 (?:?) __f2fs_get_acl+0x6fb/0x7e0 (fs/f2fs/acl.c:169) srso_alias_return_thunk+0x5/0xfbef5 (?:?) __virt_addr_valid+0x224/0x430 (?:?) kasan_report+0xe0/0x110 (?:?) __f2fs_get_acl+0x5/0x7e0 (fs/f2fs/acl.c:169) __get_acl+0x281/0x380 (?:?) vfs_get_acl+0x10b/0x190 (?:?) do_get_acl+0x2a/0x410 (?:?) do_get_acl+0x9/0x410 (?:?) do_getxattr+0xe8/0x260 (?:?) filename_getxattr+0xd1/0x140 (?:?) do_getname+0x2d/0x2d0 (?:?) path_getxattrat+0x16c/0x200 (?:?) lock_release+0xc8/0x290 (?:?) cgroup_update_frozen+0x9d/0x320 (?:?) lockdep_hardirqs_on_prepare+0xea/0x1a0 (?:?) trace_hardirqs_on+0x1a/0x170 (?:?) _raw_spin_unlock_irq+0x28/0x50 (?:?) do_syscall_64+0x115/0x6a0 (arch/x86/entry/syscall_64.c:87) entry_SYSCALL_64_after_hwframe+0x77/0x7f (?:?)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63815 In the Linux kernel, the following vulnerability has been resolved: f2fs: bound i_inline_xattr_size for non-inline-xattr inodes When the flexible_inline_xattr feature is enabled, do_read_inode() loads the on-disk i_inline_xattr_size unconditionally: if (f2fs_sb_has_flexible_inline_xattr(sbi)) fi->i_inline_xattr_size = le16_to_cpu(ri->i_inline_xattr_size); but sanity_check_inode() only range-checks it when the inode also has the FI_INLINE_XATTR flag set. An inode that carries an inline dentry or inline data but not FI_INLINE_XATTR -- the normal layout for an inline directory -- therefore keeps a fully attacker-controlled i_inline_xattr_size from a crafted image. get_inline_xattr_addrs() returns that value with no flag gating, so it feeds the inode geometry: MAX_INLINE_DATA() = 4 * (CUR_ADDRS_PER_INODE - i_inline_xattr_size - 1) NR_INLINE_DENTRY() = MAX_INLINE_DATA() * BITS_PER_BYTE / (...) addrs_per_page() = CUR_ADDRS_PER_INODE - i_inline_xattr_size A large i_inline_xattr_size drives MAX_INLINE_DATA() and NR_INLINE_DENTRY() negative, so make_dentry_ptr_inline() sets d->max (int) to a negative value. The inline directory walk then compares an unsigned long bit_pos against that negative d->max, which is promoted to a huge unsigned bound, and reads far past the inline area: while (bit_pos < d->max) /* fs/f2fs/dir.c */ ... test_bit_le(bit_pos, d->bitmap) / d->dentry[bit_pos] ... Mounting a crafted image and reading such a directory triggers an out-of-bounds read in f2fs_fill_dentries(); the same underflow also corrupts ADDRS_PER_INODE for regular files. Validate i_inline_xattr_size against MAX_INLINE_XATTR_SIZE whenever the flexible_inline_xattr feature is enabled -- i.e. whenever the value is loaded from disk and consumed -- and keep the lower MIN_INLINE_XATTR_SIZE bound gated on inodes that actually carry an inline xattr, so legitimate inodes with i_inline_xattr_size == 0 are still accepted.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63816 In the Linux kernel, the following vulnerability has been resolved: f2fs: atomic: fix UAF issue on f2fs_inode_info.atomic_inode - ioctl(F2FS_IOC_GARBAGE_COLLECT_RANGE) - shrink - f2fs_gc - gc_data_segment - ra_data_block(cow_inode) - mapping = F2FS_I(inode)->atomic_inode->i_mapping : f2fs_is_cow_file(cow_inode) is true - f2fs_evict_inode(atomic_inode) - clear_inode_flag(fi->cow_inode, FI_COW_FILE) - F2FS_I(fi->cow_inode)->atomic_inode = NULL ... - truncate_inode_pages_final(atomic_inode) - f2fs_grab_cache_folio(mapping) : create folio in atomic_inode->mapping - clear_inode(atomic_inode) - BUG_ON(atomic_inode->i_data.nrpages) We need to add a reference on fi->atomic_inode before using its mapping field during garbage collection, otherwise, it will cause UAF issue.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63817 In the Linux kernel, the following vulnerability has been resolved: f2fs: validate compress cache inode only when enabled F2FS_COMPRESS_INO() uses NM_I(sbi)->max_nid as the synthetic inode number for the compressed page cache inode. That inode only exists when the compress_cache mount option is enabled. When compress_cache is disabled, max_nid is outside the valid inode range. A corrupted directory entry that points to ino == max_nid should therefore be rejected by f2fs_check_nid_range(). However, is_meta_ino() currently treats F2FS_COMPRESS_INO() as a meta inode unconditionally, so f2fs_iget() bypasses do_read_inode() and its nid range check, and instantiates a fake internal inode instead. Gate the compressed cache inode case on COMPRESS_CACHE, matching f2fs_init_compress_inode(). With compress_cache disabled, ino == max_nid now follows the normal inode path and is rejected as an out-of-range nid.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63818 In the Linux kernel, the following vulnerability has been resolved: f2fs: validate orphan inode entry count f2fs_recover_orphan_inodes() trusts the orphan block entry_count when replaying orphan inodes from the checkpoint pack. A corrupted entry_count larger than F2FS_ORPHANS_PER_BLOCK makes the recovery loop read past the ino[] array and interpret footer or following data as inode numbers. On a crafted image, mounting an unpatched kernel can drive orphan recovery into f2fs_bug_on() and panic the kernel. Validate entry_count before consuming entries so corrupted checkpoint data fails the mount with -EFSCORRUPTED and requests fsck instead. Set ERROR_INCONSISTENT_ORPHAN as well, so the corruption reason can be recorded in the superblock s_errors[] field. This gives fsck a persistent hint even though mount-time orphan recovery failure may leave no chance to persist SBI_NEED_FSCK through a checkpoint.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63821 In the Linux kernel, the following vulnerability has been resolved: wifi: rtw88: usb: fix memory leaks on USB write failures When rtw_usb_write_port() fails to submit a USB Request Block (URB) (e.g., due to device disconnect or ENOMEM), the completion callback is never executed. Currently, the driver ignores the return value of rtw_usb_write_port() in rtw_usb_write_data() and rtw_usb_tx_agg_skb(). Because these functions rely on the completion callback to free the socket buffers (skbs) and the transaction control block (txcb), a submission failure results in: 1. A memory leak of the allocated skb in rtw_usb_write_data(). 2. A memory leak of the txcb structure and all aggregated skbs in rtw_usb_tx_agg_skb(). Fix this by checking the return value of rtw_usb_write_port(). If it fails, explicitly free the skb in rtw_usb_write_data(), and properly purge the tx_ack_queue and free the txcb in rtw_usb_tx_agg_skb(). The issue was discovered in practice during device disconnect/reconnect scenarios and memory pressure conditions. Tested by verifying normal TX operation continues after the fix without regressions.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63824 In the Linux kernel, the following vulnerability has been resolved: KEYS: fix overflow in keyctl_pkey_params_get_2() The length for the internal output buffer is calculated incorrectly, which can result overflow when a too small buffer is provided. Fix the bug by allocating internal output with the size of the maximum length of the cryptographic primitive instead of caller provided size.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63827 In the Linux kernel, the following vulnerability has been resolved: apparmor: fix use-after-free in rawdata dedup loop aa_replace_profiles() walks ns->rawdata_list to dedup the incoming policy blob against entries already attached to existing profiles. Per the kernel-doc on struct aa_loaddata, list membership does not hold a reference: profiles hold pcount, and when the last pcount drops, do_ploaddata_rmfs() is queued on a workqueue that takes ns->lock and removes the entry. Between dropping the last pcount and the workqueue running, an entry remains on the list with pcount == 0. aa_get_profile_loaddata() is an unconditional kref_get() on pcount, so when the dedup loop hits such an entry, refcount hardening reports refcount_t: addition on 0; use-after-free. inside aa_replace_profiles(), and the poisoned counter then trips "saturated" and "underflow" warnings on the subsequent uses of the same loaddata. Before commit a0b7091c4de4 ("apparmor: fix race on rawdata dereference") the dedup path used a get_unless_zero-style helper on a single counter, so the existing "if (tmp)" guard was meaningful. The split-refcount refactor introduced aa_get_profile_loaddata(), which has plain kref_get() semantics, and the guard quietly became a no-op. Introduce aa_get_profile_loaddata_not0(), matching the existing _not0 convention used by aa_get_profile_not0(), and use it for the rawdata_list dedup lookup so dying entries are skipped. Reproduced on x86_64 with v7.1-rc5 in QEMU+KVM running Ubuntu 24.04 + stress-ng 0.17.06: stress-ng --apparmor 1 --klog-check --timeout 60s Without this patch the three refcount_t warnings fire within a few seconds. With it the same 60 s run is clean. Coverage is a smoke-test only; a longer soak with CONFIG_KASAN, CONFIG_KCSAN and CONFIG_PROVE_LOCKING would be welcome from anyone with the cycles.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63828 In the Linux kernel, the following vulnerability has been resolved: apparmor: mediate the implicit connect of TCP fast open sendmsg sendmsg()/sendto() with MSG_FASTOPEN is a combination of connect(2) and write(2): it opens the connection in the SYN. apparmor_socket_sendmsg() only checks AA_MAY_SEND, so a profile that grants send but denies connect lets a confined task open an outbound TCP/MPTCP connection that connect(2) would have refused, bypassing connect mediation. Mediate the implicit connect when MSG_FASTOPEN is set and a destination is supplied. Add it to apparmor_socket_sendmsg() (not the shared aa_sock_msg_perm() helper, which recvmsg also uses) and call aa_sk_perm() directly, mirroring the selinux and tomoyo fixes. sk_is_tcp() does not cover MPTCP fast open, so the SOCK_STREAM/IPPROTO_MPTCP arm is explicit.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63833 In the Linux kernel, the following vulnerability has been resolved: ntfs3: reject direct userspace writes to reserved $LX* xattrs NTFS3 uses $LXUID, $LXGID, $LXMOD and $LXDEV as internal WSL permission metadata and reloads them into i_uid, i_gid and i_mode from ntfs_get_wsl_perm(). Because the empty-prefix xattr handler also lets file owners call setxattr() on these names directly, an unprivileged writer on a writable ntfs3 mount can plant root ownership and S_ISUID on their own file and gain euid 0 after inode reload. Reject direct userspace writes to the reserved $LX* names. Internal ntfs3 metadata updates are unchanged because ntfs_save_wsl_perm() writes them via ntfs_set_ea() directly. [almaz.alexandrovich@paragon-software.com: added an additional check for non privileged users]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63834 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tp_meter: restrict number of unacked list entries When the unacked_list is unbound, an attacker could send messages with small lengths and appropriated seqno + gaps to force the receiver to allocate more and more unacked_list entries. And the end either causing an out-of-memory situation or increase the management overhead for the (large) list that significant portions of CPU cycles are wasted in searching through the list. When limiting the list to a specific number, it is important to still correctly add a new entry to the list. But if the list became larger than the limit, the last entry of the list (with the highest seqno) must be dropped to still allow the earlier seqnos to finish and therefore to continue the process. Otherwise, the process might get stuck with too high seqnos which are not handled by batadv_tp_ack_unordered().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63835 In the Linux kernel, the following vulnerability has been resolved: batman-adv: v: prevent OGM aggregation on disabled hardif When an interface gets disabled, the worker is correctly disabled by batadv_hardif_disable_interface() -> ... -> batadv_v_ogm_iface_disable(). In this process, the skb aggr_list is also freed. But batadv_v_ogm_send_meshif() can still queue new skbs (via batadv_v_ogm_queue_on_if()) to the aggr_list. This will only stop after all cores can no longer find the RCU protected list of hard interfaces. These queued skbs will never be freed or consumed by batadv_v_ogm_aggr_work. The batadv_v_ogm_iface_disable() function must block batadv_v_ogm_queue_on_if() to avoid leak of skbs.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63836 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tp_meter: avoid divide-by-zero for dec_cwnd The cwnd is always MSS <= cwnd <= 0x20000000. But the calculation in batadv_tp_update_cwnd() assumes unsigned 32 bit arithmetics. ((mss * 8) ** 2) / (cwnd * 8) In case cwnd is actually 0x20000000, it will be shifted by 3 bit to the left end up at 0x100000000 or U32_MAX + 1. It will therefore wrap around and be 0 - resulting in: ((mss * 8) ** 2) / 0 This is of course invalid and cannot be calculated. The calculation should must be simplified to avoid this overflow: (mss ** 2) * 8 / cwnd It will keep the precision enhancement from the scaling (by 8) but avoid the overflow in the divisor. In theory, there could still be an overflow in the dividend. It is at the moment fixed to BATADV_TP_PLEN in batadv_tp_recv_ack() - so it is not an imminent problem. But allowing it to use the whole u32 bit range, would mean that it can still use up to 67 bits. To keep this calculation safe for 32 bit arithmetic, mss must never use more than floor((32 - 3) / 2) bits - or in other words: must never be larger than 16383.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63869 In the Linux kernel, the following vulnerability has been resolved: wifi: mac80211: limit injected antenna index in ieee80211_parse_tx_radiotap When parsing the radiotap header of an injected frame, ieee80211_parse_tx_radiotap() uses the IEEE80211_RADIOTAP_ANTENNA value directly as a shift count: info->control.antennas |= BIT(*iterator.this_arg); *iterator.this_arg is an 8-bit value taken straight from the frame supplied by userspace, so BIT() can be asked to shift by up to 255. That is undefined behaviour on the unsigned long and is reported by UBSAN: UBSAN: shift-out-of-bounds in net/mac80211/tx.c:2174:30 shift exponent 235 is too large for 64-bit type 'unsigned long' Call Trace: ieee80211_parse_tx_radiotap+0xadb/0x1950 net/mac80211/tx.c:2174 ieee80211_monitor_start_xmit+0xb1f/0x1250 net/mac80211/tx.c:2451 ... packet_sendmsg+0x3eb6/0x50f0 net/packet/af_packet.c:3109 info->control.antennas is a 2-bit bitmap (u8 antennas:2), so only antenna indices 0 and 1 can ever be represented. Ignore any larger value instead of shifting out of bounds.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63871 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: ISO: Fix data-race on iso_pi fields in hci_get_route calls iso_connect_bis(), iso_connect_cis(), iso_listen_bis(), and iso_conn_big_sync() call hci_get_route() using iso_pi(sk)->dst, iso_pi(sk)->src, and iso_pi(sk)->src_type without holding lock_sock(). These fields may be modified concurrently by connect() or setsockopt() on the same socket, resulting in data-races reported by KCSAN. Fix this by snapshotting the required fields under lock_sock() before calling hci_get_route(). BUG: KCSAN: data-race in memcmp+0x45/0xb0 race at unknown origin, with read to 0xffff8880122135cf of 1 bytes by task 333 on cpu 1: memcmp+0x45/0xb0 hci_get_route+0x27e/0x490 iso_connect_cis+0x4c/0xa10 iso_sock_connect+0x60e/0xb30 __sys_connect_file+0xbd/0xe0 __sys_connect+0xe0/0x110 __x64_sys_connect+0x40/0x50 x64_sys_call+0xcad/0x1c60 do_syscall_64+0x133/0x590 entry_SYSCALL_64_after_hwframe+0x77/0x7f

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63872 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63876 In the Linux kernel, the following vulnerability has been resolved: serial: zs: Convert to use a platform device Prevent a crash from happening as the first serial port is initialised: Console: switching to mono frame buffer device 160x64 fb0: PMAG-AA frame buffer device at tc0 DECstation Z85C30 serial driver version 0.10 CPU 0 Unable to handle kernel paging request at virtual address 0000002c, epc == 803ab00c, ra == 803aafe0 Oops[#1]: CPU: 0 PID: 1 Comm: swapper Not tainted 6.4.0-rc3-00031-g84a9582fd203-dirty #57 $ 0 : 00000000 10012c00 803aaeb0 00000000 $ 4 : 80e12f60 80e12f50 80e12f58 81000030 $ 8 : 00000000 805ff37c 00000000 33433538 $12 : 65732030 00000006 80c2915d 6c616972 $16 : 80e12f00 807b7630 00000000 00000000 $20 : 00000004 00000348 000001a0 807623b8 $24 : 00000018 00000000 $28 : 80c24000 80c25d60 8078b148 803aafe0 Hi : 00000000 Lo : 00000000 epc : 803ab00c serial_base_ctrl_add+0x78/0xf4 ra : 803aafe0 serial_base_ctrl_add+0x4c/0xf4 Status: 10012c03 KERNEL EXL IE Cause : 00000008 (ExcCode 02) BadVA : 0000002c PrId : 00000440 (R4400SC) Modules linked in: Process swapper (pid: 1, threadinfo=(ptrval), task=(ptrval), tls=00000000) Stack : 80760000 00000cc0 00400044 00400040 803aa02c 80d61ab8 00000000 807b7630 80760000 807623b8 807b7628 803aa644 80386998 00000000 80e17780 80220f68 80e17780 80d61ab8 80c17d80 80e17780 80e17780 8063c798 80e17780 80383fa0 00000010 80e17780 00000000 80386998 807a0000 00000000 00400040 8038f848 807623b8 80d61ab8 00000004 80e17780 00000000 803a68e4 80c25e2c 803bb884 ... Call Trace: [<803ab00c>] serial_base_ctrl_add+0x78/0xf4 [<803aa644>] serial_core_register_port+0x174/0x69c [<8077e9ac>] zs_init+0xc8/0xfc [<800404d4>] do_one_initcall+0x40/0x2ac [<8076cecc>] kernel_init_freeable+0x1e4/0x270 [<80605bec>] kernel_init+0x20/0x108 [<800431e8>] ret_from_kernel_thread+0x14/0x1c Code: 2442aeb0 ae120024 ae0200d0 <8c67002c> 50e00001 8c670000 3c06806e 3c05806e afb30010 ---[ end trace 0000000000000000 ]--- (report at the offending commit) -- where a pointer is dereferenced that has been derived from a null pointer to the port's parent device. Since no device is available with legacy probing and it's not anymore a preferable way to discover devices anyway, switch the driver to using a platform device and use it as the port's parent device. Update resource handling accordingly and only request the actual span of addresses used within the slot, which will have had its resource already requested by generic platform device code. Use platform_driver_probe() not just because SCC devices are fixed with solder on board and not straightforward to remove, but foremost because the associated TTY's major device number is the same as used by the dz driver and the first driver to claim it will prevent the other one from using it. Either one DZ device or some SCC devices will be present in a given system but never both at a time, and therefore we want the major device number to be claimed by the first driver to actually successfully bind to its device and platform_driver_probe() is a way to fulfil that. An unfortunate consequence of the switch to a platform device is we now hand the console over from the bootconsole much later in the bootstrap. The firmware console handler appears good enough though to work so late and in particular with interrupts enabled. Since there is one way only remaining to reach zs_reset() now, remove the port initialisation marker as no longer needed and go through the channel reset unconditionally.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63877 In the Linux kernel, the following vulnerability has been resolved: serial: dz: Convert to use a platform device Prevent a crash from happening as the first serial port is initialised: Console: switching to colour frame buffer device 160x64 tgafb: SFB+ detected, rev=0x02 fb0: Digital ZLX-E1 frame buffer device at 0x1e000000 DECstation DZ serial driver version 1.04 CPU 0 Unable to handle kernel paging request at virtual address 000000bc, epc == 8048b3a4, ra == 80470a78 Oops[#1]: CPU: 0 UID: 0 PID: 1 Comm: swapper/0 Not tainted 6.19.0-dirty #35 NONE $ 0 : 00000000 1000ac00 00000004 804707ac $ 4 : 00000000 80e20850 80e20858 81000030 $ 8 : 00000000 8072c81c 00000008 fefefeff $12 : 6c616972 00000006 80c5917f 69726420 $16 : 80e20800 00000000 808f8968 80e20800 $20 : 00000000 807f5a90 808b0094 808d3bc8 $24 : 00000018 80479030 $28 : 80c2e000 80c2fd70 00000069 80470a78 Hi : 00000004 Lo : 00000000 epc : 8048b3a4 __dev_fwnode+0x0/0xc ra : 80470a78 serial_base_ctrl_add+0xa0/0x168 Status: 1000ac04 IEp Cause : 30000008 (ExcCode 02) BadVA : 000000bc PrId : 00000220 (R3000) Modules linked in: Process swapper/0 (pid: 1, threadinfo=(ptrval), task=(ptrval), tls=00000000) Stack : 00400044 00400040 8046f4cc 00000000 808a6148 808a0000 808f8968 8086983c 808e0000 8046fc84 1000ac01 00000028 80e20700 802ba3f8 80e20700 80d34a94 80c1b900 80e20700 80e20700 80e20700 80e20700 80444650 00000000 00000000 00000000 807f5a90 808b0094 80447080 00400040 808e0000 80d34a94 808a6148 80d34a94 00000004 80e20700 00000000 8076974c 80469810 80c2fe3c 1000ac01 ... Call Trace: [<8048b3a4>] __dev_fwnode+0x0/0xc [<80470a78>] serial_base_ctrl_add+0xa0/0x168 [<8046fc84>] serial_core_register_port+0x1c8/0x974 [<808c6af0>] dz_init+0x74/0xc8 [<800470e0>] do_one_initcall+0x44/0x2d4 [<808b111c>] kernel_init_freeable+0x258/0x308 [<8072e434>] kernel_init+0x20/0x114 [<80049cd0>] ret_from_kernel_thread+0x14/0x1c Code: 27bd0018 03e00008 2402ffea <8c8200bc> 03e00008 00000000 27bdffc0 afbe0038 afb30024 ---[ end trace 0000000000000000 ]--- -- where a pointer is dereferenced that has been derived from a null pointer to the port's parent device. Since no device is available with legacy probing and it's not anymore a preferable way to discover devices anyway, switch the driver to using a platform device and use it as the port's parent device. Update resource handling accordingly and only request the actual span of addresses used within the slot, which will have had its resource already requested by generic platform device code. Use platform_driver_probe() not just because the DZ device is fixed with solder on board and not straightforward to remove, but foremost because the associated TTY's major device number is the same as used by the zs driver and the first driver to claim it will prevent the other one from using it. Either one DZ device or some SCC devices will be present in a given system but never both at a time, and therefore we want the major device number to be claimed by the first driver to actually successfully bind to its device and platform_driver_probe() is a way to fulfil that. An unfortunate consequence of the switch to a platform device is we now hand the console over from the bootconsole much later in the bootstrap. The firmware console handler appears good enough though to work so late and in particular with interrupts enabled. Conversely only starting the console port so late lets the reset code fully utilise our delay handlers, so switch from udelay() to fsleep() for transmitter draining so as to avoid busy-waiting for an excessive amount of time.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63883 In the Linux kernel, the following vulnerability has been resolved: serial: qcom_geni: fix kfifo underflow when flush precedes DMA completion IRQ When uart_flush_buffer() runs before the DMA completion IRQ is delivered, the following race can occur (all steps serialized by uart_port_lock): 1. DMA starts: tx_remaining = N, kfifo contains N bytes 2. DMA completes in hardware; IRQ is pending but not yet delivered 3. uart_flush_buffer() acquires the port lock and calls kfifo_reset(), making kfifo_len() = 0 while tx_remaining remains N 4. uart_flush_buffer() releases the port lock 5. DMA IRQ fires; handle_tx_dma() acquires the port lock and calls uart_xmit_advance(uport, tx_remaining) on an empty kfifo uart_xmit_advance() increments kfifo->out by tx_remaining. Since kfifo_reset() already set both in and out to 0, out wraps past in, causing kfifo_len() to return UART_XMIT_SIZE - tx_remaining. The next start_tx_dma() call then submits a DMA transfer of stale buffer data. Fix this by snapshotting kfifo_len() at the start of handle_tx_dma() and skipping uart_xmit_advance() when fifo_len < tx_remaining, which indicates the kfifo was reset by a preceding flush.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63884 In the Linux kernel, the following vulnerability has been resolved: drm/i915: Fix potential UAF in TTM object purge TLDR: The bo->ttm object might be changed by calling ttm_bo_validate(), move casting it to an i915_tt object later to actually get the right pointer. A user reported hitting the following bug under heavy use on DG2: [26620.095550] Oops: general protection fault, probably for non-canonical address 0xa56b6b6b6b6b6b8b: 0000 1 SMP NOPTI [26620.095556] CPU: 2 UID: 0 PID: 631 Comm: Xorg Not tainted 6.18.8 #1 PREEMPT(lazy) [26620.095558] Hardware name: ASRock B850M Steel Legend WiFi/B850M Steel Legend WiFi, BIOS 3.50 09/18/2025 [26620.095559] RIP: 0010:i915_ttm_purge+0x84/0x100 [i915] [26620.095604] Code: 00 00 00 48 8d 54 24 10 48 89 e6 48 89 fb e8 83 aa ae ff 85 c0 75 6f 48 83 bb a8 01 00 00 00 74 2c 48 8b 45 78 48 85 c0 74 23 <48> 8b 78 20 48 c7 c2 ff ff ff ff 31 f6 e8 7a 73 e3 e0 48 8b 7d 78 [26620.095605] RSP: 0018:ffffc90005fd7430 EFLAGS: 00010282 [26620.095607] RAX: a56b6b6b6b6b6b6b RBX: ffff8881f46c3dc0 RCX: 0000000000000000 [26620.095608] RDX: 0000000000000000 RSI: 0000000000000246 RDI: 00000000ffffffff [26620.095609] RBP: ffff888289610f00 R08: 0000000000000001 R09: ffff88823b022000 [26620.095609] R10: ffff888103029b28 R11: ffff8881fc7f3800 R12: ffff88810b6150d0 [26620.095609] R13: ffff888289610f00 R14: 0000000000000000 R15: ffff8881f46c3dc0 [26620.095610] FS: 00007f1004d86900(0000) GS:ffff88901c858000(0000) knlGS:0000000000000000 [26620.095611] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [26620.095611] CR2: 00007f0fdf489000 CR3: 000000035b0c1000 CR4: 0000000000750ef0 [26620.095612] PKRU: 55555554 [26620.095612] Call Trace: [26620.095615] <TASK> [26620.095615] i915_ttm_move+0x2b9/0x420 [i915] [26620.095642] ? ttm_tt_init+0x65/0x80 [ttm] [26620.095644] ? i915_ttm_tt_create+0xc6/0x150 [i915] [26620.095667] ttm_bo_handle_move_mem+0xb6/0x160 [ttm] [26620.095669] ttm_bo_evict+0x100/0x150 [ttm] [26620.095671] ? preempt_count_add+0x64/0xa0 [26620.095673] ? _raw_spin_lock+0xe/0x30 [26620.095675] ? _raw_spin_unlock+0xd/0x30 [26620.095675] ? i915_gem_object_evictable+0xb7/0xd0 [i915] [26620.095704] ttm_bo_evict_cb+0x6e/0xd0 [ttm] [26620.095705] ttm_lru_walk_for_evict+0xa6/0x200 [ttm] [26620.095708] ttm_bo_alloc_resource+0x185/0x4f0 [ttm] [26620.095709] ? init_object+0x62/0xd0 [26620.095712] ttm_bo_validate+0x7a/0x180 [ttm] [26620.095713] ? _raw_spin_unlock_irqrestore+0x16/0x30 [26620.095714] __i915_ttm_get_pages+0xb0/0x170 [i915] [26620.095737] i915_ttm_get_pages+0x9f/0x150 [i915] [26620.095759] ? i915_gem_do_execbuffer+0xedc/0x2b40 [i915] [26620.095786] ? alloc_debug_processing+0xd0/0x100 [26620.095787] ? _raw_spin_unlock_irqrestore+0x16/0x30 [26620.095788] ? i915_vma_instance+0xa0/0x4e0 [i915] [26620.095822] __i915_gem_object_get_pages+0x2f/0x40 [i915] [26620.095848] i915_vma_pin_ww+0x706/0x980 [i915] [26620.095875] ? i915_gem_do_execbuffer+0xedc/0x2b40 [i915] [26620.095904] eb_validate_vmas+0x170/0xa00 [i915] [26620.095930] i915_gem_do_execbuffer+0x1201/0x2b40 [i915] [26620.095953] ? alloc_debug_processing+0xd0/0x100 [26620.095954] ? _raw_spin_unlock_irqrestore+0x16/0x30 [26620.095955] ? i915_gem_execbuffer2_ioctl+0xc9/0x240 [i915] [26620.095977] ? __wake_up_sync_key+0x32/0x50 [26620.095979] ? i915_gem_execbuffer2_ioctl+0xc9/0x240 [i915] [26620.096001] ? __slab_alloc.isra.0+0x67/0xc0 [26620.096003] i915_gem_execbuffer2_ioctl+0x11a/0x240 [i915] Results from decode_stacktrace.sh pointed to dereference of a file pointer field of a i915 TTM page vector container associated with an object being purged on eviction. That path is taken when the object is marked as no longer needed. Code analysis revealed a possibility of the i915 TTM page vector container being replaced with a new instance inside a function that purges content of the object, should it be still busy. That function is called, indirectly via a more general function that changes the object's placement and caching policy, ---truncated---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63886 In the Linux kernel, the following vulnerability has been resolved: scsi: target: iscsi: Validate CHAP_R length before base64 decode chap_server_compute_hash() allocates client_digest as kzalloc(chap->digest_size) and then, for BASE64-encoded responses, passes chap_r directly to chap_base64_decode() without checking whether the input length could produce more than digest_size bytes of output. chap_base64_decode() writes to the destination unconditionally as long as there is input to consume. With MAX_RESPONSE_LENGTH set to 128 and the "0b" prefix stripped by extract_param(), up to 127 base64 characters can reach the decoder. 127 characters decode to 95 bytes. For SHA-256 (digest_size=32) this overflows client_digest by 63 bytes; for MD5 (digest_size=16) the overflow is 79 bytes. The length check at line 344 fires after the write has already happened. The HEX branch in the same switch statement already validates the length up front. Apply the same approach to the BASE64 branch: strip trailing base64 padding characters, then reject any input whose data length exceeds DIV_ROUND_UP(digest_size * 4, 3) before calling the decoder. Stripping trailing '=' before the comparison handles both padded and unpadded encodings. chap_base64_decode() already returns early on '=', so the full original string is still passed to the decoder unchanged. The mutual CHAP path decodes CHAP_C into initiatorchg_binhex, which is kzalloc(CHAP_CHALLENGE_STR_LEN). extract_param() caps initiatorchg at CHAP_CHALLENGE_STR_LEN characters, so at most CHAP_CHALLENGE_STR_LEN-1 base64 characters reach the decoder. The maximum decoded size, DIV_ROUND_UP((CHAP_CHALLENGE_STR_LEN-1) * 3, 4), is less than CHAP_CHALLENGE_STR_LEN, so no overflow is possible there. A comment is added at the call site to document this.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63887 In the Linux kernel, the following vulnerability has been resolved: scsi: target: iscsi: Bound iscsi_encode_text_output() appends to rsp_buf iscsi_encode_text_output() concatenates "key=value\0" records into login->rsp_buf, an 8192-byte kzalloc(MAX_KEY_VALUE_PAIRS) buffer allocated in iscsit_alloc_login_setup_buffer(). The three sprintf() call sites in this function (lines 1398, 1411, 1424 in v7.1-rc2) never check the remaining buffer capacity: *length += sprintf(output_buf, "%s=%s", er->key, er->value); *length += 1; output_buf = textbuf + *length; The 8192-byte ceiling at iscsi_target_check_login_request() bounds the *input* Login PDU payload, but a single PDU can carry up to 2048 minimal four-byte "a=b\0" pairs, each unknown key expanding to a 16-byte "a=NotUnderstood\0" output record via iscsi_add_notunderstood_response(). 2048 * 16 = 32 KiB of output into an 8 KiB buffer, producing a ~24 KiB heap overrun in the kmalloc-8k slab. The fix introduces a static iscsi_encode_text_record() helper that uses snprintf() with a per-call bounds check against the remaining buffer, and threads a u32 textbuf_size parameter through iscsi_encode_text_output(). Both call sites in iscsi_target_handle_csg_zero() (PHASE_SECURITY) and iscsi_target_handle_csg_one() (PHASE_OPERATIONAL) pass MAX_KEY_VALUE_PAIRS. On overflow the encoder logs the condition, calls iscsi_release_extra_responses() to drop queued records, and returns -1; both caller sites now emit ISCSI_STATUS_CLS_INITIATOR_ERR / ISCSI_LOGIN_STATUS_INIT_ERR via iscsit_tx_login_rsp() before returning, so the initiator sees an explicit failed-login response rather than a silent connection drop. (Prior to this patch only the PHASE_OPERATIONAL caller did that; the PHASE_SECURITY caller is converted to the same shape.)

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63888 In the Linux kernel, the following vulnerability has been resolved: scsi: target: iscsi: Fix CRC overread and double-free in iscsit_handle_text_cmd() Two latent bugs in the Text-phase handler, both present since the original LIO integration in commit e48354ce078c ("iscsi-target: Add iSCSI fabric support for target v4.1"): 1) DataDigest CRC buffer overread (4 bytes past text_in). text_in is kzalloc()'d at ALIGN(payload_length, 4). rx_size is then incremented by ISCSI_CRC_LEN to make room for the received DataDigest in the iovec, but the same (now-bumped) rx_size is passed as the buffer length to iscsit_crc_buf(): if (conn->conn_ops->DataDigest) { ... rx_size += ISCSI_CRC_LEN; } ... if (conn->conn_ops->DataDigest) { data_crc = iscsit_crc_buf(text_in, rx_size, 0, NULL); iscsit_crc_buf() walks rx_size bytes of text_in with crc32c(), so when DataDigest is negotiated it reads 4 bytes past the end of the text_in allocation. KASAN reproduces this directly on the unpatched mainline tree as slab-out-of-bounds in crc32c() called from the Text PDU path. The OOB bytes feed crc32c() and are then compared against the initiator-supplied checksum, so the value does not flow back to the attacker, but the kernel does read past the buffer on every Text PDU with DataDigest=CRC32C. Fix by passing the actual padded payload length (ALIGN(payload_length, 4)) that was used for the kzalloc(). 2) Stale cmd->text_in_ptr re-free (double-free) on ERL>0 bad DataDigest drop. On DataDigest mismatch with ErrorRecoveryLevel > 0 the handler silently drops the PDU and lets the initiator plug the CmdSN gap: kfree(text_in); return 0; cmd->text_in_ptr still points at the freed buffer. The next Text Request on the same ITT re-enters iscsit_setup_text_cmd(), which unconditionally does kfree(cmd->text_in_ptr); cmd->text_in_ptr = NULL; freeing the same pointer a second time. Session teardown via iscsit_release_cmd() has the same shape and hits the same double-free if the connection is dropped before a second Text Request arrives. On an unmodified mainline tree the bug-1 CRC overread fires first on the initial valid Text Request and perturbs the subsequent state, so #4 was isolated by building a kernel with only the bug-1 hunk of this patch applied plus temporary printk() observability around the three relevant kfree() sites. The observability prints are not part of this patch. On that build, a three-PDU Text Request sequence after login produces two back-to-back splats: BUG: KASAN: double-free in iscsit_setup_text_cmd+0x?? BUG: KASAN: double-free in iscsit_release_cmd+0x?? showing the same pointer freed in the ERL>0 drop path and again in iscsit_setup_text_cmd() (next Text Request on the same ITT) and once more in iscsit_release_cmd() (session teardown). On distro kernels with CONFIG_SLAB_FREELIST_HARDENED=y (default) the double-free becomes a remote kernel BUG(); on non-hardened kernels it corrupts the slab freelist. Fix by clearing cmd->text_in_ptr after the kfree() in the ERL>0 drop path. With both hunks applied #4 is directly observable on the stock tree without observability printks; fixing bug-1 alone would mask #4 less, not more, so the hunks are submitted together. Both fixes are one-liners. The Text PDU state machine is unchanged and the wire protocol is unaffected.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63889 In the Linux kernel, the following vulnerability has been resolved: scsi: scsi_transport_fc: Widen FPIN pname walker counter to u32 An adjacent Fibre Channel fabric actor that can deliver an FPIN ELS frame to an lpfc or qla2xxx Linux initiator can trigger a non-return in the generic FC transport. This is not a local userspace or IP network path; the attacker must be able to inject fabric traffic, for example as a compromised switch or fabric controller, or as a same-zone N_Port on a fabric that permits source spoofing. The Link-Integrity and Peer-Congestion FPIN walkers used a u8 loop counter against the 32-bit on-wire pname_count field, and did not bound pname_count by the descriptor body already validated by the TLV walker. A pname_count of 256 therefore wraps the counter and keeps the loop condition true indefinitely. Factor the shared pname_list[] walk into one helper, widen the counter to u32, and clamp pname_count against the entries that fit in the descriptor body before iterating.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63891 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: property: Cap recursion depth in __tb_property_parse_dir() A DIRECTORY entry's value field is used as the dir_offset for a recursive call into __tb_property_parse_dir() with no depth counter. A crafted peer that chains DIRECTORY entries into a back-reference loop drives the parser until the kernel stack is exhausted and the guard page fires. Any untrusted XDomain peer (cable, dock, in-line inspector, adjacent host) that reaches the PROPERTIES_REQUEST control-plane exchange can trigger this without authentication. Thread a depth counter through tb_property_parse() and __tb_property_parse_dir(), and reject blocks that exceed TB_PROPERTY_MAX_DEPTH = 8. That is comfortably larger than any observed legitimate XDomain layout. Operators who do not need XDomain host-to-host discovery can disable the path entirely with thunderbolt.xdomain=0 on the kernel command line.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63892 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: property: Reject dir_len < 4 to prevent size_t underflow On the non-root path, __tb_property_parse_dir() takes dir_len from entry->length (u16 widened to size_t). Two distinct OOB conditions follow when entry->length < 4: 1. The non-root path begins with kmemdup(&block[dir_offset], sizeof(*dir->uuid), ...) which always reads 4 dwords from dir_offset. tb_property_entry_valid() only enforces dir_offset + entry->length <= block_len, so a crafted entry with dir_offset close to the end of the property block and entry->length in 0..3 passes that gate but lets the UUID copy run off the block (e.g. dir_offset = 497, dir_len = 3 in a 500-dword block reads block[497..501]). 2. After the kmemdup, content_len = dir_len - 4 underflows size_t to ~SIZE_MAX, nentries becomes SIZE_MAX / 4, and the entry walk runs OOB on each iteration until an entry fails validation or the kernel oopses on an unmapped page. Reject dir_len < 4 on the non-root path *before* the UUID kmemdup, which closes both holes. Also move INIT_LIST_HEAD(&dir->properties) up to immediately after the dir allocation so the new error-return path (and the existing uuid-alloc failure path) calling tb_property_free_dir() sees a walkable list rather than the zero-initialized NULL next/prev that list_for_each_entry_safe() would oops on.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63893 In the Linux kernel, the following vulnerability has been resolved: thunderbolt: property: Reject u32 wrap in tb_property_entry_valid() entry->value is u32 and entry->length is u16; the sum is performed in u32 and wraps. A malicious XDomain peer can pick value = 0xffffff00, length = 0x100 so the sum 0x100000000 wraps to 0 and passes the > block_len check. tb_property_parse() then passes entry->value to parse_dwdata() as a dword offset into the property block, reading attacker-directed memory far past the allocation. For TEXT-typed entries with the "deviceid" or "vendorid" keys this lands in xd->device_name / xd->vendor_name and is readable back via the per-XDomain device_name / vendor_name sysfs attributes; the leak is NUL-bounded (kstrdup() stops at the first zero byte) and untargeted (the attacker picks a delta, not an absolute address). DATA-typed entries are parsed into property->value.data but not generically surfaced to userspace. Use check_add_overflow() so a wrapped sum is rejected.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63895 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: f_fs: copy only received bytes on short ep0 read ffs_ep0_read() allocates its control-OUT data buffer with kmalloc() (not kzalloc) at the Length value from the Setup packet, then copies that full len to userspace regardless of how many bytes were actually received: data = kmalloc(len, GFP_KERNEL); ... ret = __ffs_ep0_queue_wait(ffs, data, len); if ((ret > 0) && (copy_to_user(buf, data, len))) ret = -EFAULT; __ffs_ep0_queue_wait() returns req->actual, which on a short control OUT transfer is strictly less than len. The copy_to_user() call still copies len bytes, so on a short OUT the last (len - ret) bytes of the kmalloc() buffer -- uninitialised slab residue -- are delivered to the FunctionFS daemon. Short ep0 OUT completions are specified USB control-transfer behavior and are produced by in-tree UDCs: * dwc2 continues on req->actual < req->length for ep0 DATA OUT (short-not-ok is the only ep0-OUT stall path). * aspeed_udc ends ep0 OUT on rx_len < ep->ep.maxpacket. * renesas_usbf logs "ep0 short packet" and completes the request. * dwc3 stalls on short IN but not on short OUT. A short ep0 OUT is therefore not evidence of a broken UDC; it is a normal condition f_fs has to cope with. The sibling gadgetfs implementation in drivers/usb/gadget/legacy/inode.c already does this correctly via min(len, dev->req->actual) before copy_to_user(). This patch brings f_fs.c to the same safe pattern rather than trimming at a defensive layer. The bug is reached from the FunctionFS device node, which in real deployments is owned by the privileged gadget daemon (adbd, UMS, composite gadget services, etc.); it is not reachable from unprivileged userspace. Linux host stacks normally reject short-wLength control OUTs before they reach the gadget, so reproducing this required a build that bypasses that host-side check. With the bypass in place, a 1-byte payload on a 64-byte Setup produces 63 bytes of non-canary slab residue in the daemon's read buffer. Fix by copying only ret (actually received) bytes to userspace.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63896 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: composite: fix integer underflow in WebUSB GET_URL handling The WebUSB GET_URL handler in composite_setup() narrows landing_page_length to fit the host-supplied wLength using landing_page_length = w_length - WEBUSB_URL_DESCRIPTOR_HEADER_LENGTH + landing_page_offset; If wLength is smaller than WEBUSB_URL_DESCRIPTOR_HEADER_LENGTH the unsigned subtraction wraps, and the subsequent memcpy(url_descriptor->URL, cdev->landing_page + landing_page_offset, landing_page_length - landing_page_offset); ends up copying close to UINT_MAX bytes from cdev->landing_page into cdev->req->buf. KASAN reports a slab-out-of-bounds in composite_setup on the kmalloc-2k gadget_info allocation, and FORTIFY_SOURCE traps the memcpy as a 4294967293-byte field-spanning write into url_descriptor->URL (size 252). A USB host can reach this from a single SETUP packet against any gadget that has webusb/use=1 and a landingPage configured. Handle the small-wLength case before the math: when the host requested fewer bytes than the URL descriptor header, only the header is meaningful and no URL bytes need to be copied. Setting landing_page_length to landing_page_offset makes the existing memcpy a no-op and leaves the descriptor returned to the host unchanged for all larger wLength values.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63905 In the Linux kernel, the following vulnerability has been resolved: usbip: vudc: Fix use after free bug in vudc_remove due to race condition This patch follows up Zheng Wang's 2023 report of a use-after-free in vudc_remove(). The original thread stalled on Shuah Khan's request for runtime testing of the unplug/unbind path. This patch supplies that testing and keeps Zheng's original fix shape. In vudc_probe(), v_init_timer() binds udc->tr_timer.timer to v_timer(). usbip_sockfd_store() starts the timer via v_start_timer()/v_kick_timer(). vudc_remove() can then free the containing struct vudc while the timer is still pending or executing. KASAN confirms the race on an unpatched x86_64 QEMU guest with CONFIG_KASAN=y, CONFIG_USBIP_VUDC=y, CONFIG_USB_ZERO=y, and a tight loop that repeatedly writes a socket fd to usbip_sockfd, closes the socket pair, and unbinds/rebinds usbip-vudc.0: BUG: KASAN: slab-use-after-free in __run_timer_base.part.0+0x8ba/0x8e0 Write of size 8 at addr ffff888001b80740 by task trigger_and_unb/239 Allocated by task 239: vudc_probe+0x4d/0xaa0 Freed by task 239: kfree+0x18f/0x520 device_release_driver_internal+0x388/0x540 unbind_store+0xd9/0x100 This lands in the timer core rather than v_timer() itself because the embedded timer_list is being walked after its containing struct vudc has already been freed. The underlying lifetime bug is the same one Zheng reported. With v_stop_timer() called from vudc_remove() and the timer deleted synchronously, the same harness completed 5000 bind/unbind iterations with no KASAN report.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63906 In the Linux kernel, the following vulnerability has been resolved: usb: musb: omap2430: Fix use-after-free in omap2430_probe() In omap2430_probe(), of_node_put(np) is called prematurely before the last access to np, leading to a use-after-free if the node's reference count drops to zero. Move the of_node_put() calls after the last use of np in both the success and error paths.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63908 In the Linux kernel, the following vulnerability has been resolved: Input: atmel_mxt_ts - fix boundary check in mxt_prepare_cfg_mem When a configuration file provides an object size that is larger than the driver's known mxt_obj_size(object), the driver intends to discard the extra bytes. The loop iterates using for (i = 0; i < size; i++). Inside the loop, the condition to skip processing extra bytes is: if (i > mxt_obj_size(object)) continue; Since i is a 0-based index, the valid indices for the object are 0 through mxt_obj_size(object) - 1. When i == mxt_obj_size(object), the condition evaluates to false, and the code processes the byte instead of discarding it. This causes the code to calculate byte_offset = reg + i - cfg->start_ofs and writes the byte there, overwriting exactly one byte of the adjacent instance or object. Update the boundary check to skip extra bytes correctly by using >=.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63909 In the Linux kernel, the following vulnerability has been resolved: ksmbd: OOB read regression in smb_check_perm_dacl() ACE-walk loops Commit d07b26f39246 ("ksmbd: require minimum ACE size in smb_check_perm_dacl()") introduced a transposed bounds check: if (offsetof(struct smb_ace, sid) + aces_size < CIFS_SID_BASE_SIZE) Since offsetof(..sid) is 8 and CIFS_SID_BASE_SIZE is 8, this evaluates to `aces_size < 0`. Because `aces_size` is always non-negative, this check becomes dead code and never breaks the loop. Worse, that commit removed the old 4-byte guard, meaning the loop now reads `ace->size` (offset 2) even when `aces_size` is 0-3 bytes. This re-opens a 2-byte heap out-of-bounds (OOB) read past the pntsd allocation during subsequent SMB2_CREATE operations. Fix this by properly transposing the comparison to require at least 16 bytes (8-byte offset + 8-byte SID base), matching the correct form used in smb_inherit_dacl().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63913 In the Linux kernel, the following vulnerability has been resolved: netfilter: conntrack: tcp: do not force CLOSE on invalid-seq RST without direction check An unintended behavior in the TCP conntrack state machine allows a connection to be forced into the CLOSE state using an RST packet with an invalid sequence number. Specifically, after a SYN packet is observed, an RST with an invalid SEQ can transition the conntrack entry to TCP_CONNTRACK_CLOSE, regardless of whether the RST corresponds to the expected reply direction. The relevant code path assumes the RST is a response to an outgoing SYN, but does not validate packet direction or ensure that a matching SYN was actually sent in the opposite direction. As a result, a crafted packet sequence consisting of a SYN followed by an invalid-sequence RST can prematurely terminate an active NAT entry. This makes connection teardown easier than intended. So, tighten the state transition logic to ensure that RST-triggered CLOSE transitions only occur when the RST is a valid response to a previously observed SYN in the correct direction.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63915 In the Linux kernel, the following vulnerability has been resolved: nfc: hci: fix out-of-bounds read in HCP header parsing Both nfc_hci_recv_from_llc() and nci_hci_data_received_cb() read packet->header from skb->data at function entry without first checking that the buffer holds at least one byte. A malicious NFC peer can send a 0-byte HCP frame that passes through the SHDLC layer and reaches these functions, causing an out-of-bounds heap read of packet->header. The same 0-byte frame, if queued as a non-final fragment, also causes the reassembly loop to underflow msg_len to UINT_MAX, triggering skb_over_panic() when the reassembled skb is written. Fix this by adding a pskb_may_pull() check at the entry of each function before packet->header is first accessed. The existing pskb_may_pull() checks before the reassembled hcp_skb is cast to struct hcp_packet remain in place to guard the 2-byte HCP message header.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63917 In the Linux kernel, the following vulnerability has been resolved: ip6: vti: Use ip6_tnl.net in vti6_changelink(). ip netns add ns1 ip netns add ns2 ip -n ns1 link add vti6_test type vti6 remote ::1 local ::2 key 7 ip -n ns1 link set vti6_test netns ns2 ip -n ns2 link set vti6_test type vti6 remote ::3 local ::4 key 9 ip netns del ns2 ip netns del ns1 [ 132.495484] ------------[ cut here ]------------ [ 132.497609] kernel BUG at net/core/dev.c:12376! Commit 61220ab34948 ("vti6: Enable namespace changing") dropped NETIF_F_NETNS_LOCAL from vti6 devices. A vti6 tunnel can then move through IFLA_NET_NS_FD. After the move dev_net(dev) points at the new netns while t->net stays at the creation netns. vti6_changelink() and vti6_update() still use dev_net(dev) and dev_net(t->dev). They unlink from one per netns hash and relink into another. The creation netns is left with a stale entry. cleanup_net() of that netns later walks freed memory. Reachable from an unprivileged user namespace (unshare --user --map-root-user --net). Cross tenant scope on container hosts.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63919 In the Linux kernel, the following vulnerability has been resolved: xfrm: input: hold netns during deferred transport reinjection Transport-mode reinjection stores a struct net pointer in skb->cb and uses it later from xfrm_trans_reinject(). That pointer must stay valid until the deferred callback runs. Take a netns reference when queueing deferred reinjection work and drop it after the callback completes. Use maybe_get_net() so the queueing path does not revive a namespace that is already being torn down. This keeps the existing workqueue design and fixes the netns lifetime handling in one place for all users of xfrm_trans_queue_net().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63921 In the Linux kernel, the following vulnerability has been resolved: ip6: vti: Use ip6_tnl.net in vti6_siocdevprivate(). After patch 1/2 in this series, vti6_update() unlinks and relinks the tunnel through t->net. vti6_siocdevprivate() still uses dev_net(dev) for the collision lookup. For a tunnel moved through IFLA_NET_NS_FD, dev_net(dev) is the new netns, not t->net. SIOCCHGTUNNEL on a migrated tunnel then runs: net = dev_net(dev) /* migrated netns */ t = vti6_locate(net, &p1, false) /* misses target in t->net */ ... t = netdev_priv(dev) vti6_update(t, &p1, false) /* mutates t->net's hash */ A caller in the migrated netns picks params that match a tunnel in the creation netns. The lookup in dev_net(dev) finds nothing. vti6_update() prepends the migrated tunnel at the head of the creation netns hash bucket for those params. Later lookups in the creation netns resolve to the migrated device. xfrm receive delivers the matched packets through a device the caller controls. Reachable from an unprivileged user namespace (unshare --user --map-root-user --net). Cross tenant scope on container hosts. Switch the SIOCCHGTUNNEL path on a non fallback device to use t->net for the lookup. The lookup now matches the netns vti6_update() operates on. Also add ns_capable(self->net->user_ns, CAP_NET_ADMIN) before the lookup. The check at the top of the case is against dev_net(dev)->user_ns, which after migration is the attacker's netns. A caller there can pick params absent from self->net, the lookup returns NULL, t becomes self, and vti6_update() inserts the device into the creation netns hash. The new check requires CAP_NET_ADMIN in the creation netns user_ns too. SIOCADDTUNNEL and SIOCCHGTUNNEL on the fallback device keep dev_net(dev), which equals init_net there.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63925 In the Linux kernel, the following vulnerability has been resolved: macsec: fix replay protection at XPN lower-PN wrap In macsec_post_decrypt(), when pn is U32_MAX, pn + 1 overflows u32 to 0 and the first branch never fires. If next_pn_halves.lower is also in the upper half, pn_same_half(pn, lower) is true and the XPN else-if does not fire either, leaving next_pn_halves unchanged. An attacker that captures the legitimate frame carrying pn == 0xFFFFFFFF on an XPN association can then replay it indefinitely, since lowest_pn never rises above the captured pn and macsec_decrypt() reconstructs the same IV. Extend the XPN else-if to also fire when pn + 1 wraps to 0, so receipt of pn == U32_MAX advances next_pn_halves to (upper + 1, 0).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63927 In the Linux kernel, the following vulnerability has been resolved: usb: dwc2: Fix use after free in debug code We're not allowed to dereference "urb" after calling usb_hcd_giveback_urb() so save the urb->status ahead of time.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63930 In the Linux kernel, the following vulnerability has been resolved: iio: buffer: hw-consumer: fix use-after-free in error path In the err_put_buffers cleanup path of iio_hw_consumer_alloc(), the code was using list_for_each_entry() to iterate through buffers while calling iio_buffer_put() which can free the current buffer if refcount drops to 0. The list_for_each_entry() loop macro then evaluates buf->head.next to continue iteration, accessing the freed buffer. Fix this by using list_for_each_entry_safe().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63931 In the Linux kernel, the following vulnerability has been resolved: iio: chemical: scd30: fix division by zero in write_raw Add a zero check for val2 before using it as a divisor when setting the sampling frequency. A user writing a zero fractional part to the sampling_frequency sysfs attribute triggers a division by zero in the kernel.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63933 In the Linux kernel, the following vulnerability has been resolved: iio: gyro: adis16260: fix division by zero in write_raw Add a validation check for the sampling frequency value before using it as a divisor. A user writing zero to the sampling_frequency sysfs attribute triggers a division by zero in the kernel.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63934 In the Linux kernel, the following vulnerability has been resolved: iio: gyro: itg3200: fix i2c read into the wrong stack location itg3200_read_all_channels() takes `__be16 *buf' as a parameter and fills the i2c_msg destination as `(char *)&buf'. Since `buf' is the parameter (a pointer), `&buf' is the address of the local pointer slot on the stack of itg3200_read_all_channels(), not the address of the caller's scan buffer. The (char *) cast hides the type mismatch. i2c_transfer() therefore writes ITG3200_SCAN_ELEMENTS * sizeof(s16) = 8 bytes into the parameter's stack slot, which is discarded when the function returns. The caller's scan buffer in itg3200_trigger_handler() is never written to, so iio_push_to_buffers_with_timestamp() pushes uninitialised stack contents to userspace via /dev/iio:deviceX every scan -- both a functional bug (no actual gyroscope or temperature data is delivered through the triggered buffer) and an information leak. The non-buffered read_raw() path is unaffected: it goes through itg3200_read_reg_s16() which uses `&out' on a local s16 value, where that is correct. Drop the spurious `&' so the i2c read writes into the caller's buffer.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63943 In the Linux kernel, the following vulnerability has been resolved: Input: xpad - fix out-of-bounds access for Share button xpadone_process_packet() receives len directly from urb->actual_length and uses it to index the share-button byte at data[len - 18] or data[len - 26]. Since both len and data[0] are under the device's control, a broken controller can send a GIP_CMD_INPUT packet with actual_length < 18 (e.g. 5 bytes) and reach this code path, causing accesses beyond the actual array. Fix this by calculating the offset and checking bounds against the packet length.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63944 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: hci_sync: fix UAF in hci_le_create_cis_sync hci_le_create_cis_sync() dereferences conn->conn_timeout after releasing both rcu_read_lock() and hci_dev_lock(hdev). The conn pointer was obtained from an RCU-protected iteration over hdev->conn_hash.list and is not valid once these locks are dropped. A concurrent disconnect can free the hci_conn between the unlock and the dereference, causing a use-after-free read. The cancellation mechanism in hci_conn_del() cannot prevent this because hci_le_create_cis_pending() queues hci_create_cis_sync with data=NULL: hci_cmd_sync_queue(hdev, hci_create_cis_sync, NULL, NULL); While hci_conn_del() dequeues with data=conn: hci_cmd_sync_dequeue(hdev, NULL, conn, NULL); Since NULL != conn, the lookup in _hci_cmd_sync_lookup_entry() never matches, and the pending work item is not cancelled. Fix this by saving conn->conn_timeout into a local variable while the locks are still held, so the stale conn pointer is never dereferenced after unlock. This is the same class of bug as the one fixed by commit 035c25007c9e ("Bluetooth: hci_sync: Fix UAF on le_read_features_complete") which addressed the identical pattern in a different function. This vulnerability was identified using 0sec.ai, an open-source automated security auditing platform (https://github.com/0sec-labs).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63945 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: ISO: serialize iso_sock_clear_timer with socket lock iso_sock_close() calls iso_sock_clear_timer() before acquiring lock_sock(sk). iso_sock_clear_timer() reads iso_pi(sk)->conn twice without the socket lock held: if (!iso_pi(sk)->conn) return; cancel_delayed_work(&iso_pi(sk)->conn->timeout_work); Concurrently, iso_conn_del() executes under lock_sock(sk) and calls iso_chan_del(), which sets iso_pi(sk)->conn to NULL and may result in the final reference to the connection being dropped: CPU0 CPU1 ---- ---- iso_sock_clear_timer() if (conn != NULL) ... lock_sock(sk) iso_chan_del() iso_pi(sk)->conn = NULL cancel_delayed_work(conn) /* NULL deref or UAF */ iso_pi(sk)->conn is not stable across the unlock window, causing a NULL pointer dereference or use-after-free. Serialize iso_sock_clear_timer() with the socket lock by moving it inside lock_sock()/release_sock(), matching the pattern used in iso_conn_del() and all other call sites.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63946 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: ISO: fix UAF in iso_recv_frame iso_recv_frame reads conn->sk under iso_conn_lock but releases the lock before using sk, with no reference held. A concurrent iso_sock_kill() can free sk in that window, causing use-after-free on sk->sk_state and sock_queue_rcv_skb(). Fix by replacing the bare pointer read with iso_sock_hold(conn), which calls sock_hold() while the spinlock is held, atomically elevating the refcount before the lock drops. Add a drop_put label so sock_put() is called on all exit paths where the hold succeeded.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63949 In the Linux kernel, the following vulnerability has been resolved: auxdisplay: line-display: fix OOB read on zero-length message_store() linedisp_display() unconditionally reads msg[count - 1] before checking whether count is zero, so a write of zero bytes to the message sysfs attribute hits msg[-1]: write(fd, "", 0); -> message_store(..., buf, count=0) -> linedisp_display(linedisp, buf, count=0) -> msg[count - 1] == '\n' ; OOB read The kernfs write buffer for that store is a 1-byte allocation (kernfs_fop_write_iter() does kmalloc(len + 1) with len == 0), so msg[-1] is a 1-byte read before the slab object. On a KASAN-enabled kernel this trips an out-of-bounds report and panics; on stock kernels it silently reads adjacent slab data and, if that byte happens to be '\n', the following count-- wraps ssize_t 0 to -1 and is then passed to kmemdup_nul(). linedisp_display() is reached from the message_store() sysfs callback (drivers/auxdisplay/line-display.c message attribute, mode 0644) and from the in-tree initial-message setup with count == -1, so the OOB path is only userspace-triggerable via zero-byte writes; vfs_write() does not short-circuit on count == 0 and kernfs_fop_write_iter() dispatches the store callback regardless. Guard the trailing-newline trim with a count check. The existing if (!count) block then takes the clear-display path unchanged. Affects every auxdisplay driver that registers via linedisp_register() / linedisp_attach(): ht16k33, max6959, img-ascii-lcd, seg-led-gpio.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63952 In the Linux kernel, the following vulnerability has been resolved: memfd: deny writeable mappings when implying SEAL_WRITE When SEAL_EXEC is added, SEAL_WRITE is implied to make W^X. But the implied seal is set after the check that makes sure the memfd can not have any writable mappings. This means one can use SEAL_EXEC to apply SEAL_WRITE while having writeable mappings. This breaks the contract that SEAL_WRITE provides and can be used by an attacker to pass a memfd that appears to be write sealed but can still be modified arbitrarily. Fix this by adding the implied seals before the call for mapping_deny_writable() is done.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63954 In the Linux kernel, the following vulnerability has been resolved: hpfs: fix a crash if hpfs_map_dnode_bitmap fails If hpfs_map_dnode_bitmap fails, the code would call hpfs_brelse4 on uninitialized quad buffer head, causing a crash.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63960 In the Linux kernel, the following vulnerability has been resolved: usb: typec: wcove: don't write past struct pd_message in wcove_read_rx_buffer() wcove_read_rx_buffer() copies the PD RX FIFO into the caller's struct pd_message with for (i = 0; i < USBC_RXINFO_RXBYTES(info); i++) regmap_read(wcove->regmap, USBC_RX_DATA + i, msg + i); which has two problems: USBC_RXINFO_RXBYTES() is a 5-bit field (max 31) while struct pd_message is 30 bytes (__le16 header + __le32 payload[PD_MAX_PAYLOAD], packed). The byte count latched in RXINFO is the number of bytes the port partner put on the wire, so a malicious partner that transmits a 31-byte frame can drive the loop one byte past the destination if the WCOVE BMC receiver does not enforce the PD object-count limit in hardware. The existing FIXME flagged this as unverified. Independently, regmap_read() takes an unsigned int * and stores a full unsigned int at the destination. Passing the byte pointer msg + i means each iteration writes four bytes; the high three are zero (val_bits is 8) and are normally overwritten by the next iteration, but the final iteration's high bytes are not. With RXBYTES == 30 the i == 29 iteration already writes three zero bytes past msg, which sits on the IRQ thread's stack in wcove_typec_irq(). Clamp the loop to sizeof(struct pd_message) and read each register into a local before storing only its low byte, so the copy can never exceed the destination regardless of what RXINFO reports.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63964 In the Linux kernel, the following vulnerability has been resolved: usb: typec: ucsi: ccg: reject firmware images without a ':' record header do_flash() locates the first .cyacd record with p = strnchr(fw->data, fw->size, ':'); while (p < eof) { s = strnchr(p + 1, eof - p - 1, ':'); ... } If the firmware image contains no ':' byte, strnchr() returns NULL. NULL compares less than the valid kernel pointer eof, so the loop body runs and strnchr() is called with p + 1 == (void *)1 and a length of roughly (unsigned long)eof, causing a wonderful crash. The not_signed_fw fallthrough earlier in do_flash() and the chip-state branches in ccg_fw_update_needed() allow an unsigned blob to reach this loop, so a root user who can place a crafted file under /lib/firmware and write the do_flash sysfs attribute can trigger the oops. Bail out with -EINVAL when the initial strnchr() returns NULL.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63967 In the Linux kernel, the following vulnerability has been resolved: iio: imu: st_lsm6dsx: fix stack leak in tagged FIFO buffer The tagged FIFO path declares iio_buff on the stack with __aligned(8) but no initializer, but there is a hole in the structure, which will then leak to userspace as ST_LSM6DSX_SAMPLE_SIZE bytes (6) will be copied, but the space between that and the timestamp are not initialized. Commit c14edb4d0bdc ("iio:imu:st_lsm6dsx Fix alignment and data leak issues") moved the untagged FIFO path to a kzalloc'd buffer in hw->scan, but for the tagged path it only added the alignment qualifier and not the initializer :( Fix this by just zero-initializing the structure on the stack.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63968 In the Linux kernel, the following vulnerability has been resolved: ipv6: fix possible infinite loop in fib6_select_path() Found while auditing the same pattern Sashiko reported in rt6_fill_node() [1]. Apply the same fix as commit f8d8ce1b515a ("ipv6: fix possible infinite loop in fib6_info_uses_dev()"). Writers holding tb6_lock can list_del_rcu(&first->fib6_siblings) without waiting for RCU readers; first->fib6_siblings.next then still points into the old ring and this softirq-side walker never reaches &first->fib6_siblings as its terminator. fib6_purge_rt() always WRITE_ONCE()s first->fib6_nsiblings to 0 before list_del_rcu(), so an inside-loop check is a reliable detach signal. [1] https://sashiko.dev/#/patchset/20260526020227.4857-1-jiayuan.chen%40linux.dev

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63969 In the Linux kernel, the following vulnerability has been resolved: ipv6: fix possible infinite loop in rt6_fill_node() Sashiko reported this issue [1]. Apply the same fix as commit f8d8ce1b515a ("ipv6: fix possible infinite loop in fib6_info_uses_dev()"). Writers holding tb6_lock can list_del_rcu(&rt->fib6_siblings) without waiting for RCU readers; rt->fib6_siblings.next then still points into the old ring and this softirq-side walker never reaches &rt->fib6_siblings, causing a CPU stall. fib6_del_route() always WRITE_ONCE()s rt->fib6_nsiblings to 0 before list_del_rcu(), so an inside-loop check is a reliable detach signal. [1] https://sashiko.dev/#/patchset/20260526020227.4857-1-jiayuan.chen%40linux.dev

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63970 In the Linux kernel, the following vulnerability has been resolved: vsock/virtio: bind uarg before filling zerocopy skb virtio_transport_send_pkt_info() allocates or reuses the zerocopy uarg before entering the send loop, but virtio_transport_alloc_skb() still fills the skb before it inherits that uarg. When fixed-buffer vectored zerocopy hits MAX_SKB_FRAGS, io_sg_from_iter() may partially attach managed frags and return -EMSGSIZE. The rollback path call kfree_skb() to free an skb that carries SKBFL_MANAGED_FRAG_REFS but no uarg, so skb_release_data() falls through to ordinary frag unref. Pass the uarg into virtio_transport_alloc_skb() and bind it immediately before virtio_transport_fill_skb(). This keeps control or no-payload skbs untouched while ensuring success and rollback share one lifetime rule.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63971 In the Linux kernel, the following vulnerability has been resolved: sctp: fix race between sctp_wait_for_connect and peeloff sctp_wait_for_connect() drops and re-acquires the socket lock while waiting for the association to reach ESTABLISHED state. During this window, another thread can peeloff the association to a new socket via getsockopt(SCTP_SOCKOPT_PEELOFF), changing asoc->base.sk. After re-acquiring the old socket lock, sctp_wait_for_connect() returns success without noticing the migration — the caller then accesses the association under the wrong lock in sctp_datamsg_from_user(). Add the same sk != asoc->base.sk check that sctp_wait_for_sndbuf() already has, returning an error if the association was migrated while we slept.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63974 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: hci_sync: Set HCI_CMD_DRAIN_WORKQUEUE during device close Since hci_dev_close_sync() can now be called during the reset path, we should also set HCI_CMD_DRAIN_WORKQUEUE. This avoids queuing timeouts while the hdev workqueue is being drained.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63980 In the Linux kernel, the following vulnerability has been resolved: net/handshake: Use spin_lock_bh for hn_lock nvmet_tcp_state_change(), a socket callback that runs in BH context, can reach handshake_req_cancel() via nvmet_tcp_schedule_release_queue() and tls_handshake_cancel(). handshake_req_cancel() acquires hn->hn_lock with plain spin_lock(). If a process-context thread on the same CPU holds hn->hn_lock when a softirq invokes the cancel path, the lock attempt deadlocks. This is the only caller that invokes tls_handshake_cancel() from BH context; every other consumer calls it from process context. Deferring the cancel to process context in the NVMe target is not straightforward: nvmet_tcp_schedule_release_queue() must call tls_handshake_cancel() atomically with its state transition to DISCONNECTING. If the cancel were deferred, the handshake completion callback could fire in the window before the cancel runs, observe the unexpected state, and return without dropping its kref on the queue. Reworking that interlock is considerably more invasive than hardening the handshake lock. Convert all hn->hn_lock acquisitions from spin_lock/spin_unlock to spin_lock_bh/spin_unlock_bh so the lock is never taken with softirqs enabled.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-63984 In the Linux kernel, the following vulnerability has been resolved: ipv6: rpl: fix hdrlen overflow in ipv6_rpl_srh_decompress() ipv6_rpl_srh_decompress() computes: outhdr->hdrlen = (((n + 1) * sizeof(struct in6_addr)) >> 3); hdrlen is __u8. For n >= 127 the result exceeds 255 and silently truncates. With n=127 (cmpri=15, cmpre=15, pad=0, hdrlen=16): (128 * 16) >> 3 = 256, truncated to 0 as __u8 The caller in ipv6_rpl_srh_rcv() then places the compressed header at buf + ((ohdr->hdrlen + 1) << 3). With hdrlen=0 this is buf + 8, but the decompressed region occupies buf[0..2055] (8-byte header plus 128 full addresses). The compressed header overlaps the decompressed data, and ipv6_rpl_srh_compress() writes into this overlap, corrupting the routing header of the forwarded packet. The existing guard at exthdrs.c:546 checks (n + 1) > 255, which prevents n+1 from overflowing unsigned char (the segments_left field), but does not prevent the computed hdrlen from overflowing __u8. n=127 passes because 128 <= 255, yet hdrlen=256 does not fit. Tighten the bound to (n + 1) > 127. This caps n at 126, giving hdrlen = (127 * 16) >> 3 = 254, which fits in __u8. The compressed header then lands at buf + ((254 + 1) << 3) = buf + 2040, exactly past the decompressed region (buf[0..2039]). No overlap. 127 segments is well beyond any realistic RPL deployment.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63991 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: 6lowpan: check skb_clone() return value in send_mcast_pkt() The skb_clone() function can return NULL if memory allocation fails. send_mcast_pkt() calls skb_clone() without checking the return value, which can lead to a NULL pointer dereference in send_pkt() when it dereferences skb->data. Add a NULL check after skb_clone() and skip the peer if the clone fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-63993 In the Linux kernel, the following vulnerability has been resolved: vxlan: do not reuse cached ip_hdr() value after skb_tunnel_check_pmtu() skb_tunnel_check_pmtu() can change skb->head. Reusing old_iph afer skb_tunnel_check_pmtu() can cause an UAF. Use instead ip_hdr(skb) as done in drivers/net/bareudp.c and drivers/net/geneve.c. Found by Sashiko.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64000 In the Linux kernel, the following vulnerability has been resolved: net: hsr: fix potential OOB access in supervision frame handling Ensure the entire TLV header is linearized before access by adding sizeof(struct hsr_sup_tlv) to the pskb_may_pull() calls. Without this, a truncated frame could cause an out-of-bounds access.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64002 In the Linux kernel, the following vulnerability has been resolved: ipv4: free net->ipv4.sysctl_local_reserved_ports after unregister_net_sysctl_table() ipv4_sysctl_exit_net() is currently freeing net->ipv4.sysctl_local_reserved_ports too soon. Only after unregister_net_sysctl_table() we can be sure no threads can possibly use the sysctls, including /proc/sys/net/ipv4/ip_local_reserved_ports.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64003 In the Linux kernel, the following vulnerability has been resolved: scsi: core: Run queues for all non-SDEV_DEL devices from scsi_run_host_queues While a SCSI host is in a recovery state, scsi_mq_requeue_cmd() will not set the requeue list for a requeued command to be kicked in the future. The expectation is a call to scsi_run_host_queues() will kick all SCSI devices once the recovery state is cleared. However, scsi_run_host_queues() uses shost_for_each_device() which uses scsi_device_get() and so will ignore devices in a partially removed state like SDEV_CANCEL. But these devices may also have requeued requests, leaving their requests stuck from not being kicked and causing the removal process of the device to hang. scsi_run_host_queues() needs to run against more devices than the macro shost_for_each_device() allows. Instead of using the too limiting scsi_device_get() state checks, only ignore devices in SDEV_DEL state or when unable to acquire a reference. Attempt to run the queues for all other devices when scsi_run_host_queues() is called.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64007 In the Linux kernel, the following vulnerability has been resolved: netfilter: synproxy: refresh tcphdr after skb_ensure_writable synproxy_tstamp_adjust() rewrites the TCP timestamp option in place and then patches the TCP checksum via inet_proto_csum_replace4() on the caller-supplied tcphdr pointer. Both ipv4_synproxy_hook() and ipv6_synproxy_hook() obtain that pointer with skb_header_pointer() before calling in, so it may either alias skb->head directly or point at the caller's on-stack _tcph buffer. Between obtaining the pointer and using it, the function calls skb_ensure_writable(skb, optend), which on a cloned or non-linear skb invokes pskb_expand_head() and frees the old skb->head. After that point the cached th is stale: caller (ipv[46]_synproxy_hook) th = skb_header_pointer(skb, ..., &_tcph) synproxy_tstamp_adjust(skb, protoff, th, ...) skb_ensure_writable(skb, optend) pskb_expand_head() /* kfree(old skb->head) */ ... inet_proto_csum_replace4(&th->check, ...) /* writes into freed head, or into the caller's stack copy leaving the on-wire checksum stale */ The option bytes are written through skb->data and are fine; only the checksum update goes through th and so lands in the wrong place. The result is either a write into freed slab memory or a packet leaving with a checksum that does not match its payload. Fix by re-deriving th from skb->data + protoff immediately after skb_ensure_writable() succeeds, so the subsequent checksum update targets the linear, writable header.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64010 In the Linux kernel, the following vulnerability has been resolved: nfc: llcp: Fix use-after-free race in nfc_llcp_recv_cc() A race condition exists in the NFC LLCP connection state machine where the connection acceptance packet (CC) can be processed concurrently with socket release. This can lead to a use-after-free of the socket object. When nfc_llcp_recv_cc() moves the socket from the connecting_sockets list to the sockets list, it does so without holding the socket lock. If llcp_sock_release() is executing concurrently, it might have already unlinked the socket and dropped its references, which can result in nfc_llcp_recv_cc() linking a freed socket into the live list. Fix this by holding lock_sock() during the state transition and list movement in nfc_llcp_recv_cc(). After acquiring the lock, check if the socket is still hashed to ensure it hasn't already been unlinked and marked for destruction by the release path. This aligns the locking pattern with recv_hdlc() and recv_disc().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64011 In the Linux kernel, the following vulnerability has been resolved: nfc: llcp: Fix use-after-free in llcp_sock_release() llcp_sock_release() unconditionally unlinks the socket from the local sockets list. However, if the socket is still in connecting state, it is on the connecting list. Fix this by checking the socket state and unlinking from the correct list.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64014 In the Linux kernel, the following vulnerability has been resolved: Input: usbtouchscreen - clamp NEXIO data_len/x_len to URB buffer size nexio_read_data() pulls data_len and x_len from a packed __be16 header in the device's interrupt packet and then walks packet->data[0..x_len) and packet->data[x_len..data_len) comparing each byte against a threshold. Both fields are 16-bit on the wire (max 65535). The existing adjustments shave at most 0x100 / 0x80 off, so the loop bound can still reach roughly 0xfeff. The URB transfer buffer for NEXIO is rept_size (1024) bytes from usb_alloc_coherent(), with the first 7 occupied by the packed header — so packet->data[] has 1017 valid bytes. read_data() callbacks are not given urb->actual_length, and nothing else bounds the walk. A device that lies about its length can get a ~64 KiB out-of-bounds read past the coherent DMA allocation. The first index whose byte exceeds NEXIO_THRESHOLD lands in begin_x / begin_y and from there into the reported touch coordinates, so adjacent kernel memory contents leak to userspace as ABS_X / ABS_Y events. Far enough out, the read can also hit an unmapped page and fault. Fix this all by clamping data_len to the buffer's data[] capacity and x_len to data_len.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64090 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tt: avoid empty VLAN responses The commit 16116dac2339 ("batman-adv: prevent TT request storms by not sending inconsistent TT TLVLs") added checks to the local (direct) TT response code. But the response can also be done indirectly by another node using the global TT state. To avoid such inconsistency states reported in the original fix, also avoid sending empty VLANs for replies from the global TT state.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64091 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tt: fix TOCTOU race for reported vlans The local TT based TVLV is generated by first checking the number of VLANs which have at least one TT entry. A new buffer with the correct size for the VLANs is then allocated. Only then, the list of VLANs s used to fill the VLAN entries in the buffer. During this time, the meshif_vlan_list_lock is held. But the actual number of TT entries of each VLAN can still increase during this time - just not the number of VLANs in the list. But the prefilter used in the buffer size calculation might still cause an increase of the number of VLANs which need to be stored. Simply because a VLAN might now suddenly have at least one entry when it had none in the pre-alloc check - and then needs to occupy space which was not allocated. It is better to overestimate the buffer size at the beginning and then fill the buffer only with the VLANs which are not empty.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64093 In the Linux kernel, the following vulnerability has been resolved: batman-adv: tp_meter: directly shut down timer on cleanup batadv_tp_sender_cleanup() was calling timer_delete_sync() followed by timer_delete() to guard against the timer handler re-arming itself between the two calls. This double-deletion hack relied on the sending status being set to 0 to suppress re-arming. Replace both calls with a single timer_shutdown_sync(). This function both waits for any running timer callback to complete (like timer_delete_sync()) and permanently disarms the timer so it cannot be re-armed afterwards, making re-arming prevention unconditional and self-documenting. The re-arming property is also required because otherwise: 1. context 0 (batadv_tp_recv_ack()) checks in batadv_tp_reset_sender_timer() if sending is still 1 -> it is 2. context 1 changes in batadv_tp_sender_shutdown() sending to 0 and in this process forces the kthread to stop timer in batadv_tp_sender_cleanup() 3. context 0 continues in batadv_tp_reset_sender_timer() and rearms the timer -> but the reference for it is already gone

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64094 In the Linux kernel, the following vulnerability has been resolved: batman-adv: bla: avoid NULL-ptr deref for claim via dropped interface Without rtnl_lock held, a hardif might be retrieved as primary interface of a meshif, but then (while operating on this interface) getting decoupled from the mesh interface. In this case, the meshif still exists but the pointer from the primary hardif to the meshif is set to NULL. The mesh_iface must be checked first to be non-NULL before continuing to send an ARP request using meshif.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64095 In the Linux kernel, the following vulnerability has been resolved: batman-adv: bla: avoid double decrement of bla.num_requests The bla.num_requests is increased when no request_sent was in progress. And it is decremented in various places (announcement was received, backbone is purged, periodic work). But the check if the request_sent is actually set to a specific state and the atomic_dec/_inc are not safe because they are not atomic (TOCTOU) and multiple such code portions can run concurrently. At the same time, it is necessary to modify request_sent (state) and bla.num_requests atomically. Otherwise batadv_bla_send_request() might set request_sent to 1 and is interrupted. batadv_handle_announce() can then set request_sent back to 0 and decrement num_requests before batadv_bla_send_request() incremented it. The two operations must therefore be locked. And since state (request_sent) and wait_periods are only accessed inside this lock, they can be converted to simpler datatypes. And to avoid that the bla.num_requests is touched by a parallel running context with a valid backbone_gw reference after batadv_bla_purge_backbone_gw() ran, a third state "stopped" is required to correctly signal that a backbone_gw is in the state of being cleaned up.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64099 In the Linux kernel, the following vulnerability has been resolved: drm/v3d: Fix use-after-free of CPU job query arrays on error path The CPU job ioctl's fail label calls kvfree() on cpu_job's timestamp and performance query arrays after v3d_job_cleanup(), which drops the job's last reference and frees cpu_job. Reading cpu_job at that point is a use-after-free. Also, on the early v3d_job_init() failure path, it is a NULL dereference, since v3d_job_deallocate() zeroes the local pointer. In the success path, the arrays are released from the scheduler's .free_job callback, but on the error path, they are freed manually, as the job was never pushed to the scheduler. While the success path deals with this correctly, the fail path doesn't. On top of that, the manual kvfree() calls only free the array storage; they don't drm_syncobj_put() the per-query syncobjs that v3d_timestamp_query_info_free() and v3d_performance_query_info_free() release on the success path. So the same fail path that triggers the use-after-free also leaks one syncobj reference per query. Unify the CPU job teardown into the CPU job's kref destructor, mirroring v3d_render_job_free(). The scheduler's .free_job slot reverts to the generic v3d_sched_job_free() and the fail label drops the manual kvfree() calls, leaving a single teardown path that is reached from both the scheduler and the ioctl error path. That removes the use-after-free, the NULL dereference, and the syncobj leak by construction.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64123 In the Linux kernel, the following vulnerability has been resolved: net: hsr: defer node table free until after RCU readers HSR node-list and node-status generic-netlink operations run under rcu_read_lock(). They walk hsr->node_db through hsr_get_next_node() and hsr_get_node_data(), but RTM_DELLINK teardown removes the same node table with plain list_del() and frees each node immediately. That lets a generic-netlink reader hold a struct hsr_node pointer across hsr_dellink(). In a KASAN build, widening the reader window after hsr_get_next_node() obtains the node reproduces a slab-use-after-free when the reader copies node->macaddress_A; the freeing stack is hsr_del_nodes() from hsr_dellink(). Use list_del_rcu() and defer the free through the existing hsr_free_node_rcu() callback. This matches the lifetime rule used by the HSR prune paths, which already delete nodes with list_del_rcu() and call_rcu().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64131 In the Linux kernel, the following vulnerability has been resolved: mm/memory: fix spurious warning when unmapping device-private/exclusive pages Device private and exclusive entries are only supported for anonymous folios. This condition is tested in __migrate_device_pages() and make_device_exclusive() using folio_test_anon(). However the unmap path tests this assumption using vma_is_anonymous(). This is wrong because whilst anonymous VMAs can only contain folios where folio_test_anon() is true the opposite relation does not hold. A folio for which folio_test_anon() is true does not imply vma_is_anonymous() is true. Such a condition can occur if for example a folio is part of a private filebacked mapping. In this case vma_is_anonymous() is false as the mapping is filebacked, but folio_test_anon() may be true, thus permitting devices to migrate the folio to device private memory. This can lead to the following spurious warnings during process teardown: [ 772.737706] ------------[ cut here ]------------ [ 772.739201] WARNING: mm/memory.c:1754 at unmap_page_range.cold+0x26/0x18a, CPU#17: hmm-tests/2041 [ 772.742050] Modules linked in: test_hmm nvidia_uvm(O) nvidia(O) [ 772.743959] CPU: 17 UID: 0 PID: 2041 Comm: hmm-tests Tainted: G W O 7.0.0+ #387 PREEMPT(full) [ 772.747104] Tainted: [W]=WARN, [O]=OOT_MODULE [ 772.748509] Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS rel-1.17.0-0-gb52ca86e094d-prebuilt.qemu.org 04/01/2014 [ 772.752117] RIP: 0010:unmap_page_range.cold+0x26/0x18a [ 772.753780] Code: 7e fe ff ff 48 89 4c 24 78 4c 89 44 24 38 e8 f2 ff b1 00 48 8b 4c 24 78 4c 8b 44 24 38 48 8b 44 24 18 48 83 78 48 00 74 04 90 <0f> 0b 90 48 89 ca b8 ff ff 37 00 48 c1 ea 03 48 c1 e0 2a 80 3c 02 [ 772.759602] RSP: 0018:ffff888112607550 EFLAGS: 00010286 [ 772.761310] RAX: ffff88811bbf4dc0 RBX: dffffc0000000000 RCX: ffffea03e9bfffd8 [ 772.763583] RDX: 1ffff1102377e9c1 RSI: 0000000000000008 RDI: ffff88811bbf4e08 [ 772.765914] RBP: 0000000000000006 R08: ffff8881059f7448 R09: ffffed10224c0e68 [ 772.768184] R10: ffff888112607347 R11: 0000000000000001 R12: 0000000000000001 [ 772.770461] R13: ffffea03e9bfffc0 R14: ffff888112607908 R15: ffffea03e9bfffc0 [ 772.772782] FS: 00007f327caa2780(0000) GS:ffff888427b7d000(0000) knlGS:0000000000000000 [ 772.775328] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 [ 772.777187] CR2: 00007f327ca89000 CR3: 00000001994d5000 CR4: 00000000000006f0 [ 772.779135] Call Trace: [ 772.779792] <TASK> [ 772.780317] ? dmirror_interval_invalidate+0x1a3/0x290 [test_hmm] [ 772.781873] ? vm_normal_page_pud+0x2b0/0x2b0 [ 772.782992] ? __rwlock_init+0x150/0x150 [ 772.784006] ? lock_release+0x216/0x2b0 [ 772.785008] ? __mmu_notifier_invalidate_range_start+0x505/0x6e0 [ 772.786522] ? lock_release+0x216/0x2b0 [ 772.787498] ? unmap_single_vma+0xb6/0x210 [ 772.788573] unmap_vmas+0x27d/0x520 [ 772.789506] ? unmap_single_vma+0x210/0x210 [ 772.790607] ? mas_update_gap.part.0+0x620/0x620 [ 772.791834] unmap_region+0x19e/0x350 [ 772.792769] ? remove_vma+0x130/0x130 [ 772.793684] ? mas_alloc_nodes+0x1f2/0x300 [ 772.794730] vms_complete_munmap_vmas+0x8c1/0xe20 [ 772.795926] ? unmap_region+0x350/0x350 [ 772.796917] do_vmi_align_munmap+0x36a/0x4e0 [ 772.798018] ? lock_release+0x216/0x2b0 [ 772.799024] ? vma_shrink+0x620/0x620 [ 772.799983] do_vmi_munmap+0x150/0x2c0 [ 772.800939] __vm_munmap+0x161/0x2c0 [ 772.801872] ? expand_downwards+0xd60/0xd60 [ 772.802948] ? clockevents_program_event+0x1ef/0x540 [ 772.804217] ? lock_release+0x216/0x2b0 [ 772.805158] __x64_sys_munmap+0x59/0x80 [ 772.805776] do_syscall_64+0xfc/0x670 [ 772.806336] ? irqentry_exit+0xda/0x580 [ 772.806976] entry_SYSCALL_64_after_hwframe+0x4b/0x53 [ 772.807772] RIP: 0033:0x7f327cbb2717 [ 772.808323] Code: 73 01 c3 48 8b 0d f9 76 0d 00 f7 d8 64 89 01 48 83 c8 ff c3 66 2e 0f 1f 84 00 00 00 00 00 0f 1f 44 00 00 b8 0b 00 00 00 0f 05 <48> 3d 01 f0 ff ---truncated---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64158 Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64188 In the Linux kernel, the following vulnerability has been resolved: net: qualcomm: rmnet: fix endpoint use-after-free in rmnet_dellink() rmnet_dellink() removes the endpoint from the hash table with hlist_del_init_rcu() and then immediately frees it with kfree(). However, RCU readers on the receive path (rmnet_rx_handler -> __rmnet_map_ingress_handler) may still hold a reference to the endpoint and dereference ep->egress_dev after the memory has been freed. The endpoint is a kmalloc-32 object, and the stale read at offset 8 corresponds to the egress_dev pointer. BUG: unable to handle page fault for address: ffffffffde942eef Oops: 0002 [#1] SMP NOPTI CPU: 1 UID: 0 PID: 137 Comm: poc_write Not tainted 7.0.0+ #4 PREEMPTLAZY RIP: 0010:rmnet_vnd_rx_fixup (rmnet_vnd.c:27) Call Trace: <TASK> __rmnet_map_ingress_handler (rmnet_handlers.c:48 rmnet_handlers.c:101) rmnet_rx_handler (rmnet_handlers.c:129 rmnet_handlers.c:235) __netif_receive_skb_core.constprop.0 (net/core/dev.c:6096) __netif_receive_skb_one_core (net/core/dev.c:6208) netif_receive_skb (net/core/dev.c:6467) tun_get_user (drivers/net/tun.c:1955) tun_chr_write_iter (drivers/net/tun.c:2003) vfs_write (fs/read_write.c:688) ksys_write (fs/read_write.c:740) </TASK> Add an rcu_head field to struct rmnet_endpoint and replace kfree() with kfree_rcu() so the endpoint memory remains valid through the RCU grace period. Also remove the rmnet_vnd_dellink() call and inline only the nr_rmnet_devs decrement, since rmnet_vnd_dellink() would set ep->egress_dev to NULL during the grace period, creating a data race with lockless readers.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64189 In the Linux kernel, the following vulnerability has been resolved: netfilter: ipset: fix race between dump and ip_set_list resize The release path of ip_set_dump_do() and ip_set_dump_done() read inst->ip_set_list via ip_set_ref_netlink(), a plain rcu_dereference_raw() of the array pointer. These run from netlink_recvmsg() without the nfnl mutex and without an RCU read-side critical section. A concurrent ip_set_create() can grow the array: it publishes the new array, calls synchronize_net() and then kvfree()s the old one. Since the dump paths read the array outside any RCU reader, synchronize_net() does not wait for them and the old array can be freed while they still index into it, causing a use-after-free. The dumped set itself stays pinned via set->ref_netlink, so only the array load needs protecting. Take rcu_read_lock() around it, matching ip_set_get_byname() and __ip_set_put_byindex(). BUG: KASAN: slab-use-after-free in ip_set_dump_do (net/netfilter/ipset/ip_set_core.c:1697) Read of size 8 at addr ffff88800b5c4018 by task exploit/150 Call Trace: ... kasan_report (mm/kasan/report.c:595) ip_set_dump_do (net/netfilter/ipset/ip_set_core.c:1697) netlink_dump (net/netlink/af_netlink.c:2325) netlink_recvmsg (net/netlink/af_netlink.c:1976) sock_recvmsg (net/socket.c:1159) __sys_recvfrom (net/socket.c:2315) ... Oops: general protection fault, probably for non-canonical address ... KASAN NOPTI KASAN: maybe wild-memory-access in range [0x02d6...d0-0x02d6...d7] RIP: 0010:ip_set_dump_do (net/netfilter/ipset/ip_set_core.c:1698) Kernel panic - not syncing: Fatal exception

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64191 In the Linux kernel, the following vulnerability has been resolved: i2c: stub: Reject I2C block transfers with invalid length The I2C_SMBUS_I2C_BLOCK_DATA case in stub_xfer() uses data->block[0] as the transfer length. The existing check only clamps it to avoid overrunning the chip->words[256] register array, but does not validate it against I2C_SMBUS_BLOCK_MAX (32), which is the limit of the union i2c_smbus_data.block buffer (34 bytes total). The driver is a development/test tool (CONFIG_I2C_STUB=m, not built by default) that must be loaded with a chip_addr= parameter. A local user with access to /dev/i2c-* can issue an I2C_SMBUS ioctl with I2C_SMBUS_I2C_BLOCK_DATA and data->block[0] > 32, causing stub_xfer() to read or write past the end of the union i2c_smbus_data.block buffer: BUG: KASAN: stack-out-of-bounds in stub_xfer (drivers/i2c/i2c-stub.c:223) Read of size 1 at addr ffff88800abcfd92 by task exploit/81 Call Trace: <TASK> stub_xfer (drivers/i2c/i2c-stub.c:223) __i2c_smbus_xfer (drivers/i2c/i2c-core-smbus.c:593) i2c_smbus_xfer (drivers/i2c/i2c-core-smbus.c:536) i2cdev_ioctl_smbus (drivers/i2c/i2c-dev.c:391) i2cdev_ioctl (drivers/i2c/i2c-dev.c:478) __x64_sys_ioctl (fs/ioctl.c:583) do_syscall_64 (arch/x86/entry/syscall_64.c:94) entry_SYSCALL_64_after_hwframe (arch/x86/entry/entry_64.S:130) </TASK> The bug exists because i2c-stub implements .smbus_xfer directly, bypassing the I2C_SMBUS_BLOCK_MAX validation in i2c_smbus_xfer_emulated(). The I2C_SMBUS_BLOCK_DATA case in the same function correctly validates against I2C_SMBUS_BLOCK_MAX, but the I2C_SMBUS_I2C_BLOCK_DATA case does not. Fix by rejecting transfers with data->block[0] == 0 or data->block[0] > I2C_SMBUS_BLOCK_MAX with -EINVAL, consistent with both the I2C_SMBUS_BLOCK_DATA case in the same function and the I2C_SMBUS_I2C_BLOCK_DATA validation in i2c_smbus_xfer_emulated().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64222 In the Linux kernel, the following vulnerability has been resolved: octeontx2-pf: avoid double free of pool->stack on AQ init failure otx2_pool_aq_init() frees pool->stack when mailbox sync or retry allocation fails, but leaves the pointer unchanged. Later, otx2_sq_aura_pool_init() unwinds the partial setup through otx2_aura_pool_free(), which frees pool->stack again. The CN20K-specific cn20k_pool_aq_init() implementation has the same bug in its corresponding error path. Set pool->stack to NULL immediately after the local free so the shared cleanup path does not free the same stack again while cleaning up partially initialized pool state. The bug was first flagged by an experimental analysis tool we are developing for kernel memory-management bugs while analyzing v6.13-rc1. The tool is still under development and is not yet publicly available. Manual inspection confirms that the bug is still present in v7.1-rc3. Runtime validation was not performed because reproducing this path requires OcteonTX2/CN20K hardware.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64233 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: uvc: hold opts->lock across XU walks in uvc_function_bind uvc_function_bind() walks &opts->extension_units twice without holding opts->lock: - directly, for the iExtension string-descriptor fixup loop; - indirectly, four times via uvc_copy_descriptors() (once per speed), where the helper iterates uvc->desc.extension_units (which aliases &opts->extension_units) to size and emit XU descriptors. The configfs side (uvcg_extension_make / uvcg_extension_drop, in drivers/usb/gadget/function/uvc_configfs.c) takes opts->lock around its list_add_tail / list_del operations. A privileged userspace process that holds the configfs subtree open and writes the gadget UDC name to bind the function while concurrently rmdir()'ing an extensions subdir can race uvcg_extension_drop() against the bind-time list walks and dereference a freed struct uvcg_extension. Hold opts->lock from the start of the XU string-descriptor fixup through the last uvc_copy_descriptors() call, releasing on the descriptor-error path via a new error_unlock label that drops the lock before falling through to the existing error label. This matches the locking discipline of the configfs callbacks and removes the only remaining unsynchronised reader of the XU list during bind. Reachability: only privileged processes that can mount configfs and write to gadget UDC files can trigger the race, so this is a correctness fix rather than a security boundary.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64234 In the Linux kernel, the following vulnerability has been resolved: tty: serial: pch_uart: add check for dma_alloc_coherent() Add a check for dma_alloc_coherent() failure to prevent a potential NULL pointer dereference in dma_handle_rx(). Properly release DMA channels and the PCI device reference using a goto ladder if the allocation fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64239 In the Linux kernel, the following vulnerability has been resolved: mm/damon/sysfs-schemes: delete tried region in regions_rmdirs() DAMON sysfs maintains the DAMOS tried region directory objects via a linked list. When the user requests refresh of the directories, DAMON sysfs removes all the region directories first, and then generate updated regions directory on the empty space. The removal function (damon_sysfs_scheme_regions_rm_dirs()) only puts the kobj objects. Deletion of the container region object from the linked list is done inside the kobj release callback function. If somehow the callback invocation is delayed, the list will contain regions list that gonna be freed. If the updated region directories creation is started in this situation, the list can be corrupted and use-after-free can happen. Because the kobj objects are managed by only DAMON sysfs, the issue cannot happen in normal situation. But, such delays can be made on kernels that built with CONFIG_DEBUG_KOBJECT_RELEASE. On the kernel, the issue can indeed be reproduced like below. # damo start --damos_action stat # cd /sys/kernel/mm/damon/admin/kdamonds/0/ # for i in {1..10}; do echo update_schemes_tried_regions > state; done # dmesg | grep underflow [ 89.296152] refcount_t: underflow; use-after-free. Fix the issue by removing the region object from the list when decrementing the reference count. Also update damos_sysfs_populate_region_dir() to add the region object to the list only after the kobject_init_and_add() is success, so that fail of kobject_init_and_add() is not leaving the deallocated object on the list. The issue was discovered [1] by Sashiko.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64240 In the Linux kernel, the following vulnerability has been resolved: media: rc: igorplugusb: fix control request setup packet Commit eac69475b01f ("media: rc: igorplugusb: heed coherency rules") changed the control request storage from an embedded struct to an allocated pointer so it can obey DMA coherency rules. However, the driver still passes &ir->request to usb_fill_control_urb(). That points the URB setup packet at the pointer field itself rather than at the allocated struct usb_ctrlrequest. USB core then interprets pointer bytes as the setup packet. This can produce an invalid bRequestType and trigger the control direction warning reported by syzbot: usb 2-1: BOGUS control dir, pipe 80003580 doesn't match bRequestType 0 Pass ir->request itself as the setup packet.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64242 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: net2280: Fix double free in probe error path usb_initialize_gadget() installs gadget_release() as the release callback for the embedded gadget device. The struct net2280 instance is therefore released through gadget_release() when the gadget device's last reference is dropped. The probe error path calls net2280_remove(), which tears down the partially initialized device and drops the gadget reference with usb_put_gadget(). Calling kfree(dev) afterwards can free the same object again. Drop the explicit kfree() and let the gadget device release callback handle the final free. This issue was found by a static analysis tool I am developing.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64243 In the Linux kernel, the following vulnerability has been resolved: ASoC: codecs: simple-mux: Fix enum control bounds check simple_mux_control_put() rejects values greater than e->items, but enum control values are zero based. For the two-entry mux used by this driver, valid values are 0 and 1, so value 2 must be rejected as well. Accepting e->items can store an invalid mux state, pass it to the GPIO setter, and pass it on to the DAPM mux update path where it is used as an index into the enum text array. Use the same >= e->items check used by the ASoC enum helpers.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64244 In the Linux kernel, the following vulnerability has been resolved: drivers/base/memory: set mem->altmap after successful device registration If __add_memory_block() fails at xa_store() (under memory pressure for example), device_unregister() is called, which eventually triggers memory_block_release() with mem->altmap still set, causing a WARN_ON(mem->altmap). This was triggered by modifying virtio-mem driver. Fix this by delaying the assignment of mem->altmap until after __add_memory_block() has succeeded.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64246 In the Linux kernel, the following vulnerability has been resolved: power: reset: linkstation-poweroff: fix use-after-free in the linkstation_poweroff_init() Move of_node_put(dn) after the of_match_node() call, which still needs the node pointer. The node reference is correctly released after use.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64247 In the Linux kernel, the following vulnerability has been resolved: KVM: x86: hyper-v: Bound the bank index when querying sparse banks When checking if a VP ID is included in a sparse bank set, explicitly check that the ID can actually be contained in a sparse bank (the TLFS allows for a maximum of 64 banks of 64 vCPUs each). When handling a paravirtual TLB flush for L2, the VP ID is copied verbatim from the enlightened VMCS, without any bounds check, i.e. isn't guaranteed to be under the limit of 4096. Failure to check the bounds of the VP ID leads to an out-of-bounds read when testing the sparse bank, and super strictly speaking could lead to KVM performing an unnecessary TLB flush for an L2 vCPU. ================================================================== BUG: KASAN: use-after-free in hv_is_vp_in_sparse_set+0x85/0x100 [kvm] Read of size 8 at addr ffff88811ba5f598 by task hyperv_evmcs/2802 CPU: 12 UID: 1000 PID: 2802 Comm: hyperv_evmcs Not tainted 7.1.0-rc2 #7 PREEMPT Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS 0.0.0 02/06/2015 Call Trace: <TASK> dump_stack_lvl+0x51/0x60 print_report+0xcb/0x5d0 kasan_report+0xb4/0xe0 kasan_check_range+0x35/0x1b0 hv_is_vp_in_sparse_set+0x85/0x100 [kvm] kvm_hv_flush_tlb+0xe9e/0x16c0 [kvm] kvm_hv_hypercall+0xe6b/0x1e60 [kvm] vmx_handle_exit+0x485/0x1b60 [kvm_intel] kvm_arch_vcpu_ioctl_run+0x22e3/0x5070 [kvm] kvm_vcpu_ioctl+0x5d0/0x10c0 [kvm] __x64_sys_ioctl+0x129/0x1a0 do_syscall_64+0xb9/0xcf0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 RIP: 0033:0x7f0e62d1a9bf </TASK> The buggy address belongs to the physical page: page: refcount:0 mapcount:0 mapping:0000000000000000 index:0xffffffffffffffff pfn:0x11ba5f flags: 0x4000000000000000(zone=1) raw: 4000000000000000 0000000000000000 00000000ffffffff 0000000000000000 raw: ffffffffffffffff 0000000000000000 00000000ffffffff 0000000000000000 page dumped because: kasan: bad access detected Memory state around the buggy address: ffff88811ba5f480: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ffff88811ba5f500: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff >ffff88811ba5f580: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ^ ffff88811ba5f600: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ffff88811ba5f680: ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ================================================================== Disabling lock debugging due to kernel taint Opportunistically add a compile time assertion to ensure the maximum number of sparse banks exactly matches the number of possible bits in the passed in mask. [sean: add KASAN splat, drop comment, add assert, massage changelog]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64249 In the Linux kernel, the following vulnerability has been resolved: fpga: region: fix use-after-free in child_regions_with_firmware() Move of_node_put(child_region) after the error print to avoid accessing freed memory when pr_err() references child_region. [ Yilun: Fix the Fixes tag ]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64254 In the Linux kernel, the following vulnerability has been resolved: NTB: epf: Avoid pci_iounmap() with offset when PEER_SPAD and CONFIG share BAR When BAR_PEER_SPAD and BAR_CONFIG share one PCI BAR, the module teardown path ends up calling pci_iounmap() on the same iomem with some offset, which is unnecessary and triggers a kernel warning like the following: Trying to vunmap() nonexistent vm area (0000000069a5ffe8) WARNING: mm/vmalloc.c:3470 at vunmap+0x58/0x68, CPU#5: modprobe/2937 [...] Call trace: vunmap+0x58/0x68 (P) iounmap+0x34/0x48 pci_iounmap+0x2c/0x40 ntb_epf_pci_remove+0x44/0x80 [ntb_hw_epf] pci_device_remove+0x48/0xf8 device_remove+0x50/0x88 device_release_driver_internal+0x1c8/0x228 driver_detach+0x50/0xb0 bus_remove_driver+0x74/0x100 driver_unregister+0x34/0x68 pci_unregister_driver+0x34/0xa0 ntb_epf_pci_driver_exit+0x14/0xfe0 [ntb_hw_epf] [...] Fix it by unmapping only when PEER_SPAD and CONFIG use difference bars.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64256 In the Linux kernel, the following vulnerability has been resolved: xfs: don't wrap around quota ids in dqiterate LOLLM noticed that q_id is an unsigned 32-bit variable. If it happens to be set to XFS_DQ_ID_MAX due to a filesystem that actually has a dquot for ID_MAX, then this addition will truncate to zero and the iteration starts over. Fix this by casting to u64.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64268 In the Linux kernel, the following vulnerability has been resolved: RDMA/siw: bound Read Response placement to the RREAD length In drivers/infiniband/sw/siw/siw_qp_rx.c, siw_proc_rresp() places each inbound Read Response DDP segment at sge->laddr + wqe->processed and then accumulates wqe->processed, but it never checks the running total against the sink buffer length on continuation segments. siw_check_sge() resolves and validates the sink memory only on the first fragment (the if (!*mem) branch), and siw_rresp_check_ntoh() compares the cumulative length against wqe->bytes only on the final segment (the !frx->more_ddp_segs guard). A connected siw peer that answers an outstanding RREAD with Read Response segments that keep the DDP Last flag clear, carrying more total payload than the RREAD requested, drives wqe->processed past the validated sink buffer; the next siw_rx_data() call writes out of bounds at sge->laddr + wqe->processed. siw runs iWARP over ordinary routable TCP, so the peer is the remote end of an established RDMA connection and needs no local privilege. Bound every segment before placement, exactly as siw_proc_send() and siw_proc_write() already do for their tagged and untagged paths, and terminate the connection with a base-or-bounds DDP error when the Read Response would overrun the sink buffer. This is the second receive-path length fix for this file. A separate change rejects an MPA FPDU length that underflows the per-fragment remainder in the header decode; that guard does not cover this case, because here each individual segment length is self-consistent and only the accumulated placement offset overruns the buffer.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64269 In the Linux kernel, the following vulnerability has been resolved: RDMA/rtrs-srv: Bound RDMA-Write length to chunk size in rdma_write_sg When the server answers an RTRS READ, rdma_write_sg() builds the source scatter/gather entry for the IB_WR_RDMA_WRITE that returns data to the peer. Its length is taken directly from the wire descriptor: plist->length = le32_to_cpu(id->rd_msg->desc[0].len); rd_msg points into the chunk buffer that the remote peer filled via RDMA-WRITE-WITH-IMM (rtrs_srv_rdma_done() -> process_io_req() -> process_read()), so desc[0].len is attacker-controlled and, before this change, was only rejected when zero. The source address is the fixed chunk start (dma_addr[msg_id]) and the source lkey is the PD-wide local_dma_lkey, which is not tied to the chunk's MR mapping, so the verbs layer does not constrain the transfer length to max_chunk_size. msg_id and off are bounded against queue_depth and max_chunk_size in rtrs_srv_rdma_done(), but desc[0].len is a separate field that was not checked against the chunk size. A peer that advertises desc[0].len larger than max_chunk_size can make the posted RDMA write read past the chunk's mapped region. The resulting behaviour depends on the IOMMU configuration: with no IOMMU or in passthrough mode the read may extend into memory adjacent to the chunk and be returned to the peer, which can disclose host memory; with a translating IOMMU the out-of-range access is expected to fault and abort the connection. In either case the transfer exceeds what the protocol permits and is driven by a remote peer. Reject a descriptor length above max_chunk_size, mirroring the existing off >= max_chunk_size bound in rtrs_srv_rdma_done(). Legitimate clients do not exceed it: the client sets desc[0].len to its MR length, which is capped at the negotiated max_io_size (max_chunk_size - MAX_HDR_SIZE).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64271 In the Linux kernel, the following vulnerability has been resolved: Input: touchwin - reset the packet index on every complete packet tw_interrupt() accumulates each non-zero serial byte into a fixed three-byte buffer with a running index that is only reset once a full packet has been received *and* the device's two Y bytes agree: tw->data[tw->idx++] = data; if (tw->idx == TW_LENGTH && tw->data[1] == tw->data[2]) { ... tw->idx = 0; } The reset is gated on tw->data[1] == tw->data[2], a value the device controls. A malicious, malfunctioning or counterfeit Touchwindow peripheral can stream non-zero bytes whose 2nd and 3rd bytes differ: the index reaches TW_LENGTH without the equality holding, is never reset, and keeps growing, so tw->data[tw->idx++] walks off the end of the three-byte array and the rest of the heap-allocated struct tw, one attacker-chosen byte at a time -- an unbounded, device-driven heap out-of-bounds write. Reset the index on every completed packet and report an event only when the two Y bytes match, like the other serio touchscreen drivers do.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64273 In the Linux kernel, the following vulnerability has been resolved: Input: iforce - bound the device-reported force-feedback effect index iforce_process_packet() handles a status report (packet id 0x02) by taking a force-feedback effect index straight from the device wire and using it to address the per-effect state array: i = data[1] & 0x7f; if (data[1] & 0x80) { if (!test_and_set_bit(FF_CORE_IS_PLAYED, iforce->core_effects[i].flags)) ... } else if (test_and_clear_bit(FF_CORE_IS_PLAYED, iforce->core_effects[i].flags)) { ... } The index is masked only with 0x7f, so it ranges 0..127, but core_effects[] holds only IFORCE_EFFECTS_MAX (32) entries. For an index of 32..127 the test_and_set_bit()/test_and_clear_bit() is an out-of-bounds single-bit read-modify-write past the array. core_effects[] is the second-to-last member of struct iforce, so the write lands in the trailing members and beyond the embedding kzalloc()'d iforce_serio / iforce_usb object. data[1] is unvalidated device payload on both transports (the USB interrupt endpoint and serio), and the status path is not gated on force feedback being present, so a malicious or counterfeit device can set or clear a bit at an attacker-chosen offset past the object. Reject an out-of-range index instead of indexing with it. Bound against the array dimension IFORCE_EFFECTS_MAX rather than dev->ff->max_effects so the check guarantees memory safety regardless of how many effects the device registered. A legitimate "effect started/stopped" status always carries an index below IFORCE_EFFECTS_MAX, so well-formed devices are unaffected; the neighbouring mark_core_as_ready() loop is already bounded and is left untouched.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64274 In the Linux kernel, the following vulnerability has been resolved: Input: goodix - clamp the device-reported contact count goodix_ts_read_input_report() copies the number of touch points reported by the device into an on-stack buffer u8 point_data[2 + GOODIX_MAX_CONTACT_SIZE * GOODIX_MAX_CONTACTS]; which is sized for at most GOODIX_MAX_CONTACTS (10) contacts. The only runtime check bounds the per-interrupt count against ts->max_touch_num, but that value is taken verbatim from a 4-bit field of the device configuration block and is never clamped: ts->max_touch_num = ts->config[MAX_CONTACTS_LOC] & 0x0f; The nibble can be 0..15, so a malfunctioning, malicious or counterfeit controller (or an attacker tampering with the I2C bus) can advertise up to 15 contacts. goodix_ts_read_input_report() then accepts a touch_num of up to 15 and the second goodix_i2c_read() writes ts->contact_size * (touch_num - 1) bytes past the one-contact header into point_data - up to 30 bytes (45 with the 9-byte report format) beyond the 92-byte buffer: a stack out-of-bounds write. Clamp max_touch_num to GOODIX_MAX_CONTACTS, the number of contacts point_data[] is sized for, when reading it from the configuration.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64276 In the Linux kernel, the following vulnerability has been resolved: Input: synaptics-rmi4 - bound the F30 keymap to the GPIO/LED count rmi_f30_map_gpios() allocates gpioled_key_map with min(gpioled_count, TRACKSTICK_RANGE_END) == at most 6 entries, but rmi_f30_attention() iterates the full f30->gpioled_count (device query register, range 0..31) and dereferences gpioled_key_map[i], and input->keycodemax is set to the full gpioled_count while input->keycode points at the 6-entry allocation. A device that reports gpioled_count > 6 with GPIO support enabled therefore causes an out-of-bounds read on the attention interrupt and out-of-bounds read/write through the EVIOCGKEYCODE/EVIOCSKEYCODE ioctls, which bound the index only against keycodemax. This is the same defect as the F3A handler, which was copied from F30. Size the keymap for the full gpioled_count; the mapping loop still assigns only the first min(gpioled_count, TRACKSTICK_RANGE_END) entries.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64277 In the Linux kernel, the following vulnerability has been resolved: Input: synaptics-rmi4 - bound the F3A keymap to the GPIO count rmi_f3a_initialize() takes the GPIO count from the device query register (f3a->gpio_count = buf & RMI_F3A_GPIO_COUNT, range 0..127). rmi_f3a_map_gpios() then allocates gpio_key_map with min(gpio_count, TRACKSTICK_RANGE_END) == at most 6 entries, but rmi_f3a_attention() iterates the full gpio_count and dereferences gpio_key_map[i], and input->keycodemax is set to the full gpio_count while input->keycode points at the 6-entry allocation. A device that reports gpio_count > 6 therefore causes an out-of-bounds read of gpio_key_map[] on every attention interrupt, and out-of-bounds accesses through the input core's default keymap ioctls: EVIOCGKEYCODE reads past the buffer (leaking adjacent slab memory to user space) and EVIOCSKEYCODE writes a caller-controlled value past it, for any process able to open the evdev node, since input_default_getkeycode() and input_default_setkeycode() only bound the index against keycodemax. Size the keymap for the full gpio_count. The mapping loop is unchanged: it still assigns only the first min(gpio_count, TRACKSTICK_RANGE_END) entries; the remaining slots stay KEY_RESERVED (devm_kcalloc zero-fills) and are skipped when reporting.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64286 In the Linux kernel, the following vulnerability has been resolved: KVM: arm64: Clear __hyp_running_vcpu when flushing the pKVM hyp vCPU flush_hyp_vcpu() copies the host vCPU context into the hyp's private vCPU on every run. ctxt_to_vcpu() expects a guest context to have a NULL __hyp_running_vcpu, which is only ever set on the host context, so that it resolves the vCPU via container_of(). While this is generally the case, flush_hyp_vcpu() copies the context verbatim and does not enforce this, so a value provided by the host is dereferenced at EL2 (host -> EL2). Fix by clearing __hyp_running_vcpu after the copy.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64289 In the Linux kernel, the following vulnerability has been resolved: iommufd: Set upper bounds on cache invalidation entry_num and entry_len iommufd_hwpt_invalidate() takes a user-controlled entry_num and entry_len, each bounded only by U32_MAX. An entry_len beyond the kernel's struct size makes the copy helper verify the extra bytes are zero, scanning that excess in one uninterruptible pass; a multi-gigabyte value over zeroed user memory trips the soft-lockup watchdog. A large entry_num is the other half, driving the backend invalidation loop with no reschedule. The VT-d nested handler, for one, copies each entry and flushes caches per iteration, pinning the CPU on a non-preemptible kernel. Cap both in the ioctl. entry_len is held under PAGE_SIZE, above any request struct, and entry_num under 1 << 19, the order of a hardware invalidation queue and well beyond any real batch, bounding the per-call loop length.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64296 In the Linux kernel, the following vulnerability has been resolved: exfat: bound uniname advance in exfat_find_dir_entry() In exfat_find_dir_entry(), each TYPE_EXTEND (file name) entry advances the output pointer by a fixed amount while the loop guard only tracks the accumulated name length: if (++order == 2) uniname = p_uniname->name; else uniname += EXFAT_FILE_NAME_LEN; len = exfat_extract_uni_name(ep, entry_uniname); name_len += len; unichar = *(uniname+len); *(uniname+len) = 0x0; uniname grows by EXFAT_FILE_NAME_LEN (15) per name entry, but name_len grows only by the actual extracted length, which is shorter when a name fragment contains an early NUL. The only guard is `name_len >= MAX_NAME_LENGTH`, so a crafted directory with many short name fragments lets uniname run far past the p_uniname->name[MAX_NAME_LENGTH + 3] buffer while name_len stays small, causing an out-of-bounds read and write at *(uniname+len). The sibling extractor exfat_get_uniname_from_ext_entry() already stops on a short fragment (the lockstep `len != EXFAT_FILE_NAME_LEN` guard added in commit d42334578eba ("exfat: check if filename entries exceeds max filename length")); exfat_find_dir_entry() never got the equivalent. Track the per-entry write offset as a count and reject a fragment once the offset, or the offset plus the extracted length, would exceed MAX_NAME_LENGTH, before forming the output pointer.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64297 In the Linux kernel, the following vulnerability has been resolved: module: decompress: check return value of module_extend_max_pages() module_extend_max_pages() calls kvrealloc() internally and returns -ENOMEM on allocation failure. The return value is never checked. If the initial allocation fails, info->pages remains NULL and info->max_pages remains 0. Subsequent calls to module_get_next_page() will attempt to dynamically grow the array by calling module_extend_max_pages(info, 0) since info->used_pages is 0. This results in kvrealloc(NULL, 0) returning ZERO_SIZE_PTR, which is treated as a success, leading to a dereference of ZERO_SIZE_PTR and a kernel oops. Fix: add the missing error check after module_extend_max_pages() and return immediately on failure. This matches the pattern used by every other kvrealloc() caller in the module loading path. [Sami: Corrected the analysis in the commit message.]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64298 In the Linux kernel, the following vulnerability has been resolved: NFSv4: include MAY_WRITE in open permission mask for O_TRUNC POSIX requires write permission to truncate a file, so an open() that specifies O_TRUNC must be authorized for write access regardless of the O_ACCMODE access mode. nfs_open_permission_mask() builds the access mask passed to nfs_may_open(), which is the local authorization gate for OPENs the client serves itself from a cached write delegation via the can_open_delegated() path in nfs4_try_open_cached(). The mask is derived from O_ACCMODE alone, so an open(O_RDONLY | O_TRUNC) against a file the caller cannot write requests only MAY_READ and passes the local check. The OPEN is then satisfied locally and the truncation is issued to the server as a SETATTR(size=0) over the delegation stateid, which the server accepts under standard write-delegation semantics. POSIX requires that this open fail with EACCES. Include MAY_WRITE in the mask whenever O_TRUNC is set so the local check matches the access the server would have enforced.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64301 In the Linux kernel, the following vulnerability has been resolved: regulator: scmi: fix of_node refcount leak in scmi_regulator_probe() scmi_regulator_probe() calls of_find_node_by_name() which takes a reference on the returned device node. On the error path where process_scmi_regulator_of_node() fails, the function returns without calling of_node_put() on the child node, leaking the reference. Add of_node_put(np) on the error path to properly release the reference.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64303 In the Linux kernel, the following vulnerability has been resolved: spi: fsl-lpspi: terminate the RX channel on TX prepare failure path When dmaengine_prep_slave_sg() fails for the TX channel, the error path terminates the TX DMA channel but leaves the RX channel running. Since the RX channel was already submitted and issued prior to preparing the TX descriptor, returning -EINVAL causes the SPI core to unmap the DMA buffers while the RX DMA engine continues writing to them, leading to potential memory corruption or use-after-free. Terminate the RX channel before returning on the TX prepare failure path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64304 In the Linux kernel, the following vulnerability has been resolved: crypto: qat - validate RSA CRT component lengths The generic RSA key parser (rsa_helper.c) bounds each CRT component (p, q, dp, dq, qinv) by the modulus size n_sz, but qat_rsa_setkey_crt() allocates half-size DMA buffers (key_sz / 2) and right-aligns each component with: memcpy(dst + half_key_sz - len, src, len) When a CRT component is larger than half_key_sz the subtraction underflows and memcpy writes past the DMA buffer, causing memory corruption. Add a len > half_key_sz check next to the existing !len check for each of the five CRT components so the driver falls back to the non-CRT path instead of writing out of bounds.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64312 In the Linux kernel, the following vulnerability has been resolved: crypto: pcrypt - restore callback for non-parallel fallback pcrypt installs pcrypt_aead_done() on the child AEAD request before trying to submit it through padata. If padata_do_parallel() returns -EBUSY, pcrypt falls back to calling the child AEAD directly. That fallback must not keep the padata completion callback. Otherwise an asynchronous completion runs pcrypt_aead_done() even though the request was never enrolled in padata. Restore the original request callback and callback data before calling the child AEAD directly. This keeps the fallback path aligned with a direct AEAD request while leaving the parallel path unchanged.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64315 In the Linux kernel, the following vulnerability has been resolved: crypto: caam - use print_hex_dump_devel to guard key hex dumps Use print_hex_dump_devel() for dumping sensitive key material in *_setkey() to avoid leaking secrets at runtime when CONFIG_DYNAMIC_DEBUG is enabled.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64316 In the Linux kernel, the following vulnerability has been resolved: crypto: caam - use print_hex_dump_devel to guard key hex dumps Use print_hex_dump_devel() for dumping sensitive key material in *_setkey() and gen_split_key() to avoid leaking secrets at runtime when CONFIG_DYNAMIC_DEBUG is enabled.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64317 In the Linux kernel, the following vulnerability has been resolved: isofs: bound Rock Ridge symlink components to the SL record get_symlink_chunk() and the SL handling in parse_rock_ridge_inode_internal() walk the variable-length components of a Rock Ridge "SL" (symbolic link) record. Each component is a two-byte header (flags, len) followed by len bytes of text, so it occupies slp->len + 2 bytes. Both loops read slp->len and advance to the next component, and get_symlink_chunk() additionally does memcpy(rpnt, slp->text, slp->len), but neither checks that the component lies within the SL record before dereferencing it. A crafted SL record whose component declares a len that runs past the record (rr->len) therefore triggers an out-of-bounds read of up to 255 bytes. When the record sits at the tail of its backing buffer - for example a small kmalloc()ed continuation block reached through a CE record - the read crosses the allocation; get_symlink_chunk() then copies the out-of-bounds bytes into the symlink body returned to user space by readlink(), disclosing adjacent kernel memory. ISO 9660 images are routinely mounted from untrusted removable media - desktop environments auto-mount them (e.g. via udisks2) without CAP_SYS_ADMIN - so the record contents are attacker-controlled. Reject any component that does not fit in the remaining record bytes before using it. In get_symlink_chunk() return NULL, like the existing output-buffer (plimit) checks, so a malformed record makes readlink() fail with -EIO rather than silently returning a truncated target; in parse_rock_ridge_inode_internal() stop the inode-size walk.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64318 In the Linux kernel, the following vulnerability has been resolved: partitions: aix: bound the pp_count scan to the ppe array aix_partition() reads the physical volume descriptor into a fixed-size struct pvd and then scans its physical-partition-extent array: int numpps = be16_to_cpu(pvd->pp_count); ... for (i = 0; i < numpps; i += 1) { struct ppe *p = pvd->ppe + i; ... lp_ix = be16_to_cpu(p->lp_ix); pvd points at a single kmalloc()'d struct pvd whose ppe[] member holds a fixed ARRAY_SIZE(pvd->ppe) (1016) entries, but the loop runs up to the on-disk pp_count. pp_count is an unvalidated __be16 read straight from the descriptor, so a crafted AIX image with pp_count larger than 1016 drives the loop to read pvd->ppe[i] past the end of the allocation (up to 65535 entries, ~2 MB out of bounds). The partition scan runs without mounting anything, when a block device with a crafted AIX/IBM partition table appears (an attacker-supplied image attached with losetup -P, or a device auto-scanned by udev), via msdos_partition() -> aix_partition(). Clamp the scan to the number of entries the ppe[] array can hold.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64319 In the Linux kernel, the following vulnerability has been resolved: nvmet-auth: validate reply message payload bounds against transfer length nvmet_auth_reply() accesses the variable-length rval[] array using attacker-controlled hl (hash length) and dhvlen (DH value length) fields without verifying they fit within the allocated buffer of tl bytes. A malicious NVMe-oF initiator can craft a DHCHAP_REPLY message with a small transfer length but large hl/dhvlen values, causing out-of-bounds heap reads when the target processes the DH public key (rval + 2*hl) or performs the host response memcmp. With DH authentication configured, the OOB pointer is passed directly to sg_init_one() and read by crypto_kpp_compute_shared_secret(), reaching up to 526 bytes past the buffer. This is exploitable pre-authentication. Add bounds validation ensuring sizeof(*data) + 2*hl + dhvlen <= tl before any access to the variable-length fields. Discovered by Atuin - Automated Vulnerability Discovery Engine.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64321 In the Linux kernel, the following vulnerability has been resolved: nvme: target: rdma: fix ndev refcount leak on queue connect nvmet_rdma_queue_connect() calls nvmet_rdma_find_get_device() which acquires a reference on the returned ndev via kref_get(). On the path where the host queue backlog is exceeded and the function returns NVME_SC_CONNECT_CTRL_BUSY, reference of ndev is not released, leaking the kref. Fix this by adding a goto to the existing put_device label before the early return.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64324 In the Linux kernel, the following vulnerability has been resolved: udf: validate free block extents against the partition length udf_free_blocks() checks the logical block number and count against the partition length, but drops the extent offset from that final bound. A crafted extent can pass the guard while logicalBlockNum + offset + count points past the partition, which later indexes past the space bitmap array. A single ftruncate(2) on a file backed by such an extent reliably panics the kernel. This is a local availability issue. On desktop systems where UDisks/polkit allows the active user to mount removable UDF media without CAP_SYS_ADMIN, an unprivileged local user can supply the crafted filesystem and trigger the panic by truncating a writable file on it. Systems that require root or CAP_SYS_ADMIN to mount the image have a higher prerequisite. No confidentiality or integrity impact is claimed: the reproduced primitive is an out-of-bounds read of a bitmap pointer slot followed by a kernel panic. Use the already computed logicalBlockNum + offset + count value for the partition length check. Also make load_block_bitmap() reject an out-of-range block group before indexing s_block_bitmap[], so corrupted callers cannot walk past the flexible array.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64326 In the Linux kernel, the following vulnerability has been resolved: block: skip sync_blockdev() on surprise removal in bdev_mark_dead() bdev_mark_dead()'s @surprise == true means the device is already gone. The filesystem callback fs_bdev_mark_dead() honours this and skips sync_filesystem(), but the bare block device path (no ->mark_dead op) lost its !surprise guard when the holder ->mark_dead callback was wired up (see Fixes), and now calls sync_blockdev() unconditionally, which can hang forever waiting on writeback that can no longer complete. syzkaller hit this via nvme_reset_work()'s "I/O queues lost" path: nvme_mark_namespaces_dead() -> blk_mark_disk_dead() -> bdev_mark_dead(bdev, true) -> sync_blockdev() blocks in folio_wait_writeback(), wedging the reset worker and every task waiting on it. Skip the sync on surprise removal, matching fs_bdev_mark_dead(); invalidate_bdev() still runs. Orderly removal (surprise == false) is unchanged. Found by FuzzNvme(Syzkaller with FEMU fuzzing framework).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64329 In the Linux kernel, the following vulnerability has been resolved: usb: typec: ucsi: ccg: Fix use-after-free of ucsi on remove The threaded IRQ handler ccg_irq_handler() calls ucsi_notify_common(), which on a connector-change event calls ucsi_connector_change() and schedules connector work. In ucsi_ccg_remove(), ucsi_destroy() frees uc->ucsi (kfree) before free_irq() is called, so a handler invocation already in flight may access the freed object after ucsi_destroy(). CPU 0 (remove) | CPU 1 (threaded IRQ) ucsi_destroy(uc->ucsi) | ccg_irq_handler() kfree(ucsi) // FREE | ucsi_notify_common(uc->ucsi) // USE Move free_irq() before ucsi_destroy() in the remove path. It is kept after ucsi_unregister(): ucsi_unregister() cancels connector work whose handler issues GET_CONNECTOR_STATUS through ucsi_send_command_common(), which waits for a completion that is signalled from the IRQ handler, so the IRQ must stay active until that work has been cancelled. The probe error path already orders free_irq() before ucsi_destroy(). This bug was found by static analysis.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64331 In the Linux kernel, the following vulnerability has been resolved: usbip: vudc: fix NULL deref in vep_dequeue() vep_alloc_request() wasn't initializing vrequest->udc, so cancellations on the FunctionFS AIO path were arriving in vep_dequeue without a valid UDC reference. Since vrequest->udc is never actually properly used anywhere, we opt to remove it, and update vep_dequeue to obtain a reference to the udc with ep_to_vudc(), consistent with the other vep_ ops. AFAICT this bug has existed for ~10 years. Seems that nobody has really stressed the FunctionFS AIO path on usbip's vudc. I tested this fix in a QEMU aarch64 guest driving FunctionFS endpoints via AIO. Before the fix, running `usbip attach` from the host would cause the guest to oops with the following backtrace: Call trace: vep_dequeue+0x1c/0xe4 (P) usb_ep_dequeue+0x14/0x20 ffs_aio_cancel+0x24/0x34 __arm64_sys_io_cancel+0xb0/0x124 do_el0_svc+0x68/0x100 el0_svc+0x18/0x5c el0t_64_sync_handler+0x98/0xdc el0t_64_sync+0x154/0x158

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64337 In the Linux kernel, the following vulnerability has been resolved: usb: mtu3: unmap request DMA on queue failure mtu3_gadget_queue() maps the request before checking whether the QMU GPD ring can accept another transfer. the request is returned with -EAGAIN before it is linked on the endpoint request list if mtu3_prepare_transfer() fails. Normal completion and dequeue paths unmap requests from mtu3_req_complete(), but this error path never reaches that helper, so the DMA mapping is left active. Unmap the request before returning from the failed queue path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64338 In the Linux kernel, the following vulnerability has been resolved: USB: misc: uss720: unregister parport on probe failure uss720_probe() registers a parport before reading the 1284 register used to detect unsupported Belkin F5U002 adapters. If get_1284_register() fails, the error path drops the driver private data and the USB device reference, but leaves the parport device registered. Leaving the port registered is more than a private allocation leak: parport_register_port() has already reserved a parport number and registered the parport bus device, while pp->private_data still points at the private data that the common error path is about to release. Undo the pre-announce registration in the get_1284_register() failure branch before jumping to the common private-data cleanup path. Clear priv->pp first, matching the disconnect path and avoiding a stale pointer in the private data. This issue was identified during our ongoing static-analysis research while reviewing kernel code.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64346 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: udc: Fix use-after-free in gadget_match_driver The udc structure acts as the management structure for the gadget, but their lifecycles are decoupled. A race condition exists where usb_del_gadget() frees the udc memory (e.g., via mode-switch work) while gadget_match_driver() concurrently accesses the freed udc memory (e.g., via configfs), causing a Use-After-Free (UAF) that triggers a NULL pointer dereference when the freed memory is zeroed: [39430.908615][ T1171] Unable to handle kernel NULL pointer dereference at virtual address 0000000000000000 [39430.911397][ T1171] pc : __pi_strcmp+0x20/0x140 [39430.911441][ T1171] lr : gadget_match_driver+0x34/0x60 ... [39430.911890][ T1171] usb_gadget_register_driver_owner+0x50/0xf8 [39430.911910][ T1171] gadget_dev_desc_UDC_store+0xf4/0x140 [39430.931308][ T1171] configfs_write_iter+0xec/0x134 [39430.957058][ T1171] Workqueue: events_freezable __dwc3_set_mode [39430.957287][ T1171] dwc3_gadget_exit+0x34/0x8c [39430.957304][ T1171] __dwc3_set_mode+0xc0/0x664 Fix this by ensuring the udc structure remains allocated until the gadget is released. To achieve this, introduce a new usb_gadget_release() routine to the core. When the gadget is added, usb_add_gadget() stores the gadget's release routine in the udc structure and takes a reference to the udc. When the gadget is released, usb_gadget_release() drops the reference to the udc and then calls the gadget's release routine.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64347 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: composite: fix dead empty check in the USB_DT_OTG handler The OTG branch of composite_setup() falls back to the first configuration when none is selected: if (cdev->config) config = cdev->config; else config = list_first_entry(&cdev->configs, struct usb_configuration, list); if (!config) goto done; ... memcpy(req->buf, config->descriptors[0], value); list_first_entry() never returns NULL. On an empty list it returns container_of() of the list head. So the "if (!config)" check is dead. When cdev->configs is empty, config points at the head inside struct usb_composite_dev. config->descriptors[0] reads whatever sits at that offset. The memcpy copies up to w_length bytes of it into the response buffer. cdev->configs can be empty in two cases. One is a teardown race on gadget unbind with a control transfer in flight. The other is a driver that sets is_otg before it adds a config. A reproducer that holds cdev->configs empty triggers a KASAN fault in this branch. Use list_first_entry_or_null() so the existing check does its job.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64350 In the Linux kernel, the following vulnerability has been resolved: usb: cdnsp: fix stream context array leak in cdnsp_alloc_stream_info() cdnsp_alloc_stream_info() allocates stream_info->stream_ctx_array with cdnsp_alloc_stream_ctx(). If a later stream ring allocation or stream mapping update fails, the error path frees the allocated stream rings and stream_rings array, but leaves stream_ctx_array allocated. Free the stream context array before falling through to the stream_rings cleanup path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64358 In the Linux kernel, the following vulnerability has been resolved: media: mtk-jpeg: cancel workqueue on release for supported platforms only Since a recent fix the mtk_jpeg_release function cancels any pending or running work present in the driver workqueue using cancel_work_sync function. Currently, only the multicore based variants use this workqueue and they have the jpeg_worker platform data field initialized with a workqueue callback function. For the others, this field value remain NULL by default. The cancel_work_sync function is unconditionally called in mtk_jpeg_release function, even for the variants that do not use the workqueue. This call generates a WARN_ON print in __flush_work because the workqueue callback function presence check fails in __flush_work function (used by cancel_work_sync). So, to avoid these warnings, call cancel_work_sync only if a workqueue callback is defined in platform data.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64359 In the Linux kernel, the following vulnerability has been resolved: nilfs2: reject CLEAN_SEGMENTS ioctl with out-of-range segment numbers Syzbot reported a hung task in nilfs_transaction_begin() where multiple tasks performing chmod() on a nilfs2 mount blocked for over 143 seconds waiting to acquire ns_segctor_sem for read: INFO: task syz.0.17:5918 blocked for more than 143 seconds. Call Trace: schedule+0x164/0x360 rwsem_down_read_slowpath+0x6d9/0x940 down_read+0x99/0x2e0 nilfs_transaction_begin+0x364/0x710 fs/nilfs2/segment.c:221 nilfs_setattr+0x124/0x2c0 fs/nilfs2/inode.c:921 notify_change+0xc1a/0xf40 chmod_common+0x273/0x4a0 do_fchmodat+0x12d/0x230 The writer holding ns_segctor_sem was a concurrent NILFS_IOCTL_CLEAN_SEGMENTS caller, stuck inside printk while emitting per-element warnings from nilfs_sufile_updatev(): __nilfs_msg+0x373/0x450 fs/nilfs2/super.c:78 nilfs_sufile_updatev+0x21c/0x6d0 fs/nilfs2/sufile.c:186 nilfs_sufile_freev fs/nilfs2/sufile.h:93 [inline] nilfs_free_segments fs/nilfs2/segment.c:1140 [inline] nilfs_segctor_collect_blocks fs/nilfs2/segment.c:1261 [inline] nilfs_segctor_do_construct+0x1f55/0x76c0 nilfs_clean_segments+0x3bd/0xa50 nilfs_ioctl_clean_segments fs/nilfs2/ioctl.c:922 [inline] nilfs_ioctl+0x261f/0x2780 The root cause is that user-supplied segment numbers are not validated before nilfs_clean_segments() begins doing work; the range check on each segnum is performed deep inside the call chain by nilfs_sufile_updatev(), which emits a nilfs_warn() per invalid entry while still holding the segctor lock and the sufile mi_sem. Under load (repeated invocations across multiple mounts saturating the global printk path), the cumulative printk latency keeps ns_segctor_sem held long enough to trip the hung_task watchdog, blocking concurrent operations such as chmod() that need ns_segctor_sem for read. Fix by validating the contents of kbufs[4] in nilfs_clean_segments() immediately after acquiring ns_segctor_sem via nilfs_transaction_lock(). Holding ns_segctor_sem serializes the check against nilfs_ioctl_resize(), which can modify ns_nsegments, so the validation uses a consistent value. Out-of-range segment numbers are rejected with -EINVAL before any segment-cleaning work begins, so the bad entries never reach the per-element diagnostic path inside nilfs_sufile_updatev().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64360 In the Linux kernel, the following vulnerability has been resolved: hfs/hfsplus: zero-initialize buffer in hfs_bnode_read hfs_bnode_read() can return early without writing to the output buffer when is_bnode_offset_valid() fails or when check_and_correct_requested_ length() corrects the length to zero. Callers such as hfs_bnode_read_ u16() and hfs_bnode_read_u8() pass stack-allocated buffers and use the result unconditionally, leading to KMSAN uninit-value reports. Rather than initializing at each individual call site, zero the buffer at the start of hfs_bnode_read() before any validation checks. This ensures all callers in both hfs and hfsplus get a deterministic zero value regardless of which early-return path is taken.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64361 In the Linux kernel, the following vulnerability has been resolved: hfs/hfsplus: fix u32 overflow in check_and_correct_requested_length check_and_correct_requested_length() compares (off + len) against node_size using u32 arithmetic. When the caller passes a large len value (e.g. from an underflowed subtraction in hfs_brec_remove()), off + len can wrap past 2^32 and produce a small result, causing the bounds check to pass when it should fail. For example, with off=14 and len=0xFFFFFFF2 (underflowed from data_off - keyoffset - size in hfs_brec_remove), off + len wraps to 6, which is less than a typical node_size of 512, so the check passes and the subsequent memmove reads ~4GB past the node buffer. Fix this by widening the addition to u64 before comparing against node_size. This prevents the u32 wrap while keeping the logic straightforward.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64364 In the Linux kernel, the following vulnerability has been resolved: HID: multitouch: fix out-of-bounds bit access on mt_io_flags mt_io_flags is a single unsigned long, but mt_process_slot(), mt_release_pending_palms() and mt_release_contacts() use it as a per-slot bitmap indexed by the slot number. That slot number is only bounded by td->maxcontacts, which is taken from the device's ContactCountMaximum feature report and can be up to 255, not by BITS_PER_LONG. As a result, a multitouch device that advertises a large contact count makes set_bit()/clear_bit() operate past the mt_io_flags word and corrupt the adjacent members of struct mt_device. The sticky-fingers release timer is the easiest way to reach this. mt_release_contacts() runs for (i = 0; i < mt->num_slots; i++) clear_bit(i, &td->mt_io_flags); with num_slots == maxcontacts. For maxcontacts around 250 the loop clears the bits that overlap td->applications.next, zeroing that list head, and the list_for_each_entry() that immediately follows then dereferences NULL. The kernel panics from timer (softirq) context. On a KASAN build this shows up as a general protection fault in mt_release_contacts() with a null-ptr-deref at offset 0x58, which is offsetof(struct mt_application, num_received). The state is reachable from an untrusted USB or Bluetooth HID multitouch device; no local privileges are required. Store the per-slot active state in a separately allocated bitmap sized for maxcontacts, the same pattern already used for pending_palm_slots, and keep only MT_IO_FLAGS_RUNNING in mt_io_flags. The two "mt_io_flags & MT_IO_SLOTS_MASK" arming checks become bitmap_empty(td->active_slots, td->maxcontacts). Move MT_IO_FLAGS_RUNNING back to bit 0. It was bumped to bit 32 by the same commit to leave the low byte for the slot bits; with the slot bits gone it fits in bit 0 again, which also keeps it within the unsigned long on 32-bit.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64365 In the Linux kernel, the following vulnerability has been resolved: HID: letsketch: fix UAF on inrange_timer at driver unbind letsketch_driver does not provide a .remove callback, but letsketch_probe() arms a per-device timer: timer_setup(&data->inrange_timer, letsketch_inrange_timeout, 0); The timer is re-armed from letsketch_raw_event() with a 100 ms timeout on every pen-in-range report, and its callback dereferences data->input_tablet to deliver a synthetic BTN_TOOL_PEN release. letsketch_data is allocated with devm_kzalloc(), and its input_dev fields are devm-allocated via letsketch_setup_input_tablet(). On device unbind (USB unplug or rmmod), the HID core runs its default teardown and devm cleanup frees both letsketch_data and the input devices. Because no .remove callback exists, nothing drains the timer first: if raw_event armed it within ~100 ms of the unbind, the pending timer fires on freed memory. This is a UAF read of data and of data->input_tablet, followed by input_report_key() / input_sync() into the freed input_dev. The same problem can occur on the probe error path: if hid_hw_start() enabled I/O on an always-poll-quirk device and then failed, raw_event may have armed the timer before devm releases data. Fix by adding a .remove callback that calls hid_hw_stop() first. hid_hw_stop() synchronously kills the URBs that deliver raw_event(), so once it returns no path can re-arm the timer. timer_shutdown_sync() then drains any in-flight callback and permanently disables further mod_timer() calls. Apply the same timer_shutdown_sync() in the probe error path so the timer is guaranteed not to outlive data.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64368 In the Linux kernel, the following vulnerability has been resolved: mm/slab: do not limit zeroing to orig_size when only red zoning is enabled When init (zeroing) on allocation is requested, for kmalloc() we generally have to zero the full object size even if a smaller size is requested, in order to provide krealloc()'s __GFP_ZERO guarantees. But if we track the requested size, krealloc() uses that information to do the right thing, so we can zero only the requested size. With red zoning also enabled, any extra size became part of the red zone, so it must not be zeroed and thus we must zero only the requested size. However the current check is imprecise, and will trigger also when only SLAB_RED_ZONE is enabled without SLAB_STORE_USER (which enables tracking the requested size). This means enabling red zoning alone can compromise krealloc()'s __GFP_ZERO contract. Fix this by using slub_debug_orig_size() instead, which is the exact check for whether the requested size is tracked. We don't need to care if red zoning is also enabled or not. Also update and expand the comment accordingly.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64369 In the Linux kernel, the following vulnerability has been resolved: s390: Revert support for DCACHE_WORD_ACCESS load_unaligned_zeropad() reads eight bytes from unaligned addresses and may cross page boundaries. It handles exceptions which may happen if reading from the second page results in an exception. For pages which are donated to the Ultravisor for secure execution purposes the do_secure_storage_access() exception handler however does not handle such exceptions correctly. Such an exception may result in an endless exception loop which will never be resolved. An attempt to fix this [1] turned out to be not sufficient. For now revert load_unaligned_zeropad() until this problem has been resolved in a proper way. Note that the implementation of load_unaligned_zeropad() itself is correct. The revert is just a temporary workaround until there is complete fix for secure storage access exceptions. [1] commit b00be77302d7 ("s390/mm: Add missing secure storage access fixups for donated memory")

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64372 In the Linux kernel, the following vulnerability has been resolved: cpufreq: pcc: fix use-after-free and double free in _OSC evaluation pcc_cpufreq_do_osc() calls acpi_evaluate_object() twice for the two-phase _OSC negotiation. Between the two calls it freed output.pointer but left output.length unchanged. Since acpi_evaluate_object() treats a non-zero length with a non-NULL pointer as an existing buffer to write into, the second call wrote into freed memory (use-after-free). The subsequent kfree(output.pointer) at out_free then freed the same pointer a second time (double free). Reset output.pointer to NULL and output.length to ACPI_ALLOCATE_BUFFER after freeing the first result, so ACPICA allocates a fresh buffer for each phase independently.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64376 In the Linux kernel, the following vulnerability has been resolved: firmware_loader: fix device reference leak in firmware_upload_register() firmware_upload_register() -> fw_create_instance() -> device_initialize() After fw_create_instance() succeeds, the lifetime of the embedded struct device is expected to be managed through the device core reference counting, since fw_create_instance() has already called device_initialize(). In firmware_upload_register(), if alloc_lookup_fw_priv() fails after fw_create_instance() succeeds, the code reaches free_fw_sysfs and frees fw_sysfs directly instead of releasing the device reference with put_device(). This may leave the reference count of the embedded struct device unbalanced, resulting in a refcount leak. The issue was identified by a static analysis tool I developed and confirmed by manual review. Fix this by using put_device(fw_dev) in the failure path and letting fw_dev_release() handle the final cleanup, instead of freeing the instance directly from the error path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64377 In the Linux kernel, the following vulnerability has been resolved: cpufreq: qcom-cpufreq-hw: Fix possible double free qcom_cpufreq.data is allocated with devm_kzalloc() in probe() as an array of per-domain data. qcom_cpufreq_hw_cpu_init() stores a pointer to one element of this array in policy->driver_data. qcom_cpufreq_hw_cpu_exit() currently calls kfree() on policy->driver_data. This is not valid because the memory is devm-managed. For the first domain, this can free the devm-managed allocation while the devres entry is still active, leading to a possible double free when the platform device is later detached. For other domains, the pointer may refer to an element inside the array rather than the allocation base. Remove the kfree(data) call and let devres release qcom_cpufreq.data. This issue was found by a static analysis tool I am developing.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64379 In the Linux kernel, the following vulnerability has been resolved: smb: client: mask server-provided mode to 07777 in modefromsid When modefromsid is active, parse_dacl() applies the server-provided sub_auth[2] value from the NFS mode SID to cf_mode without masking to 07777. Apply the correct masking, same as in the read path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64382 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix double-free in SMB2_open() replay A response-bearing attempt can return a replayable error and free its response buffer. If SMB2_open_init() fails before the next send, cleanup retains the previous buffer type and frees that response again. Reset response bookkeeping before each attempt to prevent the stale free.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64383 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix double-free in SMB2_flush() replay SMB2_flush() keeps its response buffer bookkeeping across replay attempts. If a replayable flush response is received and the retry then fails before cifs_send_recv() stores a replacement response, flush_exit will free the stale response pointer a second time. Reinitialize resp_buftype and rsp_iov at the top of the replay loop so cleanup only acts on response state produced by the current attempt. This fixes a double-free without changing replay handling for successful requests.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64384 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix change notify replay double-free A response-bearing attempt can return a replayable error and free its response buffer. If SMB2_notify_init() fails before the next send, cleanup retains the previous buffer type and frees that response again. Reset response bookkeeping before each attempt to prevent the stale free.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64385 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix double-free in SMB2_ioctl() replay A response-bearing attempt can return a replayable error and free its response buffer. If SMB2_ioctl_init() fails before the next send, cleanup retains the previous buffer type and frees that response again. Reset response bookkeeping before each attempt to prevent the stale free.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64386 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix query_info() replay double-free A response-bearing attempt can return a replayable error and free its response buffer. If SMB2_query_info_init() fails before the next send, cleanup retains the previous buffer type and frees that response again. Reset response bookkeeping before each attempt to prevent the stale free.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64387 In the Linux kernel, the following vulnerability has been resolved: smb: client: fix query directory replay double-free A response-bearing attempt can return a replayable error and free its response buffer. If SMB2_query_directory_init() fails before the next send, cleanup retains the previous buffer type and frees that response again. Reset response bookkeeping before each attempt to prevent the stale free.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64390 In the Linux kernel, the following vulnerability has been resolved: ksmbd: track the connection owning a byte-range lock SMB2_LOCK adds each granted byte-range lock to both the file lock list and the lock list of the connection which handled the request. The final close and durable handle paths, however, remove the connection list entry while holding fp->conn->llist_lock. With SMB3 multichannel, the connection handling the LOCK request can be different from the connection which opened the file. The entry can therefore be removed under a different spinlock from the one protecting the list it belongs to. A concurrent traversal can then access freed struct ksmbd_lock and struct file_lock objects. Record the connection owning each lock's clist entry and hold a reference to it while the entry is linked. Use that connection and its llist_lock for unlock, rollback, close, and durable preserve. Durable reconnect assigns the new connection as the owner when publishing the locks again.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64401 In the Linux kernel, the following vulnerability has been resolved: smb: client: resolve SWN tcon from live registrations cifs_swn_notify() looks up a witness registration by id under cifs_swnreg_idr_mutex, drops the mutex, and then uses the registration's cached tcon pointer. That pointer is not a lifetime reference, and it is not a stable representative once cifs_get_swn_reg() lets multiple tcons for the same net/share name share one registration id. A same-share second mount can keep the cifs_swn_reg alive after the first tcon unregisters and is freed. The registration then still points at the freed first tcon, so taking tc_lock or incrementing tc_count through swnreg->tcon only moves the use-after-free earlier. Taking tc_lock while holding cifs_swnreg_idr_mutex also violates the documented CIFS lock order. Fix this by making the registration store only the stable witness identity: id, net name, share name, and notify flags. When a notify arrives, copy that identity under cifs_swnreg_idr_mutex, drop the mutex, then find and pin a live witness tcon that currently matches the net/share pair under the normal cifs_tcp_ses_lock -> tc_lock order. The notification path uses that pinned tcon directly and drops the reference when done. Registration and unregister messages now use the live tcon passed by the caller instead of a cached tcon in the registration. The final unregister send is folded into cifs_swn_unregister() while the registration is still protected by cifs_swnreg_idr_mutex. This removes the previous find/drop/reacquire raw-pointer window. The release path only removes the idr entry and frees the stable identity strings. This preserves the intended one-registration/many-tcon behavior: a registration id represents a net/share pair, and notify handling acts on a live representative selected at use time. It also preserves CLIENT_MOVE ordering for the representative tcon because the old-IP unregister is sent before cifs_swn_register() sends the new-IP register.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64402 In the Linux kernel, the following vulnerability has been resolved: coresight: ultrasoc-smb: Fix OOB write in smb_sync_perf_buffer() When the SMB sink is used as a perf AUX sink, smb_update_buffer() calls smb_sync_perf_buffer() to copy hardware trace data into the perf AUX ring buffer pages. It derives pg_idx = head >> PAGE_SHIFT from @head, which is handle->head, and indexes dst_pages[pg_idx]. The pg_idx %= nr_pages normalization is only applied after the first loop iteration. This leaves the initial page index underived from the buffer size, which can result in an out-of-bounds write past dst_pages[] when head exceeds the AUX buffer size. Normalize head modulo the AUX buffer size before deriving the page index and offset, mirroring tmc_etr_sync_perf_buffer().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64404 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: ISO: avoid NULL deref of conn in iso_conn_big_sync() iso_conn_big_sync() drops the socket lock to call hci_get_route() and then re-acquires it, but dereferences iso_pi(sk)->conn->hcon afterwards without re-checking that conn is still valid. While the lock is dropped, the connection can be torn down under the same socket lock: iso_disconn_cfm() -> iso_conn_del() -> iso_chan_del() sets iso_pi(sk)->conn to NULL (and the broadcast teardown path can also clear conn->hcon on its own). When iso_conn_big_sync() re-acquires the lock and reads conn->hcon, conn may be NULL, causing a NULL pointer dereference (hcon is the first member of struct iso_conn). This path is reached from iso_sock_recvmsg() for a PA-sync broadcast sink socket (BT_SK_DEFER_SETUP | BT_SK_PA_SYNC), so the dropped-lock window can race with connection teardown driven by controller events. Re-validate iso_pi(sk)->conn and its hcon after re-acquiring the socket lock and bail out if the connection went away, as already done in the sibling iso_sock_rebind_bc().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64406 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: fix UAF in bt_accept_dequeue() bt_accept_get() takes a temporary reference before dropping the accept queue lock. bt_accept_dequeue() currently drops that reference before bt_accept_unlink(), leaving only the queue reference. bt_accept_unlink() drops the queue reference. The subsequent sock_hold() therefore accesses freed memory if it was the final reference, as observed by KASAN during listening L2CAP socket cleanup. Retain the temporary queue-walk reference through unlink and hand it to the caller on success. Drop it explicitly on the closed and not-yet-connected paths.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64407 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: btnxpuart: Fix out-of-bounds firmware read in nxp_recv_fw_req_v3() During the v3 firmware download the controller sends a v3_data_req with a 32 bit offset and a 16 bit len. nxp_recv_fw_req_v3() checks only the lower bound of the offset and then sends firmware from that offset. nxpdev->fw_dnld_v3_offset = offset - nxpdev->fw_v3_offset_correction; serdev_device_write_buf(nxpdev->serdev, nxpdev->fw->data + nxpdev->fw_dnld_v3_offset, len); Nothing checks that fw_dnld_v3_offset + len stays within nxpdev->fw->size, so a controller that asks for an offset or length past the firmware image makes the driver read past the end of nxpdev->fw->data and send that memory back over UART. nxp_recv_fw_req_v1() already bounds the same write. Add the equivalent check to the v3 path, reject the request when it falls outside the firmware image, and zero len on the error path so the fw_v3_prev_sent bookkeeping at free_skb stays consistent.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64409 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: btmtksdio: fix infinite loop in btmtksdio_txrx_work() Every once in a while we see a hung btmtksdio_flush() task: INFO: task kworker/u17:0:189 blocked for more than 122 seconds. __cancel_work_timer+0x3f4/0x460 cancel_work_sync+0x1c/0x2c btmtksdio_flush+0x2c/0x40 hci_dev_open_sync+0x10c4/0x2190 [..] It all boils down to incorrect time_is_before_jiffies() usage in btmtksdio_txrx_work(). The btmtksdio_txrx_work() loop is expected to be terminated if running for longer than 5*HZ. However the timeout check is twisted: time_is_before_jiffies(old_jiffies + 5*HZ) evaluates to true when old_jiffies + 5*HZ is in the past i.e. when a timeout has occurred. Using OR with time_is_before_jiffies(txrx_timeout) means that: - before the 5-second timeout: the condition is `int_status || false`, so it loops as long as there are pending interrupts. - after the 5-second timeout: the condition becomes `int_status || true`, which is always true. When the loop becomes infinite btmtksdio_txrx_work() loop never terminates and never releases the SDIO host. Fix loop termination condition to actually enforce a 5*HZ timeout.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64417 In the Linux kernel, the following vulnerability has been resolved: mm: shrinker: fix NULL pointer dereference in debugfs shrinker_debugfs_add() creates both "count" and "scan" debugfs files unconditionally. That assumes every shrinker implements both count_objects() and scan_objects(), which is not guaranteed. For example, the xen-backend shrinker sets count_objects() but leaves scan_objects() NULL, so writing to its scan file calls through a NULL function pointer and panics the kernel: BUG: kernel NULL pointer dereference, address: 0000000000000000 RIP: 0010:0x0 Code: Unable to access opcode bytes at 0xffffffffffffffd6. Call Trace: <TASK> shrinker_debugfs_scan_write+0x12e/0x270 full_proxy_write+0x5f/0x90 vfs_write+0xde/0x420 ? filp_flush+0x75/0x90 ? filp_close+0x1d/0x30 ? do_dup2+0xb8/0x120 ksys_write+0x68/0xf0 ? filp_flush+0x75/0x90 do_syscall_64+0xb3/0x5b0 entry_SYSCALL_64_after_hwframe+0x76/0x7e The count path has the same issue in principle if a shrinker omits count_objects(). To fix it, only create "count" and "scan" debugfs files when the corresponding callbacks are present.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64418 In the Linux kernel, the following vulnerability has been resolved: mm: shrinker: fix shrinker_info teardown race with expansion expand_shrinker_info() iterates all visible memcgs under shrinker_mutex, including memcgs that have not finished ->css_online() yet. Once pn->shrinker_info has been published, teardown must stay serialized with expand_shrinker_info() until that memcg is either fully online or no longer visible to iteration. Today alloc_shrinker_info() breaks that rule by dropping shrinker_mutex before freeing a partially initialized shrinker_info array, which may cause the following race: CPU0 CPU1 ==== ==== css_create --> list_add_tail_rcu(&css->sibling, &parent_css->children); online_css --> mem_cgroup_css_online --> alloc_shrinker_info --> alloc node0 info rcu_assign_pointer(C->node0->shrinker_info, old0) alloc node1 info -> FAIL -> goto err mutex_unlock(shrinker_mutex) shrinker_alloc() --> shrinker_memcg_alloc --> mutex_lock(shrinker_mutex) expand_shrinker_info --> mem_cgroup_iter see the memcg expand_one_shrinker_info --> old0 = C->node0->shrinker_info memcpy(new->unit, old0->unit, ...); free_shrinker_info --> kvfree(old0); /* double free !! */ kvfree_rcu(old0, rcu); The same problem exists later in mem_cgroup_css_online(). If alloc_shrinker_info() succeeds but a subsequent objcg allocation fails, the free_objcg -> free_shrinker_info() unwind path tears down the already published pn->shrinker_info arrays without shrinker_mutex. The expand_one_shrinker_info() can race with that teardown in the same way, leading to use-after-free or double-free of the old shrinker_info. Fix this by serializing shrinker_info teardown with shrinker_mutex, and by keeping alloc_shrinker_info() error cleanup inside the locked section.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64419 In the Linux kernel, the following vulnerability has been resolved: mm/shrinker: do not hold RCU lock in shrinker_debugfs_count_show() Reading the debugfs "count" file of a memcg-aware shrinker can sleep inside an RCU read-side critical section: BUG: sleeping function called from invalid context at kernel/cgroup/rstat.c:421 RCU nest depth: 1, expected: 0 css_rstat_flush mem_cgroup_flush_stats zswap_shrinker_count shrinker_debugfs_count_show shrinker_debugfs_count_show() invokes the ->count_objects() callback under rcu_read_lock(). The zswap callback flushes memcg stats via css_rstat_flush(), which may sleep, so it must not run under RCU. The RCU lock is not needed here. mem_cgroup_iter() takes RCU internally and returns a memcg holding a css reference (dropped on the next iteration or by mem_cgroup_iter_break()), so the memcg stays alive without it. The shrinker is kept alive by the open debugfs file: shrinker_free() removes the debugfs entries via debugfs_remove_recursive(), which waits for in-flight readers to drain, before call_rcu(..., shrinker_free_rcu_cb). The sibling "scan" handler already invokes the sleeping ->scan_objects() callback with no RCU section. Drop the rcu_read_lock()/rcu_read_unlock().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64420 In the Linux kernel, the following vulnerability has been resolved: mfd: cros_ec: Delay dev_set_drvdata() until probe success If ec_device_probe() fails, cros_ec_class_release releases memory for the cros_ec_dev structure. However, because the drvdata was already set, sub-drivers like cros_ec_typec can still retrieve the stale pointer via the platform device. This leads to a use-after-free when cros_ec_typec attempts to access &typec->ec->ec->dev on a device that has already been released. Move dev_set_drvdata() to ensure that the pointer is only made available once all initialization steps have succeeded. sysfs: cannot create duplicate filename '/class/chromeos/cros_ec' Call trace: sysfs_do_create_link_sd+0x94/0xdc sysfs_create_link+0x30/0x44 device_add_class_symlinks+0x90/0x13c device_add+0xf0/0x50c ec_device_probe+0x150/0x4f0 platform_probe+0xa0/0xe0 ... BUG: KASAN: invalid-access in __memcpy+0x44/0x230 Write at addr f5ffff809e2d33ac by task kworker/u32:5/125 Pointer tag: [f5], memory tag: [fe] Tainted : [W]=WARN, [O]=OOT_MODULE Hardware name: Google Navi unprovisioned 0x7FFFFFFF/sku0 board/sku3 Workqueue: events_unbound deferred_probe_work_func Call trace: __memcpy+0x44/0x230 cros_ec_check_features+0x60/0xcc [cros_ec_proto] cros_typec_probe+0xe8/0x6e0 [cros_ec_typec] platform_probe+0xa0/0xe0

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64421 In the Linux kernel, the following vulnerability has been resolved: media: nxp: imx8-isi: Fix use-after-free on remove KASAN reports a slab-use-after-free in __media_entity_remove_link() during rmmod of imx8_isi: BUG: KASAN: slab-use-after-free in __media_entity_remove_link+0x608/0x650 Read of size 2 at addr ffff0000d47cb02a by task rmmod/724 Call trace: __media_entity_remove_link+0x608/0x650 __media_entity_remove_links+0x78/0x144 __media_device_unregister_entity+0x150/0x280 media_device_unregister_entity+0x48/0x68 v4l2_device_unregister_subdev+0x158/0x300 v4l2_async_unbind_subdev_one+0x22c/0x358 v4l2_async_nf_unbind_all_subdevs+0xfc/0x1c0 v4l2_async_nf_unregister+0x5c/0x14c mxc_isi_remove+0x124/0x2a0 [imx8_isi] Allocated by task 249: __kmalloc_noprof+0x27c/0x690 mxc_isi_crossbar_init+0x22c/0x560 [imx8_isi] Freed by task 724: kfree+0x1e4/0x5b0 mxc_isi_crossbar_cleanup+0x34/0x80 [imx8_isi] mxc_isi_remove+0x11c/0x2a0 [imx8_isi] The problem is that mxc_isi_remove() calls mxc_isi_crossbar_cleanup() before mxc_isi_v4l2_cleanup(). The crossbar cleanup frees the media entity pads, but the subsequent v4l2 cleanup still tries to remove media links that reference those pads. Fix this by calling mxc_isi_v4l2_cleanup() before mxc_isi_crossbar_cleanup() to ensure all media entities are properly unregistered while the pads are still valid.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64425 In the Linux kernel, the following vulnerability has been resolved: io_uring/io-wq: re-check IO_WQ_BIT_EXIT for each linked work item commit 10dc95939817 ("io_uring/io-wq: check IO_WQ_BIT_EXIT inside work run loop") fixed the obvious case where io_worker_handle_work() took one exit-bit snapshot before draining pending work, but the fix stops one level too early. io_worker_handle_work() now re-checks IO_WQ_BIT_EXIT in its outer work run loop, yet it still snapshots that bit once before processing a whole dependent linked-work chain. If io_wq_exit_start() sets IO_WQ_BIT_EXIT after the first linked item has started, the remaining linked items can still reuse stale do_kill = false, skip IO_WQ_WORK_CANCEL, and continue running after exit has begun. Move the check further inside, so it covers linked items too. Note: this is a syzbot special as it loves setting up tons of slow linked work on weird devices like msr that take forever to read, and immediately close the ring. Exit then takes a long time.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64428 In the Linux kernel, the following vulnerability has been resolved: gpio: sch: use raw_spinlock_t in the irq startup path sch_irq_unmask() enables the GPIO IRQ and then updates the controller state through sch_irq_mask_unmask(), which takes sch->lock with spin_lock_irqsave(). The callback can be reached from irq_startup() while setting up a requested IRQ. That path is not sleepable, but on PREEMPT_RT a regular spinlock_t becomes a sleeping lock. This issue was found by our static analysis tool and then manually reviewed against the current tree. The grounded PoC kept the request_threaded_irq() -> __setup_irq() -> irq_startup() -> sch_irq_unmask() -> sch_irq_mask_unmask() carrier and used the original spin_lock_irqsave(&sch->lock) edge. Lockdep reported: BUG: sleeping function called from invalid context hardirqs last disabled at ... __setup_irq.constprop.0 ... [vuln_msv] sch_rt_spin_lock_irqsave+0x1c/0x30 [vuln_msv] sch_irq_mask_unmask.constprop.0+0x31/0x70 [vuln_msv] __setup_irq.constprop.0+0xd/0x30 [vuln_msv] Convert the SCH controller lock to raw_spinlock_t. The same lock is also used by the GPIO direction and value callbacks, but those critical sections only update MMIO-backed GPIO registers and do not contain sleepable operations. Keeping this register lock non-sleeping is therefore appropriate for the irqchip callbacks and does not change the GPIO-side locking contract.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64429 In the Linux kernel, the following vulnerability has been resolved: gpio: eic-sprd: use raw_spinlock_t in the irq startup path sprd_eic_irq_unmask() enables the GPIO IRQ and then updates controller state through sprd_eic_update(), which takes sprd_eic->lock with spin_lock_irqsave(). The callback can be reached from irq_startup() while setting up a requested IRQ. That path is not sleepable, but on PREEMPT_RT a regular spinlock_t becomes a sleeping lock. This issue was found by our static analysis tool and then manually reviewed against the current tree. The grounded PoC kept the request_threaded_irq() -> __setup_irq() -> irq_startup() -> sprd_eic_irq_unmask() -> sprd_eic_update() carrier and used the original spin_lock_irqsave(&sprd_eic->lock) edge. Lockdep BUG: sleeping function called from invalid context hardirqs last disabled at ... __setup_irq.constprop.0 ... [vuln_msv] sprd_rt_spin_lock_irqsave+0x1c/0x30 [vuln_msv] sprd_eic_update.constprop.0+0x48/0x90 [vuln_msv] sprd_eic_irq_unmask.constprop.0+0x35/0x50 [vuln_msv] __setup_irq.constprop.0+0xd/0x30 [vuln_msv] Convert the Spreadtrum EIC controller lock to raw_spinlock_t. The locked section only serializes MMIO register updates and does not contain sleepable operations, so keeping it non-sleeping is appropriate for the irqchip callbacks.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64430 In the Linux kernel, the following vulnerability has been resolved: NTB: epf: Avoid calling pci_irq_vector() from hardirq context ntb_epf_vec_isr() calls pci_irq_vector() in hardirq context to derive the vector number. pci_irq_vector() calls msi_get_virq() that takes a mutex and can therefore trigger "scheduling while atomic" splats: BUG: scheduling while atomic: kworker/u33:0/55/0x00010001 ... Call trace: ... schedule+0x38/0x110 schedule_preempt_disabled+0x28/0x50 __mutex_lock.constprop.0+0x848/0x908 __mutex_lock_slowpath+0x18/0x30 mutex_lock+0x4c/0x60 msi_domain_get_virq+0xe8/0x138 pci_irq_vector+0x2c/0x60 ntb_epf_vec_isr+0x28/0x120 [ntb_hw_epf] __handle_irq_event_percpu+0x70/0x3a8 handle_irq_event+0x48/0x100 handle_edge_irq+0x100/0x1c8 ... Cache the Linux IRQ number for vector 0 when vectors are allocated and use it as a base in the ISR. Running the ISR in a threaded IRQ handler would also avoid the problem, but that would be unnecessary here.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64432 In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: validate Dirty Page Table capacity in log_replay copy_lcns In the analysis pass of $LogFile journal replay, log_replay() copies LCNs from each action log record into an existing Dirty Page Table (DPT) entry without bounding the destination index. A crafted NTFS image with DPT entry lcns_follow=1 and an action log record with lcns_follow=2 produces a kernel slab out-of-bounds write at mount time: BUG: KASAN: slab-out-of-bounds in log_replay+0x654c/0xdb60 Write of size 8 at addr ffff8880095e1040 by task mount Two attacker-controlled fields can drive j+i past the allocated page_lcns[] array: 1. dp->lcns_follow (capacity) can be smaller than lrh->lcns_follow. 2. lrh->target_vcn may be smaller than dp->vcn, making the u64 subtraction wrap to a huge size_t. Validate target VCN delta and per-record LCN count against the DPT entry capacity, bail via the existing out: cleanup label with -EINVAL. This mirrors the bounds-check pattern added in commit b2bc7c44ed17 ("fs/ntfs3: Fix slab-out-of-bounds read in DeleteIndexEntryRoot") and commit 0ca0485e4b2e ("fs/ntfs3: validate rec->used in journal-replay file record check").

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64433 In the Linux kernel, the following vulnerability has been resolved: Bluetooth: MGMT: Fix UAF of hci_conn_params in add_device_complete add_device_complete() runs from the hci_cmd_sync_work kworker, which holds only hci_req_sync_lock and *not* hci_dev_lock. It calls hci_conn_params_lookup() and then dereferences the returned object (params->flags) without taking hci_dev_lock: params = hci_conn_params_lookup(hdev, &cp->addr.bdaddr, le_addr_type(cp->addr.type)); ... device_flags_changed(NULL, hdev, &cp->addr.bdaddr, cp->addr.type, hdev->conn_flags, params ? params->flags : 0); hci_conn_params_lookup() walks hdev->le_conn_params and is documented to require hdev->lock. A concurrent MGMT_OP_REMOVE_DEVICE (remove_device()), which does run under hci_dev_lock, can call hci_conn_params_free() to list_del() and kfree() the very object the lookup returned, so the subsequent params->flags read touches freed memory [0]. Hold hci_dev_lock() across the hci_conn_params_lookup() and the read of params->flags (and the matching event emission) so the lookup result cannot be freed by a concurrent remove_device() before it is used, honouring the locking contract of hci_conn_params_lookup(). [0]: (trailing page/memory-state dump trimmed) BUG: KASAN: slab-use-after-free in add_device_complete+0x358/0x3d8 net/bluetooth/mgmt.c:7671 Read of size 1 at addr ffff000017ab26c1 by task kworker/u9:8/388 CPU: 1 UID: 0 PID: 388 Comm: kworker/u9:8 Not tainted 7.0.11 #20 PREEMPT Hardware name: linux,dummy-virt (DT) Workqueue: hci0 hci_cmd_sync_work Call trace: show_stack+0x2c/0x3c arch/arm64/kernel/stacktrace.c:499 (C) __dump_stack lib/dump_stack.c:94 [inline] dump_stack_lvl+0xb4/0xd4 lib/dump_stack.c:120 print_address_description mm/kasan/report.c:378 [inline] print_report+0x118/0x5d8 mm/kasan/report.c:482 kasan_report+0xb0/0xf4 mm/kasan/report.c:595 __asan_report_load1_noabort+0x20/0x2c mm/kasan/report_generic.c:378 add_device_complete+0x358/0x3d8 net/bluetooth/mgmt.c:7671 hci_cmd_sync_work+0x14c/0x240 net/bluetooth/hci_sync.c:334 process_one_work+0x628/0xd38 kernel/workqueue.c:3289 process_scheduled_works kernel/workqueue.c:3372 [inline] worker_thread+0x7a8/0xac0 kernel/workqueue.c:3453 kthread+0x39c/0x444 kernel/kthread.c:436 ret_from_fork+0x10/0x20 arch/arm64/kernel/entry.S:860 Allocated by task 3401: kasan_save_stack+0x3c/0x64 mm/kasan/common.c:57 kasan_save_track+0x20/0x3c mm/kasan/common.c:78 kasan_save_alloc_info+0x40/0x54 mm/kasan/generic.c:570 poison_kmalloc_redzone mm/kasan/common.c:398 [inline] __kasan_kmalloc+0xd4/0xd8 mm/kasan/common.c:415 kasan_kmalloc include/linux/kasan.h:263 [inline] __kmalloc_cache_noprof+0x1b0/0x458 mm/slub.c:5385 kmalloc_noprof include/linux/slab.h:950 [inline] kzalloc_noprof include/linux/slab.h:1188 [inline] hci_conn_params_add+0x10c/0x4b0 net/bluetooth/hci_core.c:2279 hci_conn_params_set net/bluetooth/mgmt.c:5162 [inline] add_device+0x5b4/0xa54 net/bluetooth/mgmt.c:7755 hci_mgmt_cmd net/bluetooth/hci_sock.c:1721 [inline] hci_sock_sendmsg+0x10b4/0x1dd0 net/bluetooth/hci_sock.c:1841 sock_sendmsg_nosec net/socket.c:727 [inline] __sock_sendmsg+0xe0/0x128 net/socket.c:742 sock_write_iter+0x250/0x390 net/socket.c:1195 new_sync_write fs/read_write.c:595 [inline] vfs_write+0x66c/0xab0 fs/read_write.c:688 ksys_write+0x1fc/0x24c fs/read_write.c:740 __do_sys_write fs/read_write.c:751 [inline] __se_sys_write fs/read_write.c:748 [inline] __arm64_sys_write+0x70/0xa4 fs/read_write.c:748 __invoke_syscall arch/arm64/kernel/syscall.c:35 [inline] invoke_syscall+0x84/0x2a8 arch/arm64/kernel/syscall.c:49 el0_svc_common.constprop.0+0xe4/0x294 arch/arm64/kernel/syscall.c:132 do_el0_svc+0x44/0x5c arch/arm64/kernel/syscall.c:151 el0_svc+0x38/0xac arch/arm64/kernel/entry-common.c:724 el0t_64_sync_handler+0xa0/0xe4 arch/arm64/kernel/entry-common.c:743 el0t_64_sync+0x198/0x19c arch/arm64/kernel/entry.S:596 Freed by task 3740: kasan_save_stack+0x3c/0x64 ---truncated---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64438 In the Linux kernel, the following vulnerability has been resolved: crypto: qat - fix VF2PF work teardown race in adf_disable_sriov() The VF2PF interrupt handler queues PF-side response work that stores a raw pointer to per-VF state (struct adf_accel_vf_info). Currently, adf_disable_sriov() destroys per-VF mutexes and frees vf_info without stopping new VF2PF work or waiting for in-flight workers to complete. A concurrently scheduled or already queued worker can then dereference freed memory. This manifests as a use-after-free when KASAN is enabled: BUG: KASAN: null-ptr-deref in mutex_lock+0x76/0xe0 Write of size 8 at addr 0000000000000260 by task kworker/24:2/... Workqueue: qat_pf2vf_resp_wq adf_iov_send_resp [intel_qat] Call Trace: kasan_report+0x119/0x140 mutex_lock+0x76/0xe0 adf_gen4_pfvf_send+0xd4/0x1f0 [intel_qat] adf_recv_and_handle_vf2pf_msg+0x290/0x360 [intel_qat] adf_iov_send_resp+0x8c/0xe0 [intel_qat] process_one_work+0x6ac/0xfd0 worker_thread+0x4dd/0xd30 kthread+0x326/0x410 ret_from_fork+0x33b/0x670 Add a PF-local flag, vf2pf_disabled, that gates work queueing, worker processing, and interrupt re-enabling during teardown. Set this flag atomically with the hardware interrupt mask inside adf_disable_all_vf2pf_interrupts(). After masking, synchronize the AE cluster MSI-X interrupt and flush the PF response workqueue before tearing down per-VF locks and state so all in-flight work completes before vf_info is destroyed. Introduce adf_enable_all_vf2pf_interrupts() to clear the flag and unmask all VF2PF interrupts under the same lock when SR-IOV is re-enabled. This ensures the software flag and hardware state transition atomically on both the enable and disable paths.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64440 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB write in HT_caps_handler() HT_caps_handler() iterates pIE->length bytes and writes into HT_caps.u.HT_cap[], which is a fixed 26-byte array (sizeof struct HT_caps_element). Because pIE->length is a raw u8 from an over-the-air 802.11 AssocResponse frame and is never validated, a malicious AP can set it up to 255, causing up to 229 bytes of out-of-bounds writes into adjacent fields of struct mlme_ext_info. Truncate the iteration count to the size of HT_caps.u.HT_cap using umin() so that data from a longer-than-expected IE is silently ignored rather than written out of bounds, preserving interoperability with APs that pad the element. An early return on oversized IEs was considered but rejected: it would bypass the pmlmeinfo->HT_caps_enable = 1 assignment that precedes the loop, silently disabling HT mode for APs that append extra bytes to the HT Capabilities IE.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64441 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB reads in rtw_get_sec_ie(), rtw_get_wapi_ie(), and rtw_get_wps_attr() Three IE/attribute parsing functions have missing bounds checks. rtw_get_sec_ie() and rtw_get_wapi_ie() iterate over a raw IE buffer without verifying that the header bytes (tag + length) are within the remaining buffer before reading them. Additionally, rtw_get_sec_ie() compares the 4-byte WPA OUI at cnt+2 without checking that at least 6 bytes remain, and rtw_get_wapi_ie() compares a 4-byte WAPI OUI at cnt+6 without checking that at least 10 bytes remain. rtw_get_wps_attr() reads wps_ie[0] and wps_ie+2 unconditionally at entry, before verifying that wps_ielen is large enough to contain the 6-byte WPS IE header (element_id + length + 4-byte OUI). Inside the attribute loop, get_unaligned_be16() is called on attr_ptr and attr_ptr+2 without checking that 4 bytes remain in the buffer. Add a cnt+2 bounds check before each loop body in rtw_get_sec_ie() and rtw_get_wapi_ie(), guard each multi-byte comparison with a minimum IE length requirement, add a wps_ielen < 6 early return in rtw_get_wps_attr(), and add a 4-byte bounds check in its inner loop.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64442 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB reads in IE loops in issue_assocreq() and join_cmd_hdl() Two IE parsing loops are missing the header bounds checks before they dereference pIE->length: - issue_assocreq() walks pmlmeinfo->network.ies to build the association request. If the stored IE data ends with only an element_id byte and no length byte, pIE->length is read one byte past the end of the buffer. - join_cmd_hdl() walks pnetwork->ies during station join and has the same problem under the same conditions. Both buffers are filled from AP beacon and probe-response frames, so a malicious AP that sends a truncated final IE can trigger the issue. Apply the two-guard pattern established in update_beacon_info(): 1. Break if fewer than sizeof(*pIE) bytes remain. 2. Break if the IE's declared data extends past the buffer end.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64443 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB read in update_beacon_info() IE loop The IE parsing loop in update_beacon_info() advances by (pIE->length + 2) each iteration but only guards on i < len. When a malicious AP sends a Beacon whose last IE has only one byte remaining in the frame (the element_id byte lands at len-1), the loop reads pIE->length from one byte past the allocated receive buffer. Additionally, even when the header bytes are in bounds, pIE->length itself can extend the data window beyond len, passing a truncated IE to the handler functions. Add two guards at the top of the loop body: 1. Break if fewer than sizeof(*pIE) bytes remain (can't read header). 2. Break if the IE's declared data extends past len. Also replace i += (pIE->length + 2) with i += sizeof(*pIE) + pIE->length for consistency with the sizeof(*pIE) guards added above.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64444 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB read in OnAssocRsp() IE loop The IE parsing loop in OnAssocRsp() advances by (pIE->length + 2) each iteration but only guards on i < pkt_len. When a malicious AP sends an AssocResponse whose last IE has only one byte remaining in the frame (the element_id byte lands at pkt_len-1), the loop reads pIE->length from pframe[pkt_len], which is one byte past the allocated receive buffer. Additionally, even when the header bytes are in bounds, pIE->length itself can extend the data window beyond pkt_len, silently passing a truncated IE to the handler functions. Add two guards at the top of the loop body: 1. Break if fewer than sizeof(*pIE) bytes remain (can't read header). 2. Break if the IE's declared data extends past pkt_len.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64445 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix WEP length underflow and OOB read in OnAuth() OnAuth() has two bugs in the shared-key authentication path. When the Privacy bit is set, rtw_wep_decrypt() is called without verifying that the frame is long enough to contain a valid WEP IV and ICV. Inside rtw_wep_decrypt(), length is computed as: length = len - WLAN_HDR_A3_LEN - iv_len and then passed as (length - 4) to crc32_le(). If len is less than WLAN_HDR_A3_LEN + iv_len + icv_len (32 bytes), length - 4 is negative and, after the implicit cast to size_t, causes crc32_le() to read far beyond the frame buffer. Add a minimum length check before accessing the IV field and calling the decryption path. When processing a seq=3 response, rtw_get_ie() stores the Challenge Text IE length in ie_len, but the subsequent memcmp() always reads 128 bytes regardless of ie_len. IEEE 802.11 mandates a challenge text of exactly 128 bytes; reject any IE whose length field differs, matching the check already applied to OnAuthClient().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64446 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix heap buffer overflow in rtw_cfg80211_set_wpa_ie() supplicant_ie is a 256-byte array in struct security_priv. The WPA and WPA2 IE copy paths use: memcpy(padapter->securitypriv.supplicant_ie, &pwpa[0], wpa_ielen + 2); where wpa_ielen is the raw IE length field (u8, 0-255). When a local user supplies a connect request via nl80211 with a crafted WPA IE of length 255, wpa_ielen + 2 equals 257, overflowing the 256-byte buffer by one byte into the adjacent last_mic_err_time field. rtw_parse_wpa_ie() does not prevent this: its length consistency check compares *(wpa_ie+1) against (u8)(wpa_ie_len-2), which is (u8)(255) == 255 when wpa_ie_len = 257, so the check passes silently. Add explicit bounds checks for both the WPA and WPA2 paths before the memcpy, rejecting any IE whose total size (wpa_ielen + 2) exceeds the supplicant_ie buffer.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64454 In the Linux kernel, the following vulnerability has been resolved: usb: dwc3: run gadget disconnect from sleepable suspend context dwc3_gadget_suspend() takes dwc->lock with IRQs disabled and then calls dwc3_disconnect_gadget(). For async callbacks that helper only uses plain spin_unlock()/spin_lock(), so the gadget ->disconnect() callback still runs with IRQs disabled and any sleepable callback trips Lockdep. This issue was found by our static analysis tool and then manually reviewed against the current tree. The grounded PoC kept the dwc3_gadget_suspend() -> dwc3_disconnect_gadget() -> gadget_driver->disconnect() chain, and Lockdep reported: BUG: sleeping function called from invalid context gadget_disconnect+0x21/0x39 [vuln_msv] dwc3_gadget_suspend.constprop.0+0x2b/0x42 [vuln_msv] Keep the disconnect callback selection in one common helper, but add a sleepable suspend-side wrapper which snapshots the callback under dwc->lock and then runs it after spin_unlock_irqrestore(). The regular event path still uses the existing spin_unlock()/spin_lock() window.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64456 In the Linux kernel, the following vulnerability has been resolved: hwrng: virtio: clamp device-reported used.len at copy_data() random_recv_done() stores the device-reported used.len directly into vi->data_avail. copy_data() then indexes vi->data[] using vi->data_idx (advanced by previous copy_data() calls) and issues a memcpy() without re-validating either value against the posted buffer size sizeof(vi->data) (SMP_CACHE_BYTES bytes, typically 32 or 64). A malicious or buggy virtio-rng backend can set used.len beyond sizeof(vi->data), steering the memcpy() past the end of the inline array into adjacent kmalloc-1k slab bytes. hwrng_fillfn() mixes those bytes into the guest RNG, and guest root can also observe them directly via /dev/hwrng. Concrete impact is inside the guest: - Memory-safety / hardening: any virtio-rng backend that over-reports used.len causes the driver to read past vi->data into unrelated slab contents. hwrng_fillfn() is a kernel thread that runs as soon as the device is probed; no guest userspace interaction is required to first-trigger the OOB. - Cross-boundary leak (confidential-compute threat model): a malicious hypervisor cooperating with a malicious or compromised guest root userspace can use /dev/hwrng as a leak channel for guest-kernel heap data. The host sets a large used.len, guest root reads /dev/hwrng, and the returned bytes contain guest kernel slab contents that were adjacent to vi->data. In practice, confidential-compute guests (SEV-SNP, TDX) usually disable virtio-rng entirely, so this path is narrow, but the fix is still worth carrying because the underlying memory-safety bug contaminates the guest RNG on any host. KASAN confirms the OOB on a 7.1-rc4 guest whose virtio-rng backend has been patched to report used.len = 0x10000: BUG: KASAN: slab-out-of-bounds in virtio_read+0x394/0x5d0 Read of size 64 at addr ffff88800ae0ba20 by task hwrng/52 Call Trace: __asan_memcpy+0x23/0x60 virtio_read+0x394/0x5d0 hwrng_fillfn+0xb2/0x470 kthread+0x2cc/0x3a0 Allocated by task 1: probe_common+0xa5/0x660 virtio_dev_probe+0x549/0xbc0 The buggy address belongs to the object at ffff88800ae0b800 which belongs to the cache kmalloc-1k of size 1024 The buggy address is located 0 bytes to the right of allocated 544-byte region [ffff88800ae0b800, ffff88800ae0ba20) Same class of bug as commit c04db81cd028 ("net/9p: Fix buffer overflow in USB transport layer"), which hardened usb9pfs_rx_complete() against unchecked device-reported length in the USB 9p transport. With the clamp at point of use and array_index_nospec() in place, the same harness boots cleanly: copy_data() returns zero for the bogus report, the device-supplied bytes after data_idx are discarded, and the driver issues a fresh request.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64458 In the Linux kernel, the following vulnerability has been resolved: mm/damon/ops-common: handle extreme intervals in damon_hot_score() Fix three issues in damon_hot_score() that comes from wrong handling of extreme (zero or too high) monitoring intervals user setup. When the user sets sampling interval zero, damon_max_nr_accesses(), which is called from damon_hot_score(), causes a divide-by-zero. Needless to say, it is a problem. When the user sets the aggregation interval zero, the function returns zero. It is wrong, since the real maximum nr_acceses in the setup should be one. Worse yet, it can cause another divide-by-zero from its caller, damon_hot_score(), since it uses damon_max_nr_accesses() return value as a denominator. When the user sets the aggregation interval very high, damon_hot_score() could return a value out of [0, DAMOS_MAX_SCORE] range. Since the return value is used as an index to the regions_score_histogram array, which is DAMOS_MAX_SCORE+1 size, it causes out of bounds array access. The issues can be relatively easily reproduced like below. The sysfs write permission is required, though. # ./damo start --damos_action lru_prio --damos_quota_space 100M \ --damos_quota_interval 1s # cd /sys/kernel/mm/damon/admin/kdamonds/0 # echo 0 > contexts/0/monitoring_attrs/intervals/sample_us # echo 0 > contexts/0/monitoring_attrs/intervals/aggr_us # echo commit > state # dmesg [...] [ 131.329762] Oops: divide error: 0000 [#1] SMP NOPTI [...] [ 131.336089] RIP: 0010:damon_hot_score+0x27/0xd0 [...] Fix the divide-by-zero intervals problems by explicitly handling the zero intervals in damon_max_nr_accesses(). Fix the out-of-bound array access by applying [0, DAMOS_MAX_SCORE] bounds before returning from damon_hot_score(). The issue was discovered [1] by Sashiko.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64461 In the Linux kernel, the following vulnerability has been resolved: PCI: mediatek: Fix IRQ domain leak when port fails to enable When mtk_pcie_enable_port() fails, mtk_pcie_port_free() removes the port from pcie->ports and frees the port structure. However, the IRQ domains set up earlier by mtk_pcie_init_irq_domain() are never freed. Fix this by refactoring mtk_pcie_irq_teardown() into a per-port helper, mtk_pcie_irq_teardown_port(), and calling it from mtk_pcie_setup() when mtk_pcie_enable_port() fails. Since the IRQ teardown must only happen in the probe error path (during resume, child devices may have active MSI mappings and the NOIRQ context prohibits sleeping locks), mtk_pcie_enable_port() is changed to return an error code so callers can distinguish the two paths and act accordingly. This issue was reported by Sashiko while reviewing the EcoNet EN7528 SoC support series.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64462 In the Linux kernel, the following vulnerability has been resolved: PCI: altera: Fix resource leaks on probe failure The chained IRQ handler is set during probe, but is only removed during the driver remove(). If pci_host_probe() fails, the handler and INTx IRQ domain remain set even though the devm-managed host bridge storage containing struct altera_pcie will be released, leaving the handler with a stale data pointer. Interrupts are also enabled before pci_host_probe() is called. If probe fails after that point, the controller interrupt source should be disabled before the chained handler and INTx domain are removed. So set the chained handler only after the INTx domain has been created. Disable controller interrupts during IRQ teardown, and tear the IRQ setup down if pci_host_probe() fails. [mani: commit log]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64468 In the Linux kernel, the following vulnerability has been resolved: binder: fix UAF in binder_free_transaction() In binder_free_transaction(), the t->to_proc is read under the t->lock. However, once the t->lock is dropped, the to_proc can die in parallel. This leads to a use-after-free error when we attempt to acquire its inner lock right afterwards: ================================================================== BUG: KASAN: slab-use-after-free in _raw_spin_lock+0xe4/0x1a0 Write of size 4 at addr ffff00001125da70 by task B/672 CPU: 20 UID: 0 PID: 672 Comm: B Not tainted 7.1.0-rc6-00284-g8e65320d91cd #4 PREEMPT Hardware name: linux,dummy-virt (DT) Call trace: _raw_spin_lock+0xe4/0x1a0 binder_free_transaction+0x8c/0x320 binder_send_failed_reply+0x21c/0x2f8 binder_thread_release+0x488/0x7e0 binder_ioctl+0x12c0/0x29a0 [...] Allocated by task 675: __kmalloc_cache_noprof+0x174/0x444 binder_open+0x118/0xb70 do_dentry_open+0x374/0x1040 vfs_open+0x58/0x3bc [...] Freed by task 212: __kasan_slab_free+0x58/0x80 kfree+0x1a0/0x4a4 binder_proc_dec_tmpref+0x32c/0x5e0 binder_deferred_func+0xc48/0x104c process_one_work+0x53c/0xbc0 [...] ================================================================== To prevent this, pin the target thread (t->to_thread) to guarantee the target process remains alive. Undelivered transactions without a target thread are already safe, as the target process can only be the current context in those paths.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64469 In the Linux kernel, the following vulnerability has been resolved: binder: fix UAF in binder_thread_release() When a thread exits, binder_thread_release() walks its transaction stack to clear the t->from and t->to_proc that correspond with the exiting thread. However, a process dying in parallel might attempt to kfree some of these transactions. And if one of them has no associated t->to_proc, the t->to_proc->inner_lock will not be acquired. This means that transaction accesses in binder_thread_release() after t->to_proc has been cleared might race with binder_free_transaction() and cause a use-after-free error as reported by KASAN: ================================================================== BUG: KASAN: slab-use-after-free in binder_thread_release+0x5d0/0x798 Write of size 8 at addr ffff000016627500 by task X/715 CPU: 17 UID: 0 PID: 715 Comm: X Not tainted 7.1.0-rc5-00149-g8fde5d1d47f6 #30 PREEMPT Hardware name: linux,dummy-virt (DT) Call trace: binder_thread_release+0x5d0/0x798 binder_ioctl+0x12c0/0x299c [...] Allocated by task 717 on cpu 18 at 67.267803s: __kasan_kmalloc+0xa0/0xbc __kmalloc_cache_noprof+0x174/0x444 binder_transaction+0x554/0x8150 binder_thread_write+0xa30/0x4354 binder_ioctl+0x20f0/0x299c [...] Freed by task 202 on cpu 18 at 90.416221s: __kasan_slab_free+0x58/0x80 kfree+0x1a0/0x4a4 binder_free_transaction+0x150/0x294 binder_send_failed_reply+0x398/0x6d8 binder_release_work+0x3e4/0x4ec binder_deferred_func+0xbd8/0x104c [...] ================================================================== In order to avoid this, make sure that binder_free_transaction() reads the t->to_proc under the transaction lock. This will serialize the transaction release with the accesses in binder_thread_release(). Plus, it matches the documented locking rules for @to_proc.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64472 In the Linux kernel, the following vulnerability has been resolved: vfio/mlx5: Fix racy bitfields and tighten struct layout Bitfield operations are not atomic, they use a read-modify-write pattern, therefore we should be careful not to pack bitfields that can be concurrently updated into the same storage unit. This split takes a binary approach: flags that are only modified pre/post open/close remain bitfields, flags modified from user action, including actions that reach across to another device (ex. reset) use dedicated storage units. Note mlx5_vhca_page_tracker.status is relocated to fill the alignment hole this split exposes. Bitfield justifications: migrate_cap: written only in mlx5vf_cmd_set_migratable() at probe chunk_mode: written only in mlx5vf_cmd_set_migratable() at probe mig_state_cap: written only in mlx5vf_cmd_set_migratable() at probe Dedicated storage units: mdev_detach: written in the VF attach/detach event notifier mlx5fv_vf_event() at runtime log_active: written in mlx5vf_start_page_tracker()/ mlx5vf_stop_page_tracker() during runtime dirty tracking deferred_reset: written in mlx5vf_state_mutex_unlock()/ mlx5vf_pci_aer_reset_done() during runtime reset handling is_err: set by tracker error handling and dirty-log polling at runtime object_changed: set by tracker event handling and cleared by dirty-log polling at runtime

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64473 In the Linux kernel, the following vulnerability has been resolved: vfio: Remove device debugfs before releasing devres VFIO device debugfs files created with debugfs_create_devm_seqfile() store a devres allocated debugfs_devm_entry as inode private data. vfio_unregister_group_dev() currently calls vfio_device_del() before vfio_device_debugfs_exit(), but device_del() releases devres. This can leave debugfs entries visible with stale inode private data while unregister waits for userspace references to drain. Remove the per-device debugfs tree before vfio_device_del(). The debugfs view is diagnostic only, so losing it at the start of unregister is preferable to preserving entries whose backing storage may already have been released. Complete the teardown by clearing the per-device debugfs root after removal. This matches the global debugfs root cleanup and prevents future users from mistaking a removed dentry for a live debugfs tree during the remainder of unregister.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64474 In the Linux kernel, the following vulnerability has been resolved: vfio: prevent infinite loop in vfio_mig_get_next_state() on blocked arc vfio_mig_get_next_state() walks vfio_from_fsm_table[] one step at a time, looping to skip optional states the device does not support until *next_fsm is supported. A blocked transition is encoded as VFIO_DEVICE_STATE_ERROR, which the trailing return reports as -EINVAL. The skip loop does not account for the ERROR sentinel. state_flags_table[ERROR] is ~0U and vfio_from_fsm_table[ERROR][*] is ERROR, so once *next_fsm becomes ERROR the loop condition stays true and *next_fsm never changes. The blocked arcs STOP_COPY -> PRE_COPY and STOP_COPY -> PRE_COPY_P2P map to ERROR yet pass the support check on a precopy-capable device, causing the loop to spin forever while holding the driver state mutex. This can result in a soft lockup, and a panic with softlockup_panic set. Terminate the skip loop on the ERROR sentinel so a blocked transition falls through to the existing return and reports -EINVAL.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64475 In the Linux kernel, the following vulnerability has been resolved: vfio/pci: Release the VGA arbiter client on register_device() failure The re-order in the Fixes commit below displaced vfio_pci_vga_init() as the last failure point of what is now vfio_pci_core_register_device() without introducing an unwind for the VGA arbiter registration. In current kernels this is mostly benign because vfio_pci_set_decode() only uses pci_dev state, but the original failure path could leave a callback with a freed vdev cookie. The stale registration also becomes unsafe again once the callback follows drvdata to the vfio device. Add the required VGA unwind callout.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64476 In the Linux kernel, the following vulnerability has been resolved: vfio/pci: Latch disable_idle_d3 per device When disable_idle_d3 was introduced in vfio-pci, it directly manipulated the device power state with pci_set_power_state(). There were no refcounts to maintain or balanced operations, we could unconditionally bring the device to D0 and conditionally move it to D3hot. Therefore the module parameter was made writable. Later, in commit c61302aa48f7 ("vfio/pci: Move module parameters to vfio_pci.c"), as part of the vfio-pci-core split, the writable aspect of the module parameter was nullified. The parameter value could still be changed through sysfs, but the vfio-pci driver latched the values into vfio-pci-core globals at module init. Loading the vfio-pci module, or unloading and reloading, with non-default or different values could change the globals relative to existing devices bound to vfio-pci variant drivers. Runtime PM was introduced in commit 7ab5e10eda02 ("vfio/pci: Move the unused device into low power state with runtime PM"), which marks the point where power states became refcounted. PM get and put operations need to be balanced, but the same module operations noted above can change the global variables relative to those devices already bound to vfio-pci variant drivers. This introduces a window where PM operations can now become unbalanced. To resolve this with a narrow footprint for stable backports, the disable_idle_d3 flag is latched into the vfio_pci_core_device at the time of initialization, such that the device always operates with a consistent value. NB. vfio_pci_dev_set_try_reset() now unconditionally raises the runtime PM usage count around bus reset to account for disable_idle_d3 becoming a per-device rather than global flag. When this flag is set, the additional get/put pair is harmless and allows continued use of the shared vfio_pci_dev_set_pm_runtime_get() helper.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64478 In the Linux kernel, the following vulnerability has been resolved: ALSA: usb-audio: avoid kobject path lookup in DualSense match The DualSense jack-detection input handler verifies that a matching input device belongs to the same physical controller by building kobject path strings for both the input device and the USB audio device, then comparing the path prefix. This was observed when a weak physical connection caused the controller to rapidly disconnect and reconnect. During that repeated hotplug, snd_dualsense_ih_match() can run while the controller's USB device is being disconnected. kobject_get_path() walks ancestor kobjects and dereferences their names; if the USB device kobject name is no longer valid, this can fault in strlen(): RIP: 0010:strlen+0x10/0x30 Call Trace: kobject_get_path+0x34/0x150 snd_dualsense_ih_match+0x49/0xd0 [snd_usb_audio] input_register_device+0x566/0x6a0 ps_probe+0xb89/0x1590 [hid_playstation] The same ownership check can be done without building kobject path strings. The input device is parented below the HID device, USB interface and USB device, so walking the input device parent chain and comparing against the mixer USB device preserves the check without dereferencing kobject names during disconnect.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64479 In the Linux kernel, the following vulnerability has been resolved: ALSA: seq: Fix uninitialised heap leak in snd_seq_event_dup() snd_seq_event_dup() copies an incoming event into a pool cell and, in the UMP-enabled build, clears the trailing cell->ump.raw.extra word that the memcpy() did not cover. The guard deciding whether to clear it compares the copied size against sizeof(cell->event): memcpy(&cell->ump, event, size); if (size < sizeof(cell->event)) cell->ump.raw.extra = 0; For a legacy (non-UMP) event, size == sizeof(struct snd_seq_event) == sizeof(cell->event), so the condition is false and the extra word keeps stale data. The cell pool is allocated with kvmalloc() (not zeroed) and cells are reused via a free list, so that word holds uninitialised heap or leftover event data. When such a cell is delivered to a UMP client (client->midi_version > 0) that set SNDRV_SEQ_FILTER_NO_CONVERT -- so the legacy event reaches it unconverted -- snd_seq_read() reads it out as the larger struct snd_seq_ump_event and copies the stale word to user space, a 4-byte kernel heap infoleak to an unprivileged /dev/snd/seq client. Compare against sizeof(cell->ump) instead, so the trailing word is zeroed for every event shorter than the UMP cell.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64481 In the Linux kernel, the following vulnerability has been resolved: ALSA: hda/cs35l41: Fix firmware load work teardown cs35l41_hda creates ALSA controls whose private data points at the cs35l41_hda object. The firmware load control can also queue fw_load_work. Those controls are not removed on component unbind, and device remove only cancels fw_load_work through cs35l41_remove_dsp(). That helper is skipped when halo_initialized is false. With firmware_autostart disabled, a firmware load can be requested before the DSP has been initialized. If the component or device is removed before the queued work runs, the worker can run after teardown and dereference driver state that is no longer valid. Track the created controls and remove them on unbind so no new control callback can reach the driver data or queue more work. Then cancel fw_load_work to drain any request that was already queued. Also cancel the work unconditionally during device remove before runtime PM teardown.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64482 In the Linux kernel, the following vulnerability has been resolved: ALSA: gus: check snd_ctl_new1() return value snd_ctl_new1() can return NULL when memory allocation fails. snd_gf1_pcm_volume_control() does not check the return value before dereferencing kctl->id.index, which can lead to a NULL pointer dereference. Add a NULL check after snd_ctl_new1() and return -ENOMEM if it fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64484 In the Linux kernel, the following vulnerability has been resolved: ALSA: es1938: check snd_ctl_new1() return value snd_ctl_new1() can return NULL when memory allocation fails. snd_es1938_mixer() does not check the return value before dereferencing the pointer, which can lead to a NULL pointer dereference. Add a NULL check after snd_ctl_new1() and return -ENOMEM if it fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64488 In the Linux kernel, the following vulnerability has been resolved: ALSA: aoa: check snd_ctl_new1() return value snd_ctl_new1() can return NULL when memory allocation fails. In layout.c, the function does not check the return value before dereferencing ctl->id.name or passing to aoa_snd_ctl_add(), which can lead to a NULL pointer dereference. Add NULL checks after snd_ctl_new1() calls and return early if any fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64489 In the Linux kernel, the following vulnerability has been resolved: ALSA: ymfpci: check snd_ctl_new1() return value snd_ctl_new1() can return NULL when memory allocation fails. snd_ymfpci_create_spdif_controls() does not check the return value before dereferencing kctl->id.device, which can lead to a NULL pointer dereference. Add NULL checks after snd_ctl_new1() calls and return -ENOMEM if any fails.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64493 In the Linux kernel, the following vulnerability has been resolved: iio: pressure: mpl115: fix runtime PM leak on read error mpl115_read_raw() takes a runtime PM reference with pm_runtime_get_sync() before reading the processed pressure or raw temperature, but on the read error path it returns without calling pm_runtime_put_autosuspend(). Each failed read therefore leaks a runtime PM reference and prevents the device from autosuspending. Drop the reference before checking the return value so both the success and error paths are balanced.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64494 In the Linux kernel, the following vulnerability has been resolved: iio: light: gp2ap002: fix runtime PM leak on read error gp2ap002_read_raw() calls pm_runtime_get_sync() before reading the lux value, but if gp2ap002_get_lux() fails, it returns directly. This skips the pm_runtime_put_autosuspend() call at the "out" label, permanently leaking a runtime PM reference and preventing the device from autosuspending. Replace the direct return with a "goto out" to ensure the reference is properly dropped on the error path.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64495 In the Linux kernel, the following vulnerability has been resolved: iio: gyro: bmg160: bail out when bandwidth/filter is not in table bmg160_get_filter() walks bmg160_samp_freq_table[] looking for the entry matching the bw_bits value read from the chip: for (i = 0; i < ARRAY_SIZE(bmg160_samp_freq_table); ++i) { if (bmg160_samp_freq_table[i].bw_bits == bw_bits) break; } *val = bmg160_samp_freq_table[i].filter; If no entry matches, i ends up equal to the array size and the next line reads one slot past the end. bmg160_set_filter() has the same shape, driven by 'val' instead of bw_bits. smatch flags both: drivers/iio/gyro/bmg160_core.c:204 bmg160_get_filter() error: buffer overflow 'bmg160_samp_freq_table' 7 <= 7 drivers/iio/gyro/bmg160_core.c:222 bmg160_set_filter() error: buffer overflow 'bmg160_samp_freq_table' 7 <= 7 Return -EINVAL when no entry matches. The set_filter() path is reachable from userspace via the sysfs in_anglvel_filter_low_pass_3db_frequency interface, so userspace can trivially trigger the out-of-bounds read with a value that is not in bmg160_samp_freq_table[].filter.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64496 In the Linux kernel, the following vulnerability has been resolved: iio: event: Fix event FIFO reset race `iio_event_getfd()` creates the event file descriptor with `anon_inode_getfd()`, which allocates a new fd, creates the anonymous file and installs it in the process fd table before returning to the caller. The IIO code resets the event FIFO after `anon_inode_getfd()` has returned, but before `IIO_GET_EVENT_FD_IOCTL` has copied the fd number to userspace. But since fd tables are shared between threads, another thread can guess the newly allocated fd number and issue a `read()` on it as soon as the fd has been installed. This means the `kfifo_to_user()` in `iio_event_chrdev_read()` can run in parallel with the `kfifo_reset_out()` in `iio_event_getfd()`. The kfifo documentation says that `kfifo_reset_out()` is only safe when it is called from the reader thread and there is only one concurrent reader. Otherwise it is dangerous and must be handled in the same way as `kfifo_reset()`. If that happens, `kfifo_to_user()` can advance the FIFO `out` index based on state from before the reset, after the reset has already moved the `out` index to the current `in` index. That can leave the FIFO with an `out` index past the `in` index. A later `read()` can then see an underflowed FIFO length and copy more data than the event FIFO buffer contains. This can result in an out-of-bounds read and leak adjacent kernel memory to userspace. Move the FIFO reset before `anon_inode_getfd()`. At that point the event fd is marked busy, but the new fd has not been installed yet, so userspace cannot access it while the FIFO is reset.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64497 In the Linux kernel, the following vulnerability has been resolved: iio: chemical: scd30: Cleanup initializations and fix sign-extension bug Include linux/bitfield.h for FIELD_GET(). Create new macros for bit manipulation in combination with manual bit manipulation being replaced with FIELD_GET(). The current variable declaration and initializations are barely readable and use comma separations across multiple lines. Refactor the initializations so that mantissa and exp have separate declarations and sign gets initialized later. In addition (and due to the nature of the cleanup), fix a sign-extension bug where, float32 would get bitwise anded with ~BIT(31) (which is 0xFFFFFFFF7FFFFFFF) which corrupted the exponent.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64500 In the Linux kernel, the following vulnerability has been resolved: iio: adc: lpc32xx: Initialize completion before requesting IRQ In the report from Jaeyoung Chung: "lpc32xx_adc_probe() in drivers/iio/adc/lpc32xx_adc.c registers its interrupt handler with devm_request_irq() before it initializes st->completion with init_completion(). If an interrupt arrives after devm_request_irq() and before init_completion(), the handler calls complete() on an uninitialized completion, causing a kernel panic. The probe path, in lpc32xx_adc_probe(): iodev = devm_iio_device_alloc(&pdev->dev, sizeof(*st)); /* st kzalloc-zeroed */ ... retval = devm_request_irq(&pdev->dev, irq, lpc32xx_adc_isr, 0, LPC32XXAD_NAME, st); /* register handler */ ... init_completion(&st->completion); /* initialize completion */ lpc32xx_adc_isr() calls complete(): complete(&st->completion); If the device raises an interrupt before init_completion() runs, complete() acquires the uninitialized wait.lock and walks the zeroed task_list in swake_up_locked(). The zeroed task_list makes list_empty() return false, so swake_up_locked() dereferences a NULL list entry, triggering a KASAN wild-memory-access." Fix the chance of a spurious IRQ causing an uninitialized pointer dereference by moving init_completion() above devm_request_irq().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64503 In the Linux kernel, the following vulnerability has been resolved: iio: accel: kxsd9: fix runtime PM imbalance on write_raw() error kxsd9_write_raw() takes a runtime PM reference with pm_runtime_get_sync() but returns -EINVAL directly when a scale with a non-zero integer part is requested, skipping the matching pm_runtime_put_autosuspend(). This leaks a runtime PM usage-counter reference on every such write, after which the device can no longer autosuspend. Set the error code and fall through to the existing put instead of returning early.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64504 In the Linux kernel, the following vulnerability has been resolved: iio: accel: bmc150: clamp the device-reported FIFO frame count __bmc150_accel_fifo_flush() copies the number of samples the device reports in its hardware FIFO into an on-stack buffer u16 buffer[BMC150_ACCEL_FIFO_LENGTH * 3]; which is sized for at most BMC150_ACCEL_FIFO_LENGTH (32) samples. The frame count is read from the FIFO_STATUS register and only masked to its 7 valid bits: count = val & 0x7F; so it can be 0..127. The only other limit applied to it is the optional caller-supplied sample budget: if (samples && count > samples) count = samples; which does not constrain count on the flush-all path (samples == 0), and leaves it well above 32 whenever samples is larger. count samples are then transferred into buffer[]: bmc150_accel_fifo_transfer(data, (u8 *)buffer, count); bmc150_accel_fifo_transfer() reads count * 6 bytes through regmap, so a malfunctioning, malicious or counterfeit accelerometer (or an attacker tampering with the I2C/SPI bus) that reports up to 127 frames writes up to 762 bytes into the 192-byte buffer: a stack out-of-bounds write of up to 570 bytes that clobbers the stack canary, saved registers and the return address. Clamp count to BMC150_ACCEL_FIFO_LENGTH, the number of samples buffer[] is sized for, before the transfer, mirroring the watermark clamp already done in bmc150_accel_set_watermark(). A well-formed flush reports at most BMC150_ACCEL_FIFO_LENGTH frames, so legitimate devices are unaffected.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64505 In the Linux kernel, the following vulnerability has been resolved: usb: gadget: function: rndis: add length check for header Add a length check for the rndis header in rndis_rm_hdr, to ensure that MessageType, MessageLength, DataOffset, and DataLength fields are present before they are accessed.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64511 In the Linux kernel, the following vulnerability has been resolved: ACPI: NFIT: core: Fix possible NULL pointer dereference After commit 9b311b7313d6 ("ACPI: NFIT: Install Notify() handler before getting NFIT table"), acpi_nfit_probe() installs an ACPI notify handler for the NFIT device before checking the presence of the NFIT table. If that table is not there, 0 is returned without allocating the acpi_desc object and setting the driver data pointer of the NFIT device. If the platform firmware triggers an NFIT_NOTIFY_UC_MEMORY_ERROR notification on the NFIT device at that point, acpi_nfit_uc_error_notify() will dereference a NULL pointer. Prevent that from occurring by adding an acpi_desc check against NULL to acpi_nfit_uc_error_notify().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64524 In the Linux kernel, the following vulnerability has been resolved: drm/hyperv: validate resolution_count and fix WIN8 fallback A SYNTHVID_RESOLUTION_RESPONSE with resolution_count > 64 walks past the supported_resolution[SYNTHVID_MAX_RESOLUTION_COUNT] array in the parse loop. Bound resolution_count against the array size, folded into the existing zero-check. When the WIN10 resolution probe fails, the caller in hyperv_connect_vsp() left hv->screen_*_max / preferred_* unpopulated, which sets mode_config.max_width / max_height to 0 and makes drm_internal_framebuffer_create() reject every userspace framebuffer with -EINVAL. The pre-WIN10 branch had the same gap for preferred_width / preferred_height. Use a single post-probe fallback guarded by screen_width_max == 0 so both paths converge on the WIN8 defaults.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64525 In the Linux kernel, the following vulnerability has been resolved: xfrm: move policy_bydst RCU sync from per-netns .exit to .pre_exit The struct pernet_operations docstring in include/net/net_namespace.h explicitly warns against blocking RCU primitives in .exit handlers: Exit methods using blocking RCU primitives, such as synchronize_rcu(), should be implemented via exit_batch. [...] Please, avoid synchronize_rcu() at all, where it's possible. Note that a combination of pre_exit() and exit() can be used, since a synchronize_rcu() is guaranteed between the calls. xfrm_policy_fini() violates this: it calls synchronize_rcu() before freeing the policy_bydst hash tables (so no RCU reader is mid- traversal at free time), but runs from xfrm_net_ops.exit -- once per namespace -- so a cleanup_net() of N namespaces pays N full RCU grace periods serially. Use the documented pre_exit/exit split. Move the policy flush (and the workqueue drains it depends on) into a new .pre_exit handler; xfrm_policy_fini() then runs in .exit and frees the hash tables after the synchronize_rcu_expedited() that cleanup_net() guarantees between the two phases. Providing O(1) RCU grace periods per batch instead of O(N). Observed on Linux 6.18 with a workload doing unshare(CLONE_NEWNET) at ~13/sec sustained: cleanup_net() and the netns_wq rescuer kthread both stuck in xfrm_policy_fini()'s synchronize_rcu(), >300k struct net accumulated in the cleanup queue, Percpu in /proc/meminfo climbed to 130+ GB on 256-CPU hosts, and memcg OOMs followed. setup_net and __put_net counts were balanced, ruling out a refcount leak.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64527 In the Linux kernel, the following vulnerability has been resolved: drm/hyperv: validate VMBus packet size in receive callback hyperv_receive_sub() reads msg->vid_hdr.type and dispatches into one of four message-type branches without knowing how many bytes the host wrote into hv->recv_buf. The completion path then runs memcpy(hv->init_buf, msg, VMBUS_MAX_PACKET_SIZE), so the consumer that wakes on wait_for_completion_timeout() can read up to 16 KiB of residue from a prior message as if it were the response payload. Pass bytes_recvd into hyperv_receive_sub() and reject any packet that does not cover the pipe + synthvid header. A single switch on msg->vid_hdr.type then computes the type-specific payload size: the three completion-driving types (SYNTHVID_VERSION_RESPONSE, SYNTHVID_RESOLUTION_RESPONSE, SYNTHVID_VRAM_LOCATION_ACK) fall through to a shared exit that requires that size before memcpy/complete, while SYNTHVID_FEATURE_CHANGE validates its own payload and returns before reading is_dirt_needed. Unknown types are dropped. SYNTHVID_RESOLUTION_RESPONSE is variable length: the host fills resolution_count entries, not the full SYNTHVID_MAX_RESOLUTION_COUNT array. Validate the fixed prefix first so resolution_count can be read, bound it against the array, then require only the count-sized array, so the shorter responses the host actually sends are accepted. Only run the sub-handler when vmbus_recvpacket() returned success. The memcpy length is bytes_recvd, which is bounded by VMBUS_MAX_PACKET_SIZE only on a successful receive; on -ENOBUFS vmbus_recvpacket() instead reports the required length, which can exceed hv->recv_buf, so copying bytes_recvd would read and write past the 16 KiB buffers. Gating on the success return keeps the copy bounded. The nonzero-return path is itself a malformed-message case and is now logged rather than silently skipped; channel recovery is not attempted. Rejected packets are reported via drm_err_ratelimited() rather than silently dropped, matching the CoCo-hardened pattern in hv_kvp_onchannelcallback().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64528 In the Linux kernel, the following vulnerability has been resolved: tty: serial: samsung: Remove redundant port lock acquisition in rx helpers Sashiko identified a deadlock when the console flow is engaged [1]. When console flow control is enabled (UPF_CONS_FLOW), s3c24xx_serial_stop_tx() calls s3c24xx_serial_rx_enable() and s3c24xx_serial_start_tx() calls s3c24xx_serial_rx_disable(). The serial core framework invokes the .stop_tx() and .start_tx() callbacks with the port->lock spinlock already held. Furthermore, all internal driver paths that invoke stop_tx (such as the DMA TX completion handler s3c24xx_serial_tx_dma_complete() or the PIO TX IRQ handler s3c24xx_serial_tx_irq()) also acquire port->lock prior to calling it. (Note that s3c24xx_serial_start_tx() is only invoked by the serial core). However, s3c24xx_serial_rx_enable() and s3c24xx_serial_rx_disable() unconditionally attempt to acquire port->lock again using uart_port_lock_irqsave(). Since spinlocks are not recursive, this causes a deadlock on the same CPU when console flow control is engaged. Remove the redundant lock acquisition from both rx helper functions.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64530 In the Linux kernel, the following vulnerability has been resolved: net/sched: cls_api: Handle TC_ACT_CONSUMED in tcf_qevent_handle tcf_classify() can return TC_ACT_CONSUMED while the skb is held by the defragmentation engine (e.g. act_ct on out-of-order fragments). When that happens the skb is no longer owned by the caller and must not be touched again. tcf_qevent_handle() did not handle TC_ACT_CONSUMED: it fell through the switch and returned the skb to the caller as if classification had passed. The only qdisc that wires up qevents today is RED, via three call sites (qe_mark on RED_PROB_MARK/HARD_MARK, qe_early_drop on congestion_drop) red_enqueue() was continuing to operate on an skb it no longer owns in this case -- enqueueing it, dropping it, or updating statistics. Resulting in a UAF. tc qdisc add dev eth0 root handle 1: red ... qevent early_drop block 10 tc filter add block 10 ... action ct (with ct defrag enabled and traffic that produces out-of-order fragments, e.g. a fragmented UDP stream) Handle TC_ACT_CONSUMED in tcf_qevent_handle() the same way the ingress and egress fast paths do: treat it as stolen and return NULL without touching the skb. Unlike the TC_ACT_STOLEN case, the skb must not be dropped/freed here, as it is no longer owned by us.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64532 In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: bound NTFS_DE view.data_off in UpdateRecordData{Root,Allocation} In do_action()'s UpdateRecordDataRoot (fslog.c:3489) and UpdateRecordDataAllocation (fslog.c:3697) cases, the memmove destination is `Add2Ptr(e, le16_to_cpu(e->view.data_off))`, where e->view.data_off comes from an on-disk NTFS_DE inside an INDEX_ROOT or INDEX_BUFFER. Neither case validates view.data_off + dlen against e->size; the existing check_if_index_root / check_if_alloc_index helpers walk the entry chain and validate the entry's offset, but not its internal view fields. The neighbouring read sites (e.g., fs/ntfs3/index.c when iterating view entries) check view.data_off + view.data_size <= e->size. Apply the same bound at the two memmove sites. Reproduced under UML+KASAN on mainline 8d90b09e6741 via pr_warn-only probe instrumentation: with view.data_off forced to 0xFFFC, the memmove writes 32 bytes past the end of the NTFS_DE. This is similar in shape to Pavitra Jha's 2026-05-02 patch "fs/ntfs3: prevent oob in case UpdateRecordDataRoot" (<20260502105008.21827-1-jhapavitra98@gmail.com>) which proposes calling ntfs3_bad_de_range(); that helper does not exist in mainline. This patch uses inline checks.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64533 In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: validate lcns_follow in log_replay conversion log_replay() converts DIR_PAGE_ENTRY_32 records into DIR_PAGE_ENTRY records when replaying version 0 restart tables. During this conversion, the memmove() length is derived directly from the on-disk lcns_follow field: memmove(&dp->vcn, &dp0->vcn_low, 2 * sizeof(u64) + le32_to_cpu(dp->lcns_follow) * sizeof(u64)); check_rstbl() validates restart table structure, but does not constrain per-entry lcns_follow values relative to the entry size. A malformed filesystem image can provide an oversized lcns_follow value, causing the conversion memmove() to access memory beyond the bounds of the allocated restart table buffer. The same field is later used to bound iteration over page_lcns[], so validating lcns_follow during conversion also prevents downstream out-of-bounds access from the same malformed metadata. Compute the maximum valid lcns_follow from the already-validated restart table entry size and reject entries that exceed this bound. Reuse the existing t16/t32 scratch variables already declared in log_replay() to avoid introducing new declarations. [almaz.alexandrovich@paragon-software.com: fixed the conflicts]

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64536 In the Linux kernel, the following vulnerability has been resolved: staging: rtl8723bs: fix OOB reads in is_ap_in_tkip() IE loop The loop in is_ap_in_tkip() iterates over IEs without verifying that enough bytes remain before dereferencing the IE header or its payload: - pIE->element_id and pIE->length are read without checking that i + sizeof(*pIE) <= ie_length, so a truncated IE at the end of the buffer causes an OOB read. - For WLAN_EID_VENDOR_SPECIFIC the code compares pIE->data + 12, which requires pIE->length >= 16. For WLAN_EID_RSN it compares pIE->data + 8, requiring pIE->length >= 12. Neither requirement is checked. Add the missing IE header and payload bounds checks and guard each data access with an explicit pIE->length minimum, matching the pattern established in update_beacon_info().

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64537 In the Linux kernel, the following vulnerability has been resolved: bridge: cfm: reject invalid CCM interval at configuration time ccm_tx_work_expired() re-arms itself via queue_delayed_work() using the configured exp_interval converted by interval_to_us(). When exp_interval is BR_CFM_CCM_INTERVAL_NONE or out of range, interval_to_us() returns 0, causing the worker to fire immediately in a tight loop that allocates skbs until OOM. Fix this by validating exp_interval at configuration time: - Constrain IFLA_BRIDGE_CFM_CC_CONFIG_EXP_INTERVAL to the valid range [BR_CFM_CCM_INTERVAL_3_3_MS, BR_CFM_CCM_INTERVAL_10_MIN] in the netlink policy so userspace cannot set an invalid value. - Reject starting CCM TX in br_cfm_cc_ccm_tx() when exp_interval has not yet been configured (defaults to 0 from kzalloc).

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64538 In the Linux kernel, the following vulnerability has been resolved: ipv6: Fix null-ptr-deref in fib6_nh_mtu_change(). fib6_nh_mtu_change() re-fetches idev via __in6_dev_get(arg->dev) and dereferences idev->cnf.mtu6 without a NULL check. addrconf_ifdown() clears dev->ip6_ptr with RCU_INIT_POINTER() after rt6_disable_ip() has released tb6_lock, so the RA-driven MTU walk can observe a NULL idev and oops. The caller rt6_mtu_change_route() guards its own __in6_dev_get(), but this re-fetch is unguarded; nexthop-backed routes survive addrconf_ifdown()'s flush, so the walk still reaches it after ip6_ptr is nulled. Return 0 when idev is NULL, matching rt6_mtu_change_route() and the fib6_mtu() fix in commit 5ad509c1fdad ("ipv6: Fix null-ptr-deref in fib6_mtu()."). Oops: general protection fault, ... KASAN: null-ptr-deref in range [0x00000000000002a8-0x00000000000002af] RIP: 0010:fib6_nh_mtu_change+0x203/0x990 rt6_mtu_change_route+0x141/0x1d0 __fib6_clean_all+0xd0/0x160 rt6_mtu_change+0xb4/0x100 ndisc_router_discovery+0x24b5/0x2cb0 icmpv6_rcv+0x12e9/0x1710 ipv6_rcv+0x39b/0x410

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64542 In the Linux kernel, the following vulnerability has been resolved: ipv6: ndisc: fix NULL deref in accept_untracked_na() accept_untracked_na() re-fetches the inet6_dev with __in6_dev_get(dev) and dereferences idev->cnf.accept_untracked_na without a NULL check, even though its only caller ndisc_recv_na() already fetched and NULL-checked idev for the same device. Both reads of dev->ip6_ptr run in the same RCU read-side critical section, but a concurrent addrconf_ifdown() can clear dev->ip6_ptr between them: lowering the MTU below IPV6_MIN_MTU calls addrconf_ifdown() without the synchronize_net() that orders the unregister path, so the re-fetch returns NULL and oopses: BUG: KASAN: null-ptr-deref in ndisc_recv_na (net/ipv6/ndisc.c:974) Read of size 4 at addr 0000000000000364 Call Trace: <IRQ> ndisc_recv_na (net/ipv6/ndisc.c:974) icmpv6_rcv (net/ipv6/icmp.c:1193) ip6_protocol_deliver_rcu (net/ipv6/ip6_input.c:479) ip6_input_finish (net/ipv6/ip6_input.c:534) ip6_input (net/ipv6/ip6_input.c:545) ip6_mc_input (net/ipv6/ip6_input.c:635) ipv6_rcv (net/ipv6/ip6_input.c:351) </IRQ> It is reachable by an unprivileged user via a network namespace. Pass the caller's already validated idev instead of re-fetching it; the idev stays alive for the whole RCU critical section, so it is safe even after dev->ip6_ptr has been cleared.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64545 In the Linux kernel, the following vulnerability has been resolved: net, bpf: check master for NULL in xdp_master_redirect() xdp_master_redirect() dereferences the result of netdev_master_upper_dev_get_rcu() without a NULL check, but that helper returns NULL when the receiving device has no upper-master adjacency. The reach guard only checks netif_is_bond_slave(). On bond slave release bond_upper_dev_unlink() drops the upper-master adjacency before clearing IFF_SLAVE, so an XDP_TX reaching xdp_master_redirect() in that window still passes netif_is_bond_slave() while master is already NULL, and faults on master->flags at offset 0xb0: BUG: kernel NULL pointer dereference, address: 00000000000000b0 RIP: 0010:xdp_master_redirect (net/core/filter.c:4432) Call Trace: xdp_master_redirect (net/core/filter.c:4432) bpf_prog_run_generic_xdp (include/net/xdp.h:700) do_xdp_generic (net/core/dev.c:5608) __netif_receive_skb_one_core (net/core/dev.c:6204) process_backlog (net/core/dev.c:6319) __napi_poll (net/core/dev.c:7729) net_rx_action (net/core/dev.c:7792) handle_softirqs (kernel/softirq.c:622) __dev_queue_xmit (include/linux/bottom_half.h:33) packet_sendmsg (net/packet/af_packet.c:3082) __sys_sendto (net/socket.c:2252) Kernel panic - not syncing: Fatal exception in interrupt The missing check dates back to the original code; commit 1921f91298d1 ("net, bpf: fix null-ptr-deref in xdp_master_redirect() for down master") later added the master->flags read where the fault now lands but kept the unconditional deref. Check master for NULL before use; a NULL master is treated the same as one that is not up.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64550 In the Linux kernel, the following vulnerability has been resolved: net: qualcomm: rmnet: validate MAP frame length before ingress parsing When ingress deaggregation is disabled, rmnet_map_ingress_handler() passes the skb straight to __rmnet_map_ingress_handler(), skipping the length validation that rmnet_map_deaggregate() performs on the aggregated path. The parser then dereferences the MAP header and csum header/trailer based on the on-wire pkt_len without checking skb->len, so a short frame is read out of bounds: BUG: KASAN: slab-out-of-bounds in rmnet_map_checksum_downlink_packet Read of size 1 at addr ffff88801118ed00 by task exploit/147 Call Trace: ... rmnet_map_checksum_downlink_packet (drivers/net/ethernet/qualcomm/rmnet/rmnet_map_data.c:413) __rmnet_map_ingress_handler (drivers/net/ethernet/qualcomm/rmnet/rmnet_handlers.c:96) rmnet_rx_handler (drivers/net/ethernet/qualcomm/rmnet/rmnet_handlers.c:129) __netif_receive_skb_core.constprop.0 (net/core/dev.c:6089) netif_receive_skb (net/core/dev.c:6460) tun_get_user (drivers/net/tun.c:1955) tun_chr_write_iter (drivers/net/tun.c:2001) vfs_write (fs/read_write.c:688) ksys_write (fs/read_write.c:740) do_syscall_64 (arch/x86/entry/syscall_64.c:94) ... Factor that validation out of rmnet_map_deaggregate() into rmnet_map_validate_packet_len() and run it on the no-aggregation path too. The MAP header is bounds-checked first, since this path can receive a frame shorter than the header.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64552 In the Linux kernel, the following vulnerability has been resolved: virtio-net: fix len check in receive_big() receive_big() bounds the device-announced length by (big_packets_num_skbfrags + 1) * PAGE_SIZE. That is still too loose: add_recvbuf_big() sets sg[1] to start at offset sizeof(struct padded_vnet_hdr) into the first page, so the chain actually carries hdr_len + (PAGE_SIZE - sizeof(padded_vnet_hdr)) + big_packets_num_skbfrags * PAGE_SIZE bytes -- 20 bytes less than the check allows for the common hdr_len == 12 case. A malicious virtio backend can announce a len in that gap. page_to_skb() then walks one frag past the page chain, storing a NULL page->private into skb_shinfo()->frags[MAX_SKB_FRAGS], which is both an out-of-bounds write past the static frag array and a NULL frag handed up the rx path. Bound len by the size add_recvbuf_big() actually advertised.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64554 In the Linux kernel, the following vulnerability has been resolved: netfilter: bridge: fix stale prevhdr pointer in br_ip6_fragment() br_ip6_fragment() gets prevhdr, a pointer into the skb head, from ip6_find_1stfragopt(), then calls skb_checksum_help(). For a cloned skb skb_checksum_help() reallocates the head via pskb_expand_head(), leaving prevhdr dangling. It is later dereferenced in ip6_frag_next(), causing a use-after-free write. Save prevhdr's offset before skb_checksum_help() and recompute it after, like commit ef0efcd3bd3f ("ipv6: Fix dangling pointer when ipv6 fragment"). BUG: KASAN: slab-use-after-free in ip6_frag_next (net/ipv6/ip6_output.c:857) Write of size 1 at addr ffff888013ff5016 by task exploit/141 Call Trace: ... kasan_report (mm/kasan/report.c:595) ip6_frag_next (net/ipv6/ip6_output.c:857) br_ip6_fragment (net/ipv6/netfilter.c:212) nf_ct_bridge_post (net/bridge/netfilter/nf_conntrack_bridge.c:407) nf_hook_slow (net/netfilter/core.c:619) br_forward_finish (net/bridge/br_forward.c:66) __br_forward (net/bridge/br_forward.c:115) maybe_deliver (net/bridge/br_forward.c:191) br_flood (net/bridge/br_forward.c:245) br_handle_frame_finish (net/bridge/br_input.c:229) br_handle_frame (net/bridge/br_input.c:442) ... packet_sendmsg (net/packet/af_packet.c:3114) ... do_syscall_64 (arch/x86/entry/syscall_64.c:94) entry_SYSCALL_64_after_hwframe (arch/x86/entry/entry_64.S:121) Kernel panic - not syncing: Fatal exception in interrupt

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64555 In the Linux kernel, the following vulnerability has been resolved: KVM: arm64: nv: Fix SPSR_EL2 restore in kvm_hyp_handle_mops() kvm_hyp_handle_mops() resets the single-step state machine as part of rewinding state for a MOPS exception by modifying vcpu_cpsr() and writing the result directly into hardware. In the case of nested virtualization, vcpu_cpsr() is a synthetic value such that the rest of KVM can deal with vEL2 cleanly. That means the value requires translation before being written into hardware, which is unfortunately missing from the MOPS handler. Fix it by directly modifying SPSR_EL2 and avoiding the synthetic state altogether, which will be resynchronized on the next 'full' exit back to KVM.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

CVE-2026-64560 In the Linux kernel, the following vulnerability has been resolved: posix-cpu-timers: Prevent UAF caused by non-leader exec() race Wongi and Jungwoo decoded and reported a non-leader exec() related race which can result in an UAF: sys_timer_delete() exec() posix_cpu_timer_del() // Observes old leader p = pid_task(pid, pid_type); de_thread() switch_leader(); release_task(old_leader) __exit_signal(old_leader) sighand = lock(old_leader, sighand); posix_cpu_timers*_exit(); sighand = lock_task_sighand(p) unhash_task(old_leader); sh = lock(p, sighand) old_leader->sighand = NULL; unlock(sighand); (p->sighand == NULL) unlock(sh) return NULL; // Returns without action if(!sighand) return 0; free_posix_timer(); This is "harmless" unless the deleted timer was armed and enqueued in p->signal because on exec() a TGID targeted timer is inherited. As sys_timer_delete() freed the underlying posix timer object run_posix_cpu_timers() or any timerqueue related add/delete operations on other timers will access the freed object's timerqueue node, which results in an UAF. There is a similar problem vs. posix_cpu_timer_set(). For regular posix timers it just transiently returns -ESRCH to user space, but for the use case in do_cpu_nanosleep() it's the same UAF just that the k_itimer is allocated on the stack. Also posix_cpu_timer_rearm() fails to rearm the timer, which means it stops to expire. While debating solutions Frederic pointed out another problem: posix_cpu_timer_del(tmr) __exit_signal(p) posix_cpu_timers*_exit(p); unhash_task(p); p->sighand = NULL; sh = lock_task_sighand(p) sighand = p->sighand; if (!sighand) return NULL; lock(sighand); if (!sh) WARN_ON_ONCE(timer_queued(tmr)); On weakly ordered architectures it is not guaranteed that posix_cpu_timer_del() will observe the stores in posix_cpu_timers*_exit() when p->sighand is observed as NULL, which means the WARN() can be a false positive. Solve these issues by: 1) Changing the store in __exit_signal() to smp_store_release(). 2) Adding a smp_acquire__after_ctrl_dep() into the !sighand path of lock_task_sighand(). 3) Creating a helper function for looking up the task and locking sighand which does not return when sighand == NULL. Instead it retries the task lookup and only if that fails it gives up. 4) Using that helper in the three affected functions. #1/#2 ensures that the reader side which observes sighand == NULL also observes all preceeding stores, i.e. the stores in posix_cpu_timers*_exit() and the ones in unhash_task(). #3 ensures that the above described non-leader exec() situation is handled gracefully. When the task lookup returns the old leader, but sighand == NULL then it retries. In the non-leader exec() case the subsequent task lookup will observe the new leader due to #1/#2. In normal exit() scenarios the subsequent lookup fails. When the task lookup fails, the function also checks whether the timer is still enqueued and issues a warning if that's the case. Unfortunately there is nothing which can be done about it, but as the task is already not longer visible the timer should not be accessed anymore. This check also requires memory ordering, which is not provided when the first lookup fails. To achieve that the check is preceeded by a smp_rmb() which pairs with the smp_wmb() in write_seqlock() in __exit_signal(). That ensures that the stores in posix_cpu_timers*_exit() are visible. The history of the non-leader exec() issue goes back to the early days of posix CPU timers, which stored a pointer to the group leader task in the timer. That obviously fails when a non-leader exec() switches the leader. commit e0a70217107e ("posix-cpu-timers: workaround to suppress the problems with mt exec") added a temporary workaround for that in 2010 which surv ---truncated---

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

CVE-2026-64600 In the Linux kernel, the following vulnerability has been resolved: xfs: resample the data fork mapping after cycling ILOCK xfs_reflink_fill_{cow_hole,delalloc} are both presented with an inode, a data fork mapping, and a cow fork mapping. Unfortunately, these two helpers cycle the ILOCK to grab a transaction, which means that the mappings are stale as soon as we reacquire the ILOCK. Currently we refresh the cow fork mapping by re-calling xfs_find_trim_cow_extent, but we don't refresh the data fork mapping beforehand, which means that the xfs_bmap_trim_cow in that function queries the refcount btree about the wrong physical blocks and returns an inaccurate value in *shared. If *shared is now false, the directio write proceeds with a stale data fork mapping. Fix this by querying the data fork mapping if the sequence counter changes across the ILOCK cycle.

cmlserving-triton-runtime
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-workbench-python3.10-cuda
ml-runtime-pbj-workbench-python3.10-standard
ml-runtime-pbj-workbench-python3.11-cuda
ml-runtime-pbj-workbench-python3.11-standard
ml-runtime-pbj-workbench-python3.12-cuda
ml-runtime-pbj-workbench-python3.12-standard
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard
ml-runtime-pbj-workbench-r4.5-standard
ml-runtime-pbj-workbench-scala2.12-standard
nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-meta-llama3.3-70b-instruct-v2.0.3
nim-minimax-ai-minimax-m25-v1.7.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-cosmos-reason2-8b-v1.7.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v2.0.3
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-nano-v2.0.3
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
nim-nvidia-nemotron-3-super-120b-a12b-v2.0.3
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-120b-v2.0.3
nim-openai-gpt-oss-20b-v1.12.4
nim-openai-gpt-oss-20b-v2.0.3

GHSA-268h-hp4c-crq3 ### Summary Nodemailer constructs `List-*` headers from the caller-provided `list` message option using internally prepared header values. The `list.*.comment` field is inserted into those prepared values without removing CR (`\r`) or LF (`\n`) characters. Because prepared headers bypass the normal header-value sanitizer and are passed to `mimeFuncs.foldLines()`, a CRLF sequence in a list comment is emitted as an actual header boundary in the generated RFC822 message. An application that lets a lower-privileged or unauthenticated user influence `list.help.comment`, `list.unsubscribe.comment`, `list.subscribe.comment`, `list.post.comment`, `list.owner.comment`, `list.archive.comment`, or `list.id.comment` can therefore be made to generate messages containing attacker-chosen additional headers.

cdsw-web

GHSA-2f96-g7mh-g2hx ## Command injection via long-option prefix abbreviation bypassing `check_unsafe_options` (incomplete fix of CVE-2026-42215 / GHSA-rpm5-65cw-6hj4) **Component:** gitpython-developers/GitPython (PyPI: GitPython) **Affected:** all versions carrying the 3.1.47 blocklist fix, through current `main` (verified at commit `20c5e275`, `3.1.50-42`) **CWE:** CWE-184 (Incomplete List of Disallowed Inputs) → CWE-78 (OS Command Injection) **Severity:** inherits the parent CVE-2026-42215 surface; estimated High, ~8.8 (`AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H`) — final scoring deferred to maintainer/CNA, mirroring the parent.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
model-registry
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
python-runtime

GHSA-2m67-wjpj-xhg9 ## Summary Jackson Core 3.x does not consistently enforce `StreamReadConstraints.maxDocumentLength`. Oversized JSON documents can be accepted without a `StreamConstraintsException` in multiple parser entry points, which allows configured size limits to be bypassed and weakens denial-of-service protections. ## Details

cdsw-mlops-governance
dss-app
thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api

GHSA-2rp8-mm9q-fp49 ### Summary `typeorm migration:generate` embeds database schema metadata into JS/TS template literals, escaping backticks but not `${...}`. An attacker who can write schema metadata (column comments, defaults, view definitions) achieves arbitrary code execution on the host that loads the generated migration. ### Details

cdsw-web

GHSA-39q2-94rc-95cp ## Summary In `src/purify.ts:1117-1123`, `ADD_TAGS` as a function (via `EXTRA_ELEMENT_HANDLING.tagCheck`) bypasses `FORBID_TAGS` due to short-circuit evaluation. The condition: ``` !(tagCheck(tagName)) && (!ALLOWED_TAGS[tagName] || FORBID_TAGS[tagName])

cloudera-ai-agent-studio

GHSA-42h9-826w-cgv3 ## Summary Axios versions `0.28.0` and later contain uncontrolled recursion in `formDataToJSON`, the helper behind the public `axios.formToJSON()` / named `formToJSON` API and the default request transform used when FormData is sent with an `application/json` content type. Applications are affected when they pass attacker-controlled `FormData` field names into this functionality. A field name with thousands of nested bracket segments can exhaust the JavaScript call stack and throw `RangeError: Maximum call stack size exceeded`, causing request failure and, in applications that do not handle the exception or rejected promise, possible process termination. ## Impact

cdsw-web

GHSA-69x8-hrgq-fjj8 ### Impact Three issues combine into a full authentication bypass chain: 1. Weak hashing: User passwords are stored as unsalted SHA-256 hashes, making them vulnerable to rainbow table attacks and trivially identifying users with identical passwords. 2. Hash exposure: Multiple API endpoints (/user/info, /user/update, /spend/users) return the password hash field in responses to any authenticated user regardless of role. Plaintext passwords could also potentially be exposed in certain scenarios.

nim-deepseek-r1-v1.7.3

GHSA-6v7q-wjvx-w8wg ## Summary basic-ftp's CRLF injection protection (added in commit 2ecc8e2 for GHSA-chqc-8p9q-pq6q) is incomplete. Two code paths bypass the `protectWhitespace()` control character check: (1) the `login()` method directly concatenates user-supplied credentials into USER/PASS FTP commands without any validation, and (2) the `_openDir()` method sends an MKD command before `cd()` invokes `protectWhitespace()`, creating a TOCTOU bypass. Both vectors allow an attacker who controls input to inject arbitrary FTP commands into the control connection. ## Details

cdsw-web

GHSA-72hv-8253-57qq ### Summary The non-blocking (async) JSON parser in `jackson-core` bypasses the `maxNumberLength` constraint (default: 1000 characters) defined in `StreamReadConstraints`. This allows an attacker to send JSON with arbitrarily long numbers through the async parser API, leading to excessive memory allocation and potential CPU exhaustion, resulting in a Denial of Service (DoS). The standard synchronous parser correctly enforces this limit, but the async parser fails to do so, creating an inconsistent enforcement policy. ### Details

admissiond
catalogd
cdc-profilers
cdc_profilers
cdsw-mlops-governance
cloudera-ai-rag-studio
cml-addon-hadoop-cli-7.1.9.20000-24
cml-addon-hadoop-cli-7.3.1.200-90
cml-addon-hadoop-cli-7.3.1.709-1
cml-addon-hadoop-cli-7.3.2.0-957
cml-addon-ozone-732.1.0-b4
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-knox
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-runtime-3.5.4-7.1.9.1078
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-runtime-3.5.4-7.3.2.0
dex-livy-server-2.4.8-7.1.9.1078
dex-livy-server-3.3.2-7.1.9.1078
dex-livy-server-3.5.4-7.1.9.1078
dex-livy-server-3.5.4-7.3.1.709
dex-livy-server-3.5.4-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-history-server-3.3.2-7.1.9.1078
dex-spark-history-server-3.5.4-7.1.9.1078
dex-spark-history-server-3.5.4-7.3.1.709
dex-spark-history-server-3.5.4-7.3.2.0
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat
dex-spark-runtime-3.5.4-7.1.9.1078
dex-spark-runtime-3.5.4-7.3.1.709
dex-spark-runtime-3.5.4-7.3.2.0
dex_ozone-parcel-image
dmx-app
dss-app
hive
hive-autoscaler
hueqp
impalad_coord_exec
impalad_coordinator
impalad_executor
knox-gateway
nim-deepseek-r1-v1.7.3
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1
ozone-parcel-image
thunderhead-backupjob
thunderhead-compute-api
thunderhead-configtemplate
thunderhead-consoleauthenticationcdp
thunderhead-de-api
thunderhead-deletebackupjob
thunderhead-diagnostics-api
thunderhead-drscp-api
thunderhead-dw-api
thunderhead-environment
thunderhead-environments2-api
thunderhead-hybrid
thunderhead-hybrid-api
thunderhead-iam-api
thunderhead-kerberosmgmt-api
thunderhead-ml-api
thunderhead-mlopsgovernance
thunderhead-notification
thunderhead-notification-api
thunderhead-onpremises-api
thunderhead-remotecluster
thunderhead-restorejob
thunderhead-sdx2-api
thunderhead-servicediscovery-api
thunderhead-servicediscoverysimple
thunderhead-usermanagement-private
thunderhead-userpreference
thunderhead-userpreference-api
trino

GHSA-747p-wmpv-9c78 **Summary** AWS CLI is a command line tool for interacting with AWS services. When the cli_history feature is enabled, the history database file is created with default permissions, potentially allowing other local users on a multi-user system to read the file. **Impact** When cli_history is enabled, AWS CLI stores command history including command parameters and API request/response data in a local SQLite database. On multi-user Unix systems, the default file permissions may allow other local users to read this file, potentially exposing sensitive information. This issue only affects users who have explicitly enabled cli_history, which is disabled by default.

model-registry

GHSA-76mc-f452-cxcm # Hook mutation of `data.allowedTags` / `data.allowedAttributes` permanently pollutes `DEFAULT_ALLOWED_TAGS` / `DEFAULT_ALLOWED_ATTR` **CWE**: CWE-501 (Trust Boundary Violation — hook-scoped mutation leaks to global default sets) via CWE-693 (Protection Mechanism Failure — the default allow-list is silently widened for all subsequent sanitize calls) ## Summary

cloudera-ai-agent-studio

GHSA-7q8q-rj6j-mhjq ## Summary Axios can consume inherited properties from nested request option objects when the JavaScript process already has a polluted `Object.prototype`. The top-level merged config is protected with a null prototype, but nested plain objects such as `auth` and `paramsSerializer` are cloned into ordinary objects. If application code passes placeholders such as `auth: {}` or `paramsSerializer: {}`, inherited `username`, `password`, `encode`, or `serialize` properties can influence outbound requests.

cdsw-web

GHSA-89vp-jrxv-24w8 JupyterLab's PyPI extension manager enforces `blocked_extensions_uris` by comparing the requested install name to blocklist entries with a custom string normalization that is weaker than PyPI package-name canonicalization. An authenticated user can request a PyPI-equivalent spelling such as `JupyterLab.Git` for a blocklisted package such as `jupyterlab-git`; JupyterLab accepts the install request even though pip resolves the variant to the same package. This has security implications only for deployments that combine all of the following: - an allowlist/blocklist configured with the intent of restricting which packages users can install; - the (default) PyPI Extension Manager enabled; and - kernels and terminals disabled or delegated to remote hosts (otherwise a user with kernel access can install packages directly regardless of this check)

nim-mit-boltz2-v1.5.0

GHSA-8jr5-v98p-w75m ## Summary Issue 1: EXIF orientation not normalized → The image orientation processed by the model differs from how humans view it, introducing interpretation bias. Issue 2: PNG tRNS not explicitly flattened before converting to RGB → After conversion, transparent/semi-transparent pixels are rendered unexpectedly, making otherwise subtle overlay elements visible and distorting the input content. (This attack is similar to AlphaDog: RGBA handling is already correct in vLLM, but since tRNS permits RGB images, the correct processing path isn’t taken.)

kserve_huggingfaceserver

GHSA-956x-8gvw-wg5v ## Summary GitPython spawns the real `git` binary with an argument vector built from caller-supplied values. To prevent argument injection, GitPython maintains denylists of "unsafe" Git options (`--upload-pack`, `--receive-pack`, `--exec`, `-c`, `--config`, …) that can be abused to run arbitrary commands, and enforces them with `Git.check_unsafe_options()`. That enforcement is only wired into the **network** commands — `clone_from`, `Remote.fetch`, `Remote.pull`, `Remote.push`. Several other public APIs that also forward caller-controlled values into the `git` argv have **no guard at all**:

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
model-registry
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
python-runtime

GHSA-9ggv-8w38-r7pm ### Impact Blind SQL injection vulnerability in `UpdateQueryBuilder` and `SoftDeleteQueryBuilder` affecting MySQL and MariaDB users. `UpdateQueryBuilder` and `SoftDeleteQueryBuilder` (including their `addOrderBy` variants) do not validate the `order` parameter against an allowlist of permitted values (`ASC`/`DESC`). The caller-supplied value is stored verbatim and concatenated directly into the generated SQL string without quoting or parameterization. `SelectQueryBuilder.orderBy` performs this validation correctly; the affected builders do not.

cdsw-web

GHSA-c4rq-3m3g-8wgx ## Summary Nokogiri's CSS selector tokenizer contains regular expressions whose construction may result in exponential regex backtracking on adversarial selectors. Three ReDoS vectors are addressed in this release: 1. String-literal tokenization on certain unterminated quoted-string input. 2. String-literal tokenization on a separate class of hex-escape-rich input.

cdw-kube-fluentd-operator
logrouter-config-reloader

GHSA-c7w3-x93f-qmm8 ### Summary When a custom `envelope` object is passed to `sendMail()` with a `size` property containing CRLF characters (`\r\n`), the value is concatenated directly into the SMTP `MAIL FROM` command without sanitization. This allows injection of arbitrary SMTP commands, including `RCPT TO` — silently adding attacker-controlled recipients to outgoing emails. ### Details In `lib/smtp-connection/index.js` (lines 1161-1162), the `envelope.size` value is concatenated into the SMTP `MAIL FROM` command without any CRLF sanitization:

cdsw-web

GHSA-cj63-jhhr-wcxv ## Summary When `USE_PROFILES` is enabled, DOMPurify rebuilds `ALLOWED_ATTR` as a plain array before populating it with the requested allowlists. Because the sanitizer still looks up attributes via `ALLOWED_ATTR[lcName]`, any `Array.prototype` property that is polluted also counts as an allowlisted attribute. An attacker who can set `Array.prototype.onclick = true` (or a runtime already subject to prototype pollution) can thus force DOMPurify to keep event handlers such as `onclick` even when they are normally forbidden. The provided PoC sanitizes `<img onclick=...>` with `USE_PROFILES` and adds the sanitized output to the DOM; the polluted prototype allows the event handler to survive and execute, turning what should be a blocklist into a silent XSS vector. ## Impact Prototype pollution makes DOMPurify accept dangerous event handler attributes, which bypasses the sanitizer and results in DOM-based XSS once the sanitized markup is rendered.

cloudera-ai-agent-studio

GHSA-cjmm-f4jc-qw8r ## Summary DOMPurify allows `ADD_ATTR` to be provided as a predicate function via `EXTRA_ELEMENT_HANDLING.attributeCheck`. When the predicate returns `true`, `_isValidAttribute` short-circuits the attribute check before URI-safe validation runs. An attacker who supplies a predicate that accepts specific attribute/tag combinations can then sanitize input such as `<a href="javascript:alert(document.domain)">` and have the `javascript:` URL survive, because URI validation is skipped for that attribute while other checks still pass. The provided PoC accepts `href` for anchors and then triggers a click inside an iframe, showing that the sanitized payload executes despite the protocol bypass. ## Impact Predicate-based allowlisting bypasses DOMPurify's URI validation, allowing unsafe protocols such as `javascript:` to reach the DOM and execute whenever the link is activated, resulting in DOM-based XSS.

cloudera-ai-agent-studio

GHSA-f4gw-2p7v-4548 ## Summary Axios versions containing `lib/helpers/shouldBypassProxy.js` do not treat `0.0.0.0` as a local address when evaluating `NO_PROXY` rules. In Node.js applications that use `HTTP_PROXY` or `HTTPS_PROXY` together with `NO_PROXY=localhost,127.0.0.1,::1` or similar, a request to `http://0.0.0.0:<port>/` can be routed through the configured proxy instead of bypassing it. The issue is exploitable when an attacker can influence the axios request URL or a followed redirect target, and when the proxy can reach or relay `0.0.0.0` to local services. This is a Node.js runtime proxy-routing issue, not a browser, install-time, or development-tooling issue.

cdsw-web

GHSA-f4xh-w4cj-qxq8 # Summary An attacker who can send an HTTP request to a server running the LangSmith SDK's `TracingMiddleware` can cause that server to read an arbitrary file from its local filesystem and upload the contents to LangSmith as a trace attachment. Depending on how the distributed trace system is deployed, triggering a read may not require authentication. Retrieving the contents requires read access to the LangSmith workspace the traces are sent to. The net effect is a trust-boundary crossing: a party with workspace trace-read access (for example a low-privilege workspace member, a contractor, or a compromised teammate account) gains the ability to read files from any server running `TracingMiddleware`, a capability outside that workspace's intended trust boundary. # Impact

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
ml-runtime-pbj-workbench-python3.13-cuda
ml-runtime-pbj-workbench-python3.13-standard

GHSA-gcfj-64vw-6mp9 ## Summary Axios’ Node.js HTTP adapter can route requests through an attacker-controlled proxy when `Object.prototype.proxy` is polluted and request configuration is materialized as a regular object before dispatch. Recent axios releases harden merged request config by creating a null-prototype object. However, request interceptors run after that merge and may return a replacement config. A common immutable interceptor pattern such as `{...config}` or `Object.assign({}, config)` converts the hardened config back into a normal object. Axios then dispatches that object without re-hardening it, and the Node HTTP adapter reads `config.proxy` through the prototype chain.

cdsw-web

GHSA-ggpf-24jw-3fcw ## Description https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54 reported a vulnerability where loading a malicious model could result in code execution on the vllm host. The fix applied to specify `weights_only=True` to calls to `torch.load()` did not solve the problem prior to PyTorch 2.6.0. PyTorch has issued a new CVE about this problem: https://github.com/advisories/GHSA-53q9-r3pm-6pq6

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

GHSA-gr75-jv2w-4656 ## Summary Several LangChain components that resolve filesystem paths or expand search patterns do not consistently confine the *resolved* path to the intended root directory. Affected behaviors include: a file-search agent middleware that validates a starting directory but not the search pattern or the resolved target of matched files, so glob patterns and symlinks can reach files outside the configured root; prompt- and chain/agent-configuration loaders that accept path fields and resolve them without confining the result to a trusted base or rejecting symlink targets; and path-prefix authorization checks that compare by string prefix without a path-segment boundary, so a sibling path sharing the prefix is accepted. When these components receive path values, search patterns, or workspace contents influenced by an untrusted source — including an LLM acting on untrusted input — the result can be disclosure of files outside the intended boundary. We have no evidence of this behavior being triggered in the wild. ## Affected users / systems

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline

GHSA-gvmj-g25r-r7wr ## Summary When DOMPurify is configured with both `SAFE_FOR_TEMPLATES: true` and `RETURN_DOM: true` (or `IN_PLACE: true`), an attacker can inject template expressions, such as `${evil}`, `{{evil}}`, or `<%evil%>`, that survive the sanitization pass inside `<template>` element content. This bypasses the explicit purpose of `SAFE_FOR_TEMPLATES`, which is to prevent template engine evaluation of user-supplied content. > **Note:** The string output path is **not** affected. Only the DOM return paths (`RETURN_DOM: true`, `RETURN_DOM_FRAGMENT: true`, `IN_PLACE: true`) are vulnerable.

cloudera-ai-agent-studio

GHSA-gx64-gj6p-pc4c JupyterLab's image viewer allows for cross-site scripting (XSS) when a specially-crafted image file is opened through the image viewer and then opened in a new tab. This XSS issue can be used to cause remote code execution (RCE) on the JupyterLab server. ### Impact This vulnerability allows for arbitrary code execution.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

GHSA-gx7w-56w6-g48x ## AI Disclosure I used an LLM to help review the source code, reason about attack surface, and help draft and refine this report. I manually validated the finding by reproducing it locally, confirming the vulnerable code path, and verifying the HTTP behavior with `curl -v`. ## Summary

cdwdataviz
runtimedataviz

GHSA-h5v5-8746-g7mm JupyterLab's plugin manager exposes administrator controls intended to prevent users from enabling or disabling selected plugins. Two server-side enforcement gaps let an authenticated user bypass those controls with direct requests to `/lab/api/plugins`. ### Impact Users could workaround the plugin manager lock rules via direct API access for either: - child plugins of extensions covering multiple plugins

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

GHSA-h8r8-wccr-v5f2 ## Description A mutation-XSS (mXSS) condition was confirmed when sanitized HTML is reinserted into a new parsing context using `innerHTML` and special wrappers. The vulnerable wrappers confirmed in browser behavior are `script`, `xmp`, `iframe`, `noembed`, `noframes`, and `noscript`. The payload remains seemingly benign after `DOMPurify.sanitize()`, but mutates during the second parse into executable markup with an event handler, enabling JavaScript execution in the client (`alert(1)` in the PoC). ## Vulnerability

cloudera-ai-agent-studio

GHSA-hcpx-6fm6-wx23 ## Summary Axios versions in the fixed lines for GHSA-62hf-57xw-28j9 still contain an incomplete depth-limit bypass in `lib/helpers/toFormData.js`. When serializing an object with a top-level key ending in `{}`, axios calls `JSON.stringify()` on that value before the `formSerializer.maxDepth` guard can inspect the nested structure. An attacker who can control object keys and nested values passed by an application into axios form or parameter serialization can trigger a raw `RangeError: Maximum call stack size exceeded`, causing a denial of service in the affected request path.

cdsw-web

GHSA-hf3c-wxg2-49q9 ### Impact This report is to highlight a vulnerability in XGrammar, a library used by the structured output feature in vLLM. The XGrammar advisory is here: https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3 The [xgrammar](https://xgrammar.mlc.ai/docs/) library is the default backend used by vLLM to support structured output (a.k.a. guided decoding). Xgrammar provides a required, built-in cache for its compiled grammars stored in RAM. xgrammar is available by default through the OpenAI compatible API server with both the V0 and V1 engines.

nim-deepseek-r1-v1.7.3
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

GHSA-j828-28rj-hfhp ### Summary A recent review identified several regular expressions in the vllm codebase that are susceptible to Regular Expression Denial of Service (ReDoS) attacks. These patterns, if fed with crafted or malicious input, may cause severe performance degradation due to catastrophic backtracking. #### 1. vllm/lora/utils.py [Line 173](https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/lora/utils.py#L173) https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/lora/utils.py#L173

nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1

GHSA-j965-2qgj-vjmq CVSSv3.1 Rating: 3.7 (LOW) Summary This notification is related to the use of specific values for the region input field when calling AWS services. An actor with access to the environment in which the SDK is used could set the region input field to an invalid value. Per the AWS shared responsibility model, customer applications should protect instances appropriately, or implement proper input sanitization checks. The AWS SDK for JavaScript v2 reached end-of-support on September 8, 2025, but a defense-in-depth enhancement has been implemented in AWS SDK for JavaScript v3. Please migrate to that version. Impact

cdsw-web

GHSA-jqh4-m9w3-8hp9 ## Summary axios’ fetch adapter does not enforce `maxBodyLength` for live WHATWG `ReadableStream` request bodies whose size cannot be determined before dispatch. Applications that use `adapter: "fetch"` and rely on `maxBodyLength` to cap untrusted upload/proxy streams can send the full stream even when it exceeds the configured limit. This affects fetch-adapter usage in edge runtimes where fetch is selected, and in Node.js or browser environments where the fetch adapter is explicitly selected. The HTTP adapter’s stream upload path is not affected.

cdsw-web

GHSA-mcmc-2m55-j8jj ### Summary The fix [here](https://github.com/vllm-project/vllm/pull/27204) for CVE-2025-62164 is not sufficient. The fix only disables prompt embeds by default rather than addressing the root cause, so the DoS vulnerability remains when the feature is enabled. ### Details vLLM's pending change attempts to fix the root cause, which is the missing sparse tensor validation. PyTorch (~v2.0) disables sparse tensor validation (specifically, sparse tensor invariants checks) by default for performance reasons. vLLM is adding the sparse tensor validation to ensure indices are valid, non-negative, and within bounds. These checks help catch malformed tensors.

nim-bigcode-starcoder2-7b-v1.15.3
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-nvidia-nemotron-3-nano-v1.7.0
nim-openai-gpt-oss-120b-v1.12.4
nim-openai-gpt-oss-20b-v1.12.4

GHSA-mfg7-5gfp-c4w3 ### Summary A memory leak can be caused in Netty's DNS codec by sending malicious DNS packets containing invalid domain names. Because the leak occurs incrementally per packet, sustained malicious requests will cause a gradual Denial of Service. ### Details Inside `io.netty.handler.codec.dns.AbstractDnsRecord`, the parsed domain name string is passed to `IDN.toASCII(name)`. If the domain name contains characters that violate IDNA rules, `IDN.toASCII` throws an `IllegalArgumentException`.

admissiond
catalogd
cml-addon-hadoop-cli-7.1.9.20000-24
cml-addon-hadoop-cli-7.3.1.200-90
cml-addon-hadoop-cli-7.3.1.709-1
cml-addon-hadoop-cli-7.3.2.0-957
cml-addon-ozone-732.1.0-b4
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-runtime-3.5.4-7.1.9.1078
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-runtime-3.5.4-7.3.2.0
dex-livy-server-2.4.8-7.1.9.1078
dex-livy-server-3.3.2-7.1.9.1078
dex-livy-server-3.5.4-7.1.9.1078
dex-livy-server-3.5.4-7.3.1.709
dex-livy-server-3.5.4-7.3.2.0
dex-pipelines-api-server
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-history-server-3.5.4-7.3.2.0
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.5.4-7.3.2.0
dex_ozone-parcel-image
dmx-app
dss-app
hive
hueqp
impalad_coord_exec
impalad_coordinator
impalad_executor
ozone-parcel-image
trino

GHSA-mmx7-hfxf-jppx ## Summary axios is vulnerable to read-side prototype-pollution gadgets when `Object.prototype` has already been polluted by another vulnerability or dependency. The most broadly reachable issue is in the bodyless method aliases: `axios.get()`, `axios.delete()`, `axios.head()`, and `axios.options()` read inherited `data` before config normalization, causing attacker-controlled body data to be sent on requests that did not explicitly set a body. Additional low-level paths affect consumers that call exported adapters/helpers directly with plain config objects. In those cases, inherited `proxy` or `paramsSerializer` values can influence request routing or URL serialization. These low-level paths are not reproduced through normal `axios.get()` usage on `1.15.2+`.

cdsw-web

GHSA-mwf2-3pr3-8698 ## Summary Axios versions with Node.js HTTP/2 support allow streamed request bodies to bypass `maxBodyLength` enforcement when requests are sent with `httpVersion: 2`. This affects applications that rely on `maxBodyLength` as a hard cap while forwarding attacker-controlled streams, such as upload endpoints proxying user data to an upstream HTTP/2 service. Buffered request bodies are still checked before the request is sent.

cdsw-web

GHSA-p6gq-j5cr-w38f # Message-level `raw` option bypasses `disableFileAccess` / `disableUrlAccess`, enabling arbitrary file read and full-response SSRF in the sent message - **Target:** nodemailer/nodemailer, npm `nodemailer` **v9.0.0** (HEAD `4e58450eb490e5097a74b2b2cce35a8d9e21856e`) - **Verdict:** CONFIRMED (local PoC, no network) ## Summary

cdsw-web

GHSA-pmv8-rq9r-6j72 ## Summary Axios versions starting with `0.28.0` contain uncontrolled recursion in `formDataToJSON`, which is exposed as `axios.formToJSON()` and used internally when axios serialises `FormData` with `Content-Type: application/json`. If an application passes attacker-controlled `FormData` field names to this functionality, a field name with thousands of nested bracket segments can exhaust the JavaScript call stack and cause denial of service for that request or, in applications without appropriate error handling, process termination.

cdsw-web

GHSA-pppj-hq3g-57pj JupyterLab 4.5+ allows notebook settings to be shared and applied through an `overrides.json` file using the `Import` button in the Settings Editor. Certain notebook display settings were not properly validated before being applied. As a result, a crafted settings file could contain hidden instructions that run as code inside JupyterLab when imported, instead of only changing a display preference. Because importing a settings file appears harmless, a user could import a file shared by another party without realizing it could do more. On multi-tenant file systems without proper permission control, another user could plant a malicious `overrides.json`.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

GHSA-q56x-g2fj-4rj6 ### Summary The `save_external_data` method seems to include multiple issues introducing a local TOCTOU vulnerability, an arbitrary file read/write on any system. It potentially includes a path validation bypass on Windows systems. Regarding the TOCTOU, an attacker seems to be able to overwrite victim's files via symlink following under the same privilege scope. The mentioned function can be found here: https://github.com/onnx/onnx/blob/main/onnx/external_data_helper.py#L188

nemotron_nano_12b_v2_vl_v150
nim-baidu-paddleocr-v1.5.0
nim-bigcode-starcoder2-7b-v1.14.1
nim-bigcode-starcoder2-7b-v1.15.3
nim-deepseek-r1-v1.7.3
nim-meta-llama-3.1-nemotron-nano-8b-v1-v1.8.4
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-70b-instruct-v1.14.0
nim-meta-llama3.1-8b-instruct-v1.13.1
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.2-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.2-1b-instruct-v1.12.0
nim-meta-llama3.2-3b-instruct-v1.10.1
nim-meta-llama3.3-70b-instruct-v1.14.0
nim-meta-llama3.3-70b-instruct-v1.15.1
nim-mistralai-mistral-7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.12.0
nim-mistralai-mixtral-8x7b-instruct-v1.8.4
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-llama-3.1-nemotron-nano-4b-v1.1-v1.8.5
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.8.0
nim-nvidia-llama-3.2-nv-rerankqa-1b-v2-v1.9.3-stig-fips-x86
nim-nvidia-llama-3.3-nemotron-super-49b-v1-v1.10.1
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-v1.14.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.10.0
nim-nvidia-llama-32-nv-embedqa-1b-v2-v1.11.3-stig-fips-x86
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-nemoretriever-graphic-elements-v1-v1.6.0
nim-nvidia-nemoretriever-page-elements-v3-v1.7.0
nim-nvidia-nemoretriever-table-structure-v1-v1.6.0
nim-nvidia-nemotron-parse-v1.5.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

GHSA-r28c-9q8g-f849 ## Vulnerability Details **File**: `lib/previous-map.js` **Line**: 87-98 (`loadFile`), 129-144 (`loadMap`)

cloudera-ai-agent-studio

GHSA-r292-9mhp-454m ## Summary `node-tar` (npm `tar`) contains an uncontrolled-recursion stack-exhaustion DoS in the internal `mapHas` helper used by `filesFilter`. When a consumer calls `tar.t(...)` or `tar.x(...)` with a non-empty member-selection list, node-tar installs a filter that closes over the recursive `mapHas` (`src/list.ts:33-44`). `mapHas` walks an entry path upward one `path.dirname()` call per recursion **with no segment cap**. A single crafted tar with a GNU-`L` (or PAX-`x`) long-path header can deliver a path of tens of thousands of `/`-separated segments (up to `maxMetaEntrySize` = 1 MiB). The recursion overflows the call stack, throwing an uncatchable `RangeError` that terminates the Node process on async/streaming consumers. ## Root Cause `filesFilter` (`src/list.ts:27-51`) is installed whenever a caller passes a member-selection list (`src/list.ts:119-122`, `src/extract.ts:55-57`). Its filter is invoked at `src/parse.ts:253` (`entry.ignore = entry.ignore || !this.filter(entry.path, entry)`) inside `Parser[CONSUMEHEADER]` — and crucially **outside** the only try/catch in that method (which wraps `new Header` at `src/parse.ts:179-183`). `mapHas` recurses once per path segment with no depth limit. The `Unpack` `maxDepth` guard (`src/unpack.ts:342`, in `[CHECKPATH]`) only runs on the `'entry'` event, which fires *after* `CONSUMEHEADER` has already invoked the filter — so the stack overflows before any depth guard executes. `tar.t` (list) has no `maxDepth` at all.

cdsw-web
cloudera-ai-agent-studio
cloudera-ai-rag-studio

GHSA-r7g4-qg5f-qqm2 ### Summary Nodemailer disables TLS certificate verification in its internal HTTPS fetch client through the use of rejectUnauthorized: false inside lib/fetch/index.js. As a result, OAuth2 token requests trust invalid or self-signed HTTPS certificates and transmit sensitive OAuth credentials over connections that should fail TLS validation. An attacker in a machine-in-the-middle position can intercept OAuth2 credential exchanges and capture:

cdsw-web

GHSA-rf74-v2fm-23pw ### Summary `JSONTaggedDecoder.decode_obj()` in `nltk/jsontags.py` calls itself recursively without any depth limit. A deeply nested JSON structure exceeding `sys.getrecursionlimit()` (default: 1000) will raise an unhandled `RecursionError`, crashing the Python process.

cloudera-ai-rag-studio
nim-meta-llama3.1-70b-instruct-pb25h2-v1.14.0-pb5.5-stig-fips-x86-64
nim-meta-llama3.1-8b-instruct-v1.14.0-pb5.5-stig-fips-x86-64
nim-nvidia-llama-3.3-nemotron-super-49b-v1.5-pb25h2-v1.14.0
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0

GHSA-rwj8-pgh3-r573 ### Summary `Repo.clone_from()` passes the caller-supplied remote URL through `Git.polish_url()`, which on every non-Cygwin platform calls `os.path.expandvars()` on the URL before handing it to `git clone`. An attacker who controls the URL argument — the documented use case for `clone_from()` in "import repository from URL" features of CI servers, git-hosting mirrors, and dependency scanners — can embed `$NAME` / `${NAME}` tokens that are expanded server-side to the values of the hosting process's environment variables. The resulting URL, now containing the secret, is transmitted over the network to the attacker-named host. This crosses the trust boundary between an untrusted remote URL and the server's process environment, disclosing secrets such as `AWS_SECRET_ACCESS_KEY` or `GITHUB_TOKEN` with no precondition beyond the ability to submit a clone URL. ### Details **Affected versions:** `gitpython` (PyPI) — all releases up to and including `3.1.50` (latest at time of reporting); confirmed present on the `main` branch.

cloudera-ai-rag-studio
ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
model-registry
nim-nvidia-magpie-tts-multilingual-v1.6.0
nim-nvidia-parakeet-1-1b-ctc-en-us-v1.4.0
nim-nvidia-whisper-large-v3-v1.3.0
nim-nvidia-whisper-large-v3-v1.4.0
python-runtime

GHSA-v2fc-qm4h-8hqv ## Summary Nokogiri's `Nokogiri::XSLT::Stylesheet#transform` leaks a small heap allocation when passed a Ruby string parameter containing a null byte. For applications that pass attacker-controlled input through `XSLT.transform` parameters, this may be a vector for a denial of service attack against long-running processes.

cdw-kube-fluentd-operator
logrouter-config-reloader

GHSA-v396-v7q4-x2qj `GitPython` version `3.1.50` blocks unsafe `git clone` options such as `--upload-pack`, `-u`, `--config`, and `-c` unless callers explicitly pass `allow_unsafe_options=True`. However, the default unsafe-option gate does not recognize joined short-option forms such as `-u/path/to/helper`. Git itself accepts `-u<upload-pack>` as the short form of `--upload-pack=<upload-pack>`. As a result, `Repo.clone_from(..., multi_options=["-u<helper>"], allow_unsafe_options=False)` can execute the helper command even though the equivalent long option is blocked. Affected package:

python-runtime

GHSA-v74w-7mr3-4qg3 ### Summary An attacker can cause Denial of Service by sending a specially crafted malicious XML payload (e.g., repeated `</` characters) to a Netty server utilizing XmlFrameDecoder, causing the server's EventLoop thread to exhaust CPU resources and become unresponsive. ### Details `io.netty.handler.codec.xml.XmlFrameDecoder` suffers from a vulnerability resulting in CPU exhaustion. When `<` followed by `/` is encountered, the decoder scans the remaining buffer for a closing `>`. Because the parser state is not saved between `decode()` invocations, an attacker can trickle-feed a payload of `</` characters. This forces the decoder to repeatedly rescan the entire accumulated buffer. A 1MB `maxFrameLength` is enough to completely hang a server's thread while it loops endlessly.

admissiond
catalogd
cml-addon-hadoop-cli-7.1.9.20000-24
cml-addon-hadoop-cli-7.3.1.200-90
cml-addon-hadoop-cli-7.3.1.709-1
cml-addon-hadoop-cli-7.3.2.0-957
dex-airflow-7.1.9.1078
dex-airflow-7.3.1.709
dex-airflow-7.3.2.0
dex-airflow-api-server-7.1.9.1078
dex-airflow-api-server-7.3.1.709
dex-airflow-api-server-7.3.2.0
dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-runtime-3.5.4-7.1.9.1078
dex-livy-runtime-3.5.4-7.3.1.709
dex-livy-runtime-3.5.4-7.3.2.0
dex-livy-server-2.4.8-7.1.9.1078
dex-livy-server-3.3.2-7.1.9.1078
dex-livy-server-3.5.4-7.1.9.1078
dex-livy-server-3.5.4-7.3.1.709
dex-livy-server-3.5.4-7.3.2.0
dex-runtime-airflow-python-builder-7.1.9.1078
dex-runtime-airflow-python-builder-7.3.1.709
dex-runtime-airflow-python-builder-7.3.2.0
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dss-app
hive
hueqp
impalad_coord_exec
impalad_coordinator
impalad_executor
trino

GHSA-vmhf-c436-hxj4 A malicious PyPI package can place a `javascript:` URL in its `[project.urls]` metadata. JupyterLab's Extension Manager renders this as the extension's home-page link without validating the protocol, so a user who clicks the extension name executes attacker-controlled JavaScript in the JupyterLab origin. ### Details One of the PyPI package's URL (jupyterlab/extensions/pypi.py) is copied straight into the `homepage_url` rendered by the frontend in packages/extensionmanager/src/widget.tsx#L77-L88.

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

GHSA-vvjj-xcjg-gr5g ### Summary Nodemailer versions up to and including 8.0.4 are vulnerable to SMTP command injection via CRLF sequences in the transport `name` configuration option. The `name` value is used directly in the EHLO/HELO SMTP command without any sanitization for carriage return and line feed characters (`\r\n`). An attacker who can influence this option can inject arbitrary SMTP commands, enabling unauthorized email sending, email spoofing, and phishing attacks. ### Details

cdsw-web

GHSA-vxr8-fq34-vvx9 ## Impact A DOMPurify instance that is reused across trust boundaries can stay bound to a previously supplied `TRUSTED_TYPES_POLICY` even after `clearConfig()` is called. A later caller that requests `RETURN_TRUSTED_TYPE` receives a `TrustedHTML` object created by the old policy, not by a clean default configuration. If the old policy is unsafe or controlled by a less-trusted integration, this turns a later "default" sanitize call into script execution at a Trusted Types sink. `TRUSTED_TYPES_POLICY: null` on the later call also does not clear the retained policy. [dompurify-trusted-types-policy-survives-clearconfig-poc.js](https://github.com/user-attachments/files/28604913/dompurify-trusted-types-policy-survives-clearconfig-poc.js)

cloudera-ai-agent-studio

GHSA-whvh-wf3x-g77j The extension allowlist/blocklist check inside `PyPIExtensionManager.install()` was not enforced due to a missing await. For purposes of JupyterLab this was a secondary defense-in-depth check: `install()` was intended to enforce the allowlist/blocklist itself for any future uses and users calling this method directly (in addition to the separate check handling requests arriving through the HTTP API). The only runtime symptom was a `RuntimeWarning: coroutine 'is_install_allowed' was never awaited.` This has security implications only for deployments that combine all of the following: - a custom extension or downstream integration that imports `PyPIExtensionManager` and calls `install()` directly with a package name influenced by untrusted user input (the stock JupyterLab HTTP handler is not affected - it performs its own awaited allowlist check before calling `install()`); - an allowlist/blocklist configured with the intent of restricting which packages users can install; - the (default) PyPI Extension Manager enabled; and

ml-runtime-pbj-conda-standard
ml-runtime-pbj-jupyterlab-python3.10-cuda
ml-runtime-pbj-jupyterlab-python3.10-standard
ml-runtime-pbj-jupyterlab-python3.11-cuda
ml-runtime-pbj-jupyterlab-python3.11-freshline
ml-runtime-pbj-jupyterlab-python3.11-hardened
ml-runtime-pbj-jupyterlab-python3.11-standard
ml-runtime-pbj-jupyterlab-python3.12-cuda
ml-runtime-pbj-jupyterlab-python3.12-standard
ml-runtime-pbj-jupyterlab-python3.13-cuda
ml-runtime-pbj-jupyterlab-python3.13-standard
ml-runtime-pbj-jupyterlab-python3.14-hardened
ml-runtime-pbj-jupyterlab-r4.5-freshline
nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0

GHSA-wqvq-jvpq-h66f ### Summary Nodemailer's `disableFileAccess` and `disableUrlAccess` options are intended to prevent message content and attachments from reading local files or fetching URLs. The normal MIME streaming path enforces those options in `MimeNode._getStream()`. However, `jsonTransport` serializes messages by calling `mail.normalize()`, which resolves `html`, `text`, alternatives, calendar events, and attachments through `shared.resolveContent()` before MIME generation. `shared.resolveContent()` reads local files and fetches HTTP(S) URLs directly, without receiving or checking `disableFileAccess` or `disableUrlAccess`. As a result, applications that use `jsonTransport` as a safe serializer or queue payload generator while relying on `disableFileAccess` / `disableUrlAccess` can still be made to read local files into the generated JSON output or make outbound HTTP requests when an attacker controls message content fields such as attachment `path` or `text.href`.

cdsw-web

GHSA-wwhq-w58m-w29c # ## TL;DR CVE-2026-30852 fixed double expansion in `vars_regexp` when the variable key is a placeholder (e.g. `{http.vars.x}`). The fix does NOT protect literal key names (e.g. `tenant_id`). An attacker injects `{env.AWS_SECRET_ACCESS_KEY}` or `{file./etc/passwd}` via a request header → Caddy expands it on the second pass → secrets leaked in response headers.

cdwdataviz
runtimedataviz

GHSA-x4vx-rjvf-j5p4 ## Summary When `DOMPurify.sanitize(root, { IN_PLACE: true })` is called on an attacker-supplied live DOM node, `DOMPurify` still trusts `currentNode.nodeName` for non-`form` nodes in the main `_sanitizeElements` pipeline. A real `<script>` child node whose observable `nodeName` is attacker-controlled can therefore be misclassified as an allowed element and retained. When the sanitized tree is inserted into a live document, the script executes. This affects current `3.4.6`. The recent `IN_PLACE` hardening work covers clobbered `form` handling and foreign-realm shadow/template traversal, but does not harden the main per-node element decision for hostile non-`form` live nodes.

cloudera-ai-agent-studio

GHSA-xj6q-8x83-jv6g ## Summary Axios versions after the `GHSA-q8qp-cvcw-x6jj` fix still contain prototype-pollution read-side gadgets in Basic auth subfield handling. If a host application is already affected by prototype pollution and then makes an axios request with an own `auth` object that omits `username` or `password`, axios reads inherited `Object.prototype.username` and `Object.prototype.password` values and uses them to construct an outbound `Authorization: Basic ...` header. This does not mean axios itself pollutes prototypes. Exploitation requires a separate prototype-pollution primitive in the host process, plus an axios call pattern such as `auth: opts.auth || {}`.

cdsw-web

RHSA-2025:10128 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2025:14560 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:1631 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:2128 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:5588 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:6473 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:11077 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:24339 The Berkeley Internet Name Domain (BIND) is an implementation of the Domain Name System (DNS) protocols. BIND includes a DNS server (named); a resolver library (routines for applications to use when interfacing with DNS); and tools for verifying that the DNS server is operating correctly.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:25090 The httpd packages provide the Apache HTTP Server, a powerful, efficient, and extensible web server.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:29898 The libpng packages contain a library of functions for creating and manipulating Portable Network Graphics (PNG) image format files.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:33126 The glibc packages provide the standard C libraries (libc), POSIX thread libraries (libpthread), standard math libraries (libm), and the name service cache daemon (nscd) used by multiple programs on the system. Without these libraries, the Linux system cannot function correctly.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:39320 Python is an interpreted, interactive, object-oriented programming language, which includes modules, classes, exceptions, very high level dynamic data types and dynamic typing. Python supports interfaces to many system calls and libraries, as well as to various windowing systems.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:42733 The glibc packages provide the standard C libraries (libc), POSIX thread libraries (libpthread), standard math libraries (libm), and the name service cache daemon (nscd) used by multiple programs on the system. Without these libraries, the Linux system cannot function correctly.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-runtime-python-builder-7.1.9.1078-compat
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:42828 The httpd packages provide the Apache HTTP Server, a powerful, efficient, and extensible web server.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

RHSA-2026:42877 The java-1.8.0-openjdk packages provide the OpenJDK 8 Java Runtime Environment and the OpenJDK 8 Java Software Development Kit.

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078

RHSA-2026:47184 RHSA-2026:47184

dex-livy-runtime-2.4.8-7.1.9.1078
dex-livy-runtime-3.3.2-7.1.9.1078-compat
dex-livy-server-2.4.8-7.1.9.1078
dex-spark-history-server-2.4.8-7.1.9.1078
dex-spark-runtime-2.4.8-7.1.9.1078
dex-spark-runtime-3.3.2-7.1.9.1078-compat

NSWG-ECO-328 Jquery is a javascript library for DOM traversal and manipulation, event handling, animation, and Ajax. When text/javascript responses are received from cross-origin ajax requests not containing the option `dataType`, the result is executed in `jQuery.globalEval` potentially allowing an attacker to execute arbitrary code on the origin.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1

NSWG-ECO-329 jQuery is a javascript library for DOM manipulation. jQuery's main method in affected versions contains an unreliable way of detecting whether the input to the `jQuery(strInput)` function is intended to be a selector or HTML. For example, this code would be parsed as a selector, executing the code in the `onerror` attribute: ``` $("#log").html( $("element[attribute='<img src=\"x\" onerror=\"alert(1)\" />']").html() ); ``` The fix in v1.9.0 updates a regular expression for detecting whether the input is HTML or a selector. HTML input must now explicitly start with `<`, rather than previously assuming that the input was HTML if the string contained `<` anywhere.

nim-mit-boltz2-v1.3.0
nim-mit-boltz2-v1.5.0
nim-nvidia-nemotron-3-nano-v1.7.0
nim-nvidia-nemotron-3-super-120b-a12b-v1.8.1