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CVE-2026-47155 (GCVE-0-2026-47155)
Vulnerability from cvelistv5 – Published: 2026-06-22 22:20 – Updated: 2026-06-23 12:35- CWE-345 - Insufficient Verification of Data Authenticity
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
| https://github.com/vllm-project/vllm/pull/42616 | x_refsource_MISC |
| https://github.com/vllm-project/vllm/commit/d26a2… | x_refsource_MISC |
| https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a70… | x_refsource_MISC |
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.22.0
|
guessed |
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FKIE_CVE-2026-47155
Vulnerability from fkie_nvd - Published: 2026-06-22 23:16 - Updated: 2026-06-24 16:49{
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GHSA-3WW4-5JV9-J5GM
Vulnerability from github – Published: 2026-06-10 17:11 – Updated: 2026-07-17 16:21Summary
vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision.
This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision.
Details
The expected invariant is:
When a vLLM operator supplies a model or code revision pin, every code, config, processor, weight file, side weight, and same-repository subfolder artifact loaded as part of that model should resolve under that pin unless vLLM exposes and enforces a separate explicit pin for that artifact.
Current main was verified affected at commit 3795d7acf431980e62e738493f437ae2a51549da.
Affected source boundaries:
vllm/model_executor/models/registry.py:1045-1051and:1058-1064_try_resolve_transformers()passesrevision=model_config.revisionandtrust_remote_code=model_config.trust_remote_code, but omitscode_revision=model_config.code_revisionfor externalauto_mapdynamic module imports.vllm/model_executor/model_loader/gguf_loader.py:58-60- The direct-file GGUF form
repo/file.ggufcallshf_hub_download(repo_id=repo_id, filename=filename)without passingrevision. vllm/model_executor/models/roberta.py:203-209- BGE-M3 secondary sparse and ColBERT side weights are declared with
revision=None. vllm/model_executor/models/kimi_k25.py:111-114- Kimi-K2.5 calls
cached_get_image_processor()without passingmodel_config.revision. vllm/model_executor/models/kimi_audio.py:92-95- Kimi-Audio loads Whisper config from the
whisper-large-v3subfolder without arevisionargument. vllm/model_executor/models/kimi_audio.py:425-430- Kimi-Audio declares same-repository
whisper-large-v3secondary weights withrevision=None. vllm/model_executor/model_loader/default_loader.py:287-301- The default loader preserves
model_config.revisionfor the primary source, then consumes model-supplied secondary sources as declared.
The strongest example is Kimi-Audio: the primary moonshotai/Kimi-Audio-7B-Instruct weights preserve the configured model revision, but the same-repository whisper-large-v3 audio tower config/weights do not. A pinned Kimi-Audio deployment can therefore load the Whisper subfolder outside the audited revision.
This report does not claim a trust_remote_code=False bypass, unauthenticated RCE, or real artifact compromise. The issue is improper propagation of explicit artifact pins across supported loader paths.
Impact
Affected users are operators who pin vLLM model deployments to a reviewed Hugging Face revision for safety review, provenance, rollback, or reproducibility. The impact is that the pin does not reliably describe the full set of artifacts vLLM serves. Even when the operator selects an audited revision, vLLM can resolve behavior-affecting secondary artifacts from the repository default branch or another mutable ref.
Depending on the model path, the unpinned artifact can be dynamic model code, a GGUF file, an image processor, retrieval side weights, or the same-repository Kimi-Audio Whisper subfolder weights/config.
This breaks the operational guarantee of a pinned deployment: "serve the exact artifact set I reviewed." A later change to an unpinned secondary artifact can alter model behavior without changing the operator's configured revision, making review, rollback, incident response, and audit records unreliable.
Occurrences
vllm/model_executor/models/kimi_k25.pyL111-L114 — Kimi-K2.5 loads its image processor withcached_get_image_processor()but does not passself.ctx.model_config.revision. The processor can therefore resolve from the default repository revision even when the model deployment is pinned.vllm/model_executor/models/kimi_audio.pyL425-L430 — Kimi-Audio declares same-repositorywhisper-large-v3secondary weights withrevision=None. A pinned Kimi-Audio deployment can therefore load the Whisper audio tower weights from an unpinned/default revision.vllm/model_executor/models/kimi_audio.pyL92-L95 — Kimi-Audio loads Whisper config from the same repository'swhisper-large-v3subfolder without passing the top-level model revision. The config for this behavior-affecting subcomponent can be resolved outside the audited model revision.vllm/model_executor/models/registry.pyL1058-L1064 — The later dynamic model-class resolution repeats the same pin-decay pattern: it forwardsrevisionandtrust_remote_code, but omitscode_revision. This means an operator-provided code pin is not enforced at the dynamic module loader boundary.vllm/model_executor/model_loader/gguf_loader.pyL58-L60 — The direct GGUF formrepo/file.ggufcallshf_hub_download(repo_id=repo_id, filename=filename)without passingmodel_config.revision. A deployment that pins the model revision can therefore resolve this GGUF file from the repository default revision.vllm/model_executor/models/registry.pyL1045-L1051 —try_get_class_from_dynamic_module()is called for externalauto_mapconfig/model classes withrevision=model_config.revision, but without forwardingmodel_config.code_revision. When--code-revisionis set, this dynamic module resolution can still fall back to the default code revision instead of the audited code revision.vllm/model_executor/models/roberta.pyL203-L209 —BgeM3EmbeddingModelcreates same-repository secondary sparse/ColBERT weight sources withrevision=None. The primary model revision is not propagated to these side weights, so they can be downloaded outside the operator-selected model revision.
Fixes
This was fixed in: https://github.com/vllm-project/vllm/pull/42616
Originally filed via huntr: https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac.
The vLLM maintainer (Russell Bryant) redirected the report to the private GHSA channel. Offline proof bundle (vllm_artifact_pin_decay_bundle_verify.py + bundle-verification-20260430T143506Z.json) is available upon request.
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"details": "### Summary\n\nvLLM\u0027s revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies `--revision` or `--code-revision` can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision.\n\nThis is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision.\n\n### Details\n\nThe expected invariant is:\n\n\u003e When a vLLM operator supplies a model or code revision pin, every code, config, processor, weight file, side weight, and same-repository subfolder artifact loaded as part of that model should resolve under that pin unless vLLM exposes and enforces a separate explicit pin for that artifact.\n\nCurrent `main` was verified affected at commit `3795d7acf431980e62e738493f437ae2a51549da`.\n\nAffected source boundaries:\n\n- `vllm/model_executor/models/registry.py:1045-1051` and `:1058-1064`\n - `_try_resolve_transformers()` passes `revision=model_config.revision` and `trust_remote_code=model_config.trust_remote_code`, but omits `code_revision=model_config.code_revision` for external `auto_map` dynamic module imports.\n- `vllm/model_executor/model_loader/gguf_loader.py:58-60`\n - The direct-file GGUF form `repo/file.gguf` calls `hf_hub_download(repo_id=repo_id, filename=filename)` without passing `revision`.\n- `vllm/model_executor/models/roberta.py:203-209`\n - BGE-M3 secondary sparse and ColBERT side weights are declared with `revision=None`.\n- `vllm/model_executor/models/kimi_k25.py:111-114`\n - Kimi-K2.5 calls `cached_get_image_processor()` without passing `model_config.revision`.\n- `vllm/model_executor/models/kimi_audio.py:92-95`\n - Kimi-Audio loads Whisper config from the `whisper-large-v3` subfolder without a `revision` argument.\n- `vllm/model_executor/models/kimi_audio.py:425-430`\n - Kimi-Audio declares same-repository `whisper-large-v3` secondary weights with `revision=None`.\n- `vllm/model_executor/model_loader/default_loader.py:287-301`\n - The default loader preserves `model_config.revision` for the primary source, then consumes model-supplied secondary sources as declared.\n\nThe strongest example is Kimi-Audio: the primary `moonshotai/Kimi-Audio-7B-Instruct` weights preserve the configured model revision, but the same-repository `whisper-large-v3` audio tower config/weights do not. A pinned Kimi-Audio deployment can therefore load the Whisper subfolder outside the audited revision.\n\nThis report does not claim a `trust_remote_code=False` bypass, unauthenticated RCE, or real artifact compromise. The issue is improper propagation of explicit artifact pins across supported loader paths.\n\n### Impact\n\nAffected users are operators who pin vLLM model deployments to a reviewed Hugging Face revision for safety review, provenance, rollback, or reproducibility. The impact is that the pin does not reliably describe the full set of artifacts vLLM serves. Even when the operator selects an audited revision, vLLM can resolve behavior-affecting secondary artifacts from the repository default branch or another mutable ref.\n\nDepending on the model path, the unpinned artifact can be dynamic model code, a GGUF file, an image processor, retrieval side weights, or the same-repository Kimi-Audio Whisper subfolder weights/config.\n\nThis breaks the operational guarantee of a pinned deployment: \"serve the exact artifact set I reviewed.\" A later change to an unpinned secondary artifact can alter model behavior without changing the operator\u0027s configured revision, making review, rollback, incident response, and audit records unreliable.\n\n### Occurrences\n\n- `vllm/model_executor/models/kimi_k25.py` L111-L114 \u2014 Kimi-K2.5 loads its image processor with `cached_get_image_processor()` but does not pass `self.ctx.model_config.revision`. The processor can therefore resolve from the default repository revision even when the model deployment is pinned.\n- `vllm/model_executor/models/kimi_audio.py` L425-L430 \u2014 Kimi-Audio declares same-repository `whisper-large-v3` secondary weights with `revision=None`. A pinned Kimi-Audio deployment can therefore load the Whisper audio tower weights from an unpinned/default revision.\n- `vllm/model_executor/models/kimi_audio.py` L92-L95 \u2014 Kimi-Audio loads Whisper config from the same repository\u0027s `whisper-large-v3` subfolder without passing the top-level model revision. The config for this behavior-affecting subcomponent can be resolved outside the audited model revision.\n- `vllm/model_executor/models/registry.py` L1058-L1064 \u2014 The later dynamic model-class resolution repeats the same pin-decay pattern: it forwards `revision` and `trust_remote_code`, but omits `code_revision`. This means an operator-provided code pin is not enforced at the dynamic module loader boundary.\n- `vllm/model_executor/model_loader/gguf_loader.py` L58-L60 \u2014 The direct GGUF form `repo/file.gguf` calls `hf_hub_download(repo_id=repo_id, filename=filename)` without passing `model_config.revision`. A deployment that pins the model revision can therefore resolve this GGUF file from the repository default revision.\n- `vllm/model_executor/models/registry.py` L1045-L1051 \u2014 `try_get_class_from_dynamic_module()` is called for external `auto_map` config/model classes with `revision=model_config.revision`, but without forwarding `model_config.code_revision`. When `--code-revision` is set, this dynamic module resolution can still fall back to the default code revision instead of the audited code revision.\n- `vllm/model_executor/models/roberta.py` L203-L209 \u2014 `BgeM3EmbeddingModel` creates same-repository secondary sparse/ColBERT weight sources with `revision=None`. The primary model revision is not propagated to these side weights, so they can be downloaded outside the operator-selected model revision.\n\n### Fixes\n\nThis was fixed in: https://github.com/vllm-project/vllm/pull/42616\n\n___\n\nOriginally filed via huntr: https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac.\n\nThe vLLM maintainer (Russell Bryant) redirected the report to the private GHSA channel. Offline proof bundle (`vllm_artifact_pin_decay_bundle_verify.py` + `bundle-verification-20260430T143506Z.json`) is available upon request.",
"id": "GHSA-3ww4-5jv9-j5gm",
"modified": "2026-07-17T16:21:38Z",
"published": "2026-06-10T17:11:38Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/pull/42616"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6"
},
{
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"url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2301.yaml"
},
{
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"url": "https://github.com/vllm-project/vllm"
},
{
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"url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
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],
"schema_version": "1.4.0",
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{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
"type": "CVSS_V3"
}
],
"summary": "vLLM\u0027s Artifact Pin Decay allows pinned deployments to load unpinned code, weights, and processors"
}
PYSEC-2026-2301
Vulnerability from pysec - Published: 2026-06-22 23:16 - Updated: 2026-07-13 05:52vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.
| Name | purl | vllm | pkg:pypi/vllm |
|---|
{
"affected": [
{
"ecosystem_specific": {},
"package": {
"ecosystem": "PyPI",
"name": "vllm",
"purl": "pkg:pypi/vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.22.0"
}
],
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}
],
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"0.14.1",
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"0.16.0",
"0.17.0",
"0.17.1",
"0.18.0",
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"0.19.0",
"0.19.1",
"0.2.0",
"0.2.1",
"0.2.1.post1",
"0.2.2",
"0.2.3",
"0.2.4",
"0.2.5",
"0.2.6",
"0.2.7",
"0.20.0",
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"0.20.2",
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"0.3.0",
"0.3.1",
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"0.4.0",
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"0.8.5",
"0.8.5.post1",
"0.9.0",
"0.9.0.1",
"0.9.1",
"0.9.2"
]
}
],
"aliases": [
"CVE-2026-47155",
"GHSA-3ww4-5jv9-j5gm"
],
"details": "vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM\u0027s revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.",
"id": "PYSEC-2026-2301",
"modified": "2026-07-13T05:52:25.503631Z",
"published": "2026-06-22T23:16:30.357Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
},
{
"type": "ADVISORY",
"url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
},
{
"type": "REPORT",
"url": "https://github.com/vllm-project/vllm/pull/42616"
},
{
"type": "FIX",
"url": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
"type": "CVSS_V3"
}
]
}
RHSA-2026:59138
Vulnerability from csaf_redhat - Published: 2026-08-24 16:51 - Updated: 2026-09-06 04:18A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:201b9f3ebdbaa9979d9f40276e3aa5cc78f20d08ed3abae90954caff30ae9d8b_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:333f2a87d0dd8bc399eb7c1d9c7033e2c90c25c1b1b44a9941f16437b33ddccb_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:201b9f3ebdbaa9979d9f40276e3aa5cc78f20d08ed3abae90954caff30ae9d8b_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:333f2a87d0dd8bc399eb7c1d9c7033e2c90c25c1b1b44a9941f16437b33ddccb_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:201b9f3ebdbaa9979d9f40276e3aa5cc78f20d08ed3abae90954caff30ae9d8b_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:333f2a87d0dd8bc399eb7c1d9c7033e2c90c25c1b1b44a9941f16437b33ddccb_amd64 | — |
Vendor Fix
fix
Workaround
|
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{
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"title": "CVSS score applicability"
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"cve": "CVE-2026-53923",
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"title": "Vulnerability description"
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{
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RHSA-2026:59139
Vulnerability from csaf_redhat - Published: 2026-08-24 16:51 - Updated: 2026-09-06 04:19A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 | — |
Vendor Fix
fix
Workaround
|
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{
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"text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
"title": "Statement"
},
{
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"title": "CVSS score applicability"
}
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-41523"
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"date": "2026-08-24T16:51:58+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:59139",
"product_ids": [
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
],
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},
"url": "https://access.redhat.com/errata/RHSA-2026:59139"
},
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"details": "Avoid running vLLM with python -O or PYTHONOPTIMIZE=1 until updated packages are available. Only load models from trusted sources. Restrict who can deploy or update models on inference endpoints. Apply network access controls and authentication in front of vLLM APIs.",
"product_ids": [
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
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{
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"cwe": {
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},
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"ids": [
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"text": "2491580"
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"text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.",
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},
{
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"text": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Moderate impact. The flaw is a supply-chain integrity issue when operators pin a HuggingFace model revision but vLLM may still load nested artifacts from an unpinned revision. It affects Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI images that ship vLLM versions prior to 0.22.0. KServe control-plane components that bundle vLLM as a library are not affected.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-47155"
},
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"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6",
"url": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6"
},
{
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"summary": "https://github.com/vllm-project/vllm/pull/42616",
"url": "https://github.com/vllm-project/vllm/pull/42616"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
},
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"url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
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"date": "2026-08-24T16:51:58+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:59139",
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]
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],
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"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
"version": "3.1"
},
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]
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}
],
"title": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls"
},
{
"cve": "CVE-2026-53923",
"cwe": {
"id": "CWE-824",
"name": "Access of Uninitialized Pointer"
},
"discovery_date": "2026-06-22T23:00:51.742636+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2491579"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM\u0027s GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users\u0027 requests. This constitutes an information disclosure vulnerability.",
"title": "Vulnerability description"
},
{
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"text": "vllm: vLLM: Information disclosure via integer truncation",
"title": "Vulnerability summary"
},
{
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"text": "Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models.",
"title": "Statement"
},
{
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491579"
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-53923"
},
{
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"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923"
},
{
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"summary": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e",
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"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
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RHSA-2026:60363
Vulnerability from csaf_redhat - Published: 2026-08-26 16:25 - Updated: 2026-09-06 04:18A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). This vulnerability allows unauthenticated attackers to cause a Denial of Service (DoS) by supplying image or video placeholder sequences without matching data, leading to an unhandled error and worker termination. Additionally, text-only prompts containing special tokens can be misinterpreted as control commands, potentially leading to unexpected behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
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"text": "This is an Important denial of service vulnerability affecting vLLM, as integrated into Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI. The flaw allows unauthenticated attackers to trigger worker termination and degrade service availability by submitting malformed multimodal input or text prompts containing specific token sequences. This impact is significant due to the potential for service disruption in AI inference workloads.",
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"date": "2026-08-26T16:25:47+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
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"details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.",
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
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"category": "impact",
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"title": "vllm: vLLM: Denial of Service via malformed multimodal input or token injection"
},
{
"cve": "CVE-2026-47155",
"cwe": {
"id": "CWE-829",
"name": "Inclusion of Functionality from Untrusted Control Sphere"
},
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"ids": [
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"category": "description",
"text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Moderate impact. The flaw is a supply-chain integrity issue when operators pin a HuggingFace model revision but vLLM may still load nested artifacts from an unpinned revision. It affects Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI images that ship vLLM versions prior to 0.22.0. KServe control-plane components that bundle vLLM as a library are not affected.",
"title": "Statement"
},
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
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"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491580"
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"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-47155",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-47155"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6",
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"url": "https://github.com/vllm-project/vllm/pull/42616"
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"category": "external",
"summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
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"summary": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac",
"url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
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"release_date": "2026-06-22T22:20:10.793000+00:00",
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"date": "2026-08-26T16:25:47+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
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"details": "Upgrade to a vLLM build containing the fix (\u003e= 0.22.0) when available from Red Hat. Until then, only serve models from trusted registries, pin revisions explicitly, and review nested artifacts in model repositories before deployment.",
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"attackVector": "NETWORK",
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"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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"version": "3.1"
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"title": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls"
},
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"cve": "CVE-2026-53923",
"cwe": {
"id": "CWE-824",
"name": "Access of Uninitialized Pointer"
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"discovery_date": "2026-06-22T23:00:51.742636+00:00",
"ids": [
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"system_name": "Red Hat Bugzilla ID",
"text": "2491579"
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{
"category": "description",
"text": "A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM\u0027s GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users\u0027 requests. This constitutes an information disclosure vulnerability.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Information disclosure via integer truncation",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-53923"
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"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923"
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"baseScore": 4.3,
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"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
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"title": "vllm: vLLM: Information disclosure via integer truncation"
},
{
"cve": "CVE-2026-54234",
"cwe": {
"id": "CWE-125",
"name": "Out-of-bounds Read"
},
"discovery_date": "2026-07-06T21:01:59.789808+00:00",
"ids": [
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"text": "2497515"
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"text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.",
"title": "Vulnerability description"
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{
"category": "summary",
"text": "vllm: vLLM: Denial of Service via malformed speculative decoding workload",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important denial of service flaw in vLLM, utilized by Red Hat AI Inference Server, Red Hat Enterprise Linux AI, and Red Hat OpenShift AI, allows a remote attacker to crash the engine worker. By sending a specially crafted multi-request speculative decoding workload to public gRPC endpoints, an attacker can trigger an out-of-vocabulary token, leading to a service-wide disruption for all connected clients.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-54234"
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{
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"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54234",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54234"
},
{
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"summary": "https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca",
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"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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},
{
"cve": "CVE-2026-55574",
"cwe": {
"id": "CWE-1333",
"name": "Inefficient Regular Expression Complexity"
},
"discovery_date": "2026-07-06T21:01:40.112117+00:00",
"ids": [
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"system_name": "Red Hat Bugzilla ID",
"text": "2497509"
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"category": "description",
"text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "An Important denial of service vulnerability exists in vLLM, as utilized within Red Hat AI Inference Server and Red Hat OpenShift AI. This flaw allows a remote, unauthenticated attacker to cause an inference worker to hang indefinitely by submitting a specially crafted regular expression to the structured outputs API. The absence of complexity analysis for nested quantifiers in the regex compiler leads to an exponential state-space expansion, resulting in prolonged service disruption.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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RHSA-2026:62335
Vulnerability from csaf_redhat - Published: 2026-09-01 21:52 - Updated: 2026-09-11 13:56A flaw was found in AIOHTTP, an asynchronous HTTP client/server framework for asyncio and Python. An attacker could exploit this vulnerability by providing untrusted input to the `CookieJar.load()` function. This could potentially lead to arbitrary code execution, allowing the attacker to run malicious code on the affected system. This issue is most likely to occur in applications that allow attacker-controlled files to be loaded.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in ip-address, a JavaScript library for parsing and manipulating IPv4 and IPv6 addresses. This vulnerability allows a remote attacker to perform cross-site scripting (XSS) by providing untrusted input to the Address6 constructor. When an application renders the output of Address6.group(), Address6.link(), or the AddressError.parseMessage as HTML without proper escaping, the attacker-controlled content can be executed in the user's browser.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow prior to 12.3.0. When an uncompressed McIdas AREA image is loaded from a filename through the mmap raw codec path, attacker-controlled header words can set a row stride smaller than the natural row width. Pixel access such as Image.tobytes(), getpixel, convert, or save can then read beyond the mapped region, disclosing adjacent process memory or causing a fault.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
|
A flaw was found in Pillow, a Python imaging library. When processing a specially crafted font file, the library's font compilation function does not adequately check for excessive memory allocation. This oversight allows a remote attacker to trigger an unreasonable consumption of system memory, leading to a denial of service (DoS) for the application.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
|
A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to cause a Denial of Service (DoS) by providing a specially crafted BDF font file. The library's image processing function fails to properly validate dimensions from the font file, bypassing a critical security check designed to prevent excessive memory usage. This oversight can lead to the system consuming an unreasonable amount of memory, making it unavailable to legitimate users.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted GD 2.x image file. The GdImageFile._open() function reads image dimensions without proper validation, leading to excessive memory allocation. This can result in a Denial of Service (DoS) due to C-heap exhaustion.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the library's image processing functions, specifically when handling image coordinates near certain limits. This flaw, a native heap out-of-bounds write, could lead to a denial of service (DoS), making the affected system or application unavailable.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the PdfParser.PdfStream.decode() function when processing a crafted FlateDecode PDF stream. By providing a specially designed PDF file, the attacker could cause the application to exhaust available memory, leading to a denial of service (DoS).
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted JPEG2000 image file. Due to incorrect calculation of memory requirements for image tiles, processing this file can lead to excessive memory consumption, resulting in a denial of service (DoS) through out-of-memory failures.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform's declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in pyasn1, a generic ASN.1 library for Python. The Basic Encoding Rules (BER) decoder, used by CER and DER codecs, processes long-form tags by accumulating continuation octets without an upper bound. A remote attacker can exploit this by providing a specially crafted input, leading to the construction of an arbitrarily large integer. This can cause the CPU cost to grow quadratically, resulting in a Denial of Service (DoS) for any application decoding untrusted BER, CER, or DER input.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in pyasn1, a Python library for Abstract Syntax Notation One (ASN.1). The BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs. A remote attacker could exploit this by providing a specially crafted payload containing an OID with many arcs, leading to excessive CPU consumption and a denial of service (DoS) in applications that decode untrusted ASN.1 data. The corresponding encoders also exhibit this quadratic behavior when re-encoding attacker-supplied values.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
A remote attacker can exploit this by providing specially crafted BER/CER/DER-encoded ASN.1 data with a large exponent in the REAL value. When the application subsequently prints, logs, compares, or performs arithmetic on the decoded value, this can cause excessive CPU and memory consumption, leading to a denial of service (DoS).
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64 | — |
Vendor Fix
fix
Workaround
|
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"title": "Vulnerability description"
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{
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"title": "Vulnerability summary"
},
{
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"text": "This is an Important flaw in AIOHTTP, a Python asynchronous HTTP framework, due to potential arbitrary code execution. The vulnerability arises when applications load untrusted input into the `CookieJar.load()` function. Exploitation requires an application to be configured to process attacker-controlled files, which is not a default or common scenario in most Red Hat product deployments.",
"title": "Statement"
},
{
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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"integrityImpact": "HIGH",
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},
{
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
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"date": "2026-09-01T21:52:57+00:00",
"details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.3",
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},
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},
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"product_ids": [
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"title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
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},
"discovery_date": "2026-05-12T21:01:14.436876+00:00",
"ids": [
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"text": "A flaw was found in ip-address, a JavaScript library for parsing and manipulating IPv4 and IPv6 addresses. This vulnerability allows a remote attacker to perform cross-site scripting (XSS) by providing untrusted input to the Address6 constructor. When an application renders the output of Address6.group(), Address6.link(), or the AddressError.parseMessage as HTML without proper escaping, the attacker-controlled content can be executed in the user\u0027s browser.",
"title": "Vulnerability description"
},
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},
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"category": "external",
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-42338"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-42338",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42338"
},
{
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"summary": "https://github.com/beaugunderson/ip-address/security/advisories/GHSA-v2v4-37r5-5v8g",
"url": "https://github.com/beaugunderson/ip-address/security/advisories/GHSA-v2v4-37r5-5v8g"
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"release_date": "2026-05-12T19:43:16.470000+00:00",
"remediations": [
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"date": "2026-09-01T21:52:57+00:00",
"details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.3",
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},
{
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"cwe": {
"id": "CWE-829",
"name": "Inclusion of Functionality from Untrusted Control Sphere"
},
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"ids": [
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"notes": [
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"title": "Vulnerability description"
},
{
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"title": "Vulnerability summary"
},
{
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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},
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
},
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},
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"url": "https://github.com/vllm-project/vllm/pull/42616"
},
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"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
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"details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.3",
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"url": "https://access.redhat.com/errata/RHSA-2026:62335"
},
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"category": "workaround",
"details": "Upgrade to a vLLM build containing the fix (\u003e= 0.22.0) when available from Red Hat. Until then, only serve models from trusted registries, pin revisions explicitly, and review nested artifacts in model repositories before deployment.",
"product_ids": [
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"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
"version": "3.1"
},
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"threats": [
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"details": "Moderate"
}
],
"title": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls"
},
{
"cve": "CVE-2026-53923",
"cwe": {
"id": "CWE-824",
"name": "Access of Uninitialized Pointer"
},
"discovery_date": "2026-06-22T23:00:51.742636+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2491579"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM\u0027s GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users\u0027 requests. This constitutes an information disclosure vulnerability.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Information disclosure via integer truncation",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
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"summary": "Canonical URL",
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"category": "external",
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},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923"
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{
"category": "external",
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"attackVector": "NETWORK",
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"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
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"details": "Low"
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"title": "vllm: vLLM: Information disclosure via integer truncation"
},
{
"cve": "CVE-2026-54058",
"cwe": {
"id": "CWE-125",
"name": "Out-of-bounds Read"
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"discovery_date": "2026-07-14T17:02:34.864708+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500057"
}
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"notes": [
{
"category": "description",
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Memory disclosure or denial of service via crafted McIdas AREA image",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Pillow is vulnerable to an out-of-bounds read when processing a crafted uncompressed McIdas AREA image via the mmap raw codec. A remote attacker who can supply such an image to a service that loads it without user interaction may disclose adjacent process memory or trigger a denial of service. Fixed in Pillow 12.3.0.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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},
{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54058"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/6a8de891fb00968e5ea79bfa84368ed90b3cfc1d",
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"category": "external",
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},
{
"category": "external",
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"remediations": [
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"baseScore": 9.1,
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"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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"title": "Pillow: Pillow: Memory disclosure or denial of service via crafted McIdas AREA image"
},
{
"cve": "CVE-2026-54060",
"cwe": {
"id": "CWE-1050",
"name": "Excessive Platform Resource Consumption within a Loop"
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"discovery_date": "2026-07-06T20:02:29.509113+00:00",
"ids": [
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"system_name": "Red Hat Bugzilla ID",
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"category": "description",
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "python-pillow: Pillow: Denial of Service via excessive memory allocation when processing font files",
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{
"category": "other",
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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],
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-54060"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54060",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54060"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst",
"url": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst"
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"category": "external",
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"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-5x94-69rx-g8h2"
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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"title": "python-pillow: Pillow: Denial of Service via excessive memory allocation when processing font files"
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{
"cve": "CVE-2026-55379",
"cwe": {
"id": "CWE-770",
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"ids": [
{
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{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to cause a Denial of Service (DoS) by providing a specially crafted BDF font file. The library\u0027s image processing function fails to properly validate dimensions from the font file, bypassing a critical security check designed to prevent excessive memory usage. This oversight can lead to the system consuming an unreasonable amount of memory, making it unavailable to legitimate users.",
"title": "Vulnerability description"
},
{
"category": "summary",
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"title": "Vulnerability summary"
},
{
"category": "other",
"text": "A flaw was found in the Pillow Python imaging library. The BDF font file parser passes attacker-controlled dimensions to Image.new() without decompression bomb validation, allowing excessive memory allocation and denial of service.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497452"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-55379",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-55379"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55379",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55379"
},
{
"category": "external",
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},
{
"category": "external",
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},
{
"category": "workaround",
"details": "Do not load BDF font files from untrusted sources. Applications that only process standard image formats (PNG, JPEG, etc.) and do not use BdfFontFile or ImageFont.load() with BDF files are not affected.",
"product_ids": [
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"attackVector": "NETWORK",
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"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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},
{
"cve": "CVE-2026-55380",
"cwe": {
"id": "CWE-1285",
"name": "Improper Validation of Specified Index, Position, or Offset in Input"
},
"discovery_date": "2026-07-06T20:01:42.245051+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2497455"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted GD 2.x image file. The GdImageFile._open() function reads image dimensions without proper validation, leading to excessive memory allocation. This can result in a Denial of Service (DoS) due to C-heap exhaustion.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "python-pillow: Pillow: Denial of Service via crafted GD 2.x image file",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "A denial-of-service vulnerability was found in Pillow\u0027s GdImageFile plugin. The GdImageFile._open() function reads image dimensions from GD 2.x file headers and stores them without performing a decompression bomb check. A crafted .gd file of approximately 1 KB can trigger an unchecked 4.3 GB C-heap allocation, potentially crashing the process or exhausting memory on systems with insufficient resources. This issue is fixed in Pillow 12.3.0.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
"fixed": [
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:c89a11f9d1f3ec913be22a418b9d7667f6cda2aa7dd0556c796c90c1fc785096_amd64"
]
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"summary": "Canonical URL",
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{
"category": "external",
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{
"category": "external",
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{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55380"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst",
"url": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst"
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{
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{
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},
{
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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]
}
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},
{
"cve": "CVE-2026-59199",
"cwe": {
"id": "CWE-190",
"name": "Integer Overflow or Wraparound"
},
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"ids": [
{
"system_name": "Red Hat Bugzilla ID",
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}
],
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{
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Denial of Service via out-of-bounds write in image processing",
"title": "Vulnerability summary"
},
{
"category": "other",
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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],
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},
{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59199"
},
{
"category": "external",
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"url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
},
{
"category": "external",
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"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-6r8x-57c9-28j4"
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"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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}
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"details": "Important"
}
],
"title": "Pillow: Pillow: Denial of Service via out-of-bounds write in image processing"
},
{
"cve": "CVE-2026-59200",
"cwe": {
"id": "CWE-409",
"name": "Improper Handling of Highly Compressed Data (Data Amplification)"
},
"discovery_date": "2026-07-14T17:02:45.929986+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500060"
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],
"notes": [
{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the PdfParser.PdfStream.decode() function when processing a crafted FlateDecode PDF stream. By providing a specially designed PDF file, the attacker could cause the application to exhaust available memory, leading to a denial of service (DoS).",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Denial of service via crafted PDF stream",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The impact is limited to denial of service via memory exhaustion. A small crafted PDF input can force unbounded decompression, but this does not lead to code execution or information disclosure.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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{
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"summary": "RHBZ#2500060",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500060"
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{
"category": "external",
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-59200"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59200",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59200"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/f7a31ea75e460e108c37126da1f47812f21f6b09",
"url": "https://github.com/python-pillow/Pillow/commit/f7a31ea75e460e108c37126da1f47812f21f6b09"
},
{
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"url": "https://github.com/python-pillow/Pillow/pull/9718"
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{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
"url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
},
{
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"summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-jjj6-mw9f-p565",
"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-jjj6-mw9f-p565"
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"scope": "UNCHANGED",
"userInteraction": "NONE",
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"title": "Pillow: Pillow: Denial of service via crafted PDF stream"
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{
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"cwe": {
"id": "CWE-131",
"name": "Incorrect Calculation of Buffer Size"
},
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Denial of Service via crafted JPEG2000 image",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The impact is limited to denial of service via excessive memory allocation. A crafted JPEG2000 file can trigger out-of-memory failures, but this does not lead to code execution or information disclosure.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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],
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{
"category": "external",
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-59204"
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{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59204"
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"category": "workaround",
"details": "If the application does not need JPEG2000 support, block .jp2, .j2k, .jpf, and .jpx uploads at the input layer.\n\nFor services that do process JPEG2000, set memory limits on the process or container (LimitAS= in systemd, or memory limits in Kubernetes/Podman) so a crafted image can only crash the worker, not the whole host. Add automatic restarts (Restart=always in systemd, or container restart policies) so the service recovers from OOM kills without someone having to intervene.",
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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{
"cve": "CVE-2026-59205",
"cwe": {
"id": "CWE-843",
"name": "Access of Resource Using Incompatible Type (\u0027Type Confusion\u0027)"
},
"discovery_date": "2026-07-14T17:02:11.367224+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
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{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform\u0027s declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The impact is limited to denial of service. While the underlying issue is heap corruption via mode mismatch, it does not provide a controlled write primitive exploitable for code execution in affected Red Hat products.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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},
{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721",
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{
"category": "external",
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{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
"url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
},
{
"category": "external",
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"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-9hw9-ch79-4vh6"
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"date": "2026-09-01T21:52:57+00:00",
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"product_ids": [
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"category": "none"
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"scope": "UNCHANGED",
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"title": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API"
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{
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"id": "CWE-770",
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"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500204"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in pyasn1, a generic ASN.1 library for Python. The Basic Encoding Rules (BER) decoder, used by CER and DER codecs, processes long-form tags by accumulating continuation octets without an upper bound. A remote attacker can exploit this by providing a specially crafted input, leading to the construction of an arbitrarily large integer. This can cause the CPU cost to grow quadratically, resulting in a Denial of Service (DoS) for any application decoding untrusted BER, CER, or DER input.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "python-pyasn1: pyasn1: Denial of Service via crafted BER input",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "A flaw was found in pyasn1\u0027s BER decoder. The decoder parses long-form tags by accumulating continuation octets without an upper bound, allowing crafted input to force construction of an arbitrarily large integer with quadratic CPU cost. On Python 3.11+, this can also trigger unhandled ValueError exceptions. Exploitation requires that an application passes untrusted BER, CER, or DER input directly to the pyasn1 decoder \u2014 pyasn1 is a library and does not independently accept network input. Many common uses of pyasn1 involve parsing trusted certificates or local configuration and are not exposed to this flaw. The realistic impact is a denial-of-service of the consuming Python process, with automatic recovery in typical service deployments.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59884"
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"url": "https://github.com/pyasn1/pyasn1/releases/tag/v0.6.4"
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"date": "2026-09-01T21:52:57+00:00",
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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"title": "python-pyasn1: pyasn1: Denial of Service via crafted BER input"
},
{
"cve": "CVE-2026-59885",
"cwe": {
"id": "CWE-1050",
"name": "Excessive Platform Resource Consumption within a Loop"
},
"discovery_date": "2026-07-14T18:17:12.157807+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500380"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in pyasn1, a Python library for Abstract Syntax Notation One (ASN.1). The BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs. A remote attacker could exploit this by providing a specially crafted payload containing an OID with many arcs, leading to excessive CPU consumption and a denial of service (DoS) in applications that decode untrusted ASN.1 data. The corresponding encoders also exhibit this quadratic behavior when re-encoding attacker-supplied values.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "pyasn1: python-pyasn1: pyasn1: Denial of Service via crafted ASN.1 OBJECT IDENTIFIER",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "A flaw was found in pyasn1. The BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs. A small crafted payload containing an OID with many arcs consumes excessive CPU per decode() call, allowing denial of service in applications that decode untrusted ASN.1 data (certificates, LDAP, SNMP, Kerberos). The corresponding encoders exhibit the same behavior when re-encoding attacker-supplied values. The arc-size limit introduced for CVE-2026-23490 does not mitigate this issue. This issue is fixed in pyasn1 version 0.6.4.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"category": "external",
"summary": "RHBZ#2500380",
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"category": "external",
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-59885"
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"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59885"
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{
"category": "external",
"summary": "https://github.com/pyasn1/pyasn1/commit/45bdb19eb7df4b3780fe9c912c63e99bffc39dd9",
"url": "https://github.com/pyasn1/pyasn1/commit/45bdb19eb7df4b3780fe9c912c63e99bffc39dd9"
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"url": "https://github.com/pyasn1/pyasn1/releases/tag/v0.6.4"
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"url": "https://github.com/pyasn1/pyasn1/security/advisories/GHSA-8ppf-4f7h-5ppj"
}
],
"release_date": "2026-07-14T16:40:00.515000+00:00",
"remediations": [
{
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"date": "2026-09-01T21:52:57+00:00",
"details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.3",
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"products": [
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"title": "pyasn1: python-pyasn1: pyasn1: Denial of Service via crafted ASN.1 OBJECT IDENTIFIER"
},
{
"cve": "CVE-2026-59886",
"cwe": {
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},
"discovery_date": "2026-07-14T17:01:19.689422+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500041"
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],
"notes": [
{
"category": "description",
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "pyasn1: pyasn1: Denial of Service via crafted ASN.1 REAL values",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This flaw in pyasn1, rated as Important, could lead to a denial of service in Red Hat products that process untrusted ASN.1 data. A remote attacker could send a specially crafted BER, CER, or DER encoded REAL value with a large exponent, causing applications to consume excessive resources during decoding operations such as printing, logging, or arithmetic, thereby rendering the service unavailable.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59886"
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},
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"category": "external",
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"url": "https://github.com/pyasn1/pyasn1/security/advisories/GHSA-hm4w-wwcw-mr6r"
}
],
"release_date": "2026-07-14T16:38:29.691000+00:00",
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"date": "2026-09-01T21:52:57+00:00",
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],
"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:62335"
},
{
"category": "workaround",
"details": "When processing untrusted ASN.1 data with pyasn1, avoid calling prettyPrint(), str(), float(), int(), or performing comparisons or arithmetic on decoded Real (ASN.1 REAL type) objects. Instead, inspect the raw (mantissa, base, exponent) tuple directly. Where logging decoded ASN.1 structures is necessary, filter out or sanitize Real-typed values before conversion.",
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]
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"attackVector": "NETWORK",
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"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"products": [
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"title": "pyasn1: pyasn1: Denial of Service via crafted ASN.1 REAL values"
}
]
}
RHSA-2026:62336
Vulnerability from csaf_redhat - Published: 2026-09-01 21:55 - Updated: 2026-09-11 13:56A flaw was found in AIOHTTP, an asynchronous HTTP client/server framework for asyncio and Python. An attacker could exploit this vulnerability by providing untrusted input to the `CookieJar.load()` function. This could potentially lead to arbitrary code execution, allowing the attacker to run malicious code on the affected system. This issue is most likely to occur in applications that allow attacker-controlled files to be loaded.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
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A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in ip-address, a JavaScript library for parsing and manipulating IPv4 and IPv6 addresses. This vulnerability allows a remote attacker to perform cross-site scripting (XSS) by providing untrusted input to the Address6 constructor. When an application renders the output of Address6.group(), Address6.link(), or the AddressError.parseMessage as HTML without proper escaping, the attacker-controlled content can be executed in the user's browser.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Vendor Fix
fix
Workaround
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A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Vendor Fix
fix
Workaround
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A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Vendor Fix
fix
Workaround
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A flaw was found in Pillow prior to 12.3.0. When an uncompressed McIdas AREA image is loaded from a filename through the mmap raw codec path, attacker-controlled header words can set a row stride smaller than the natural row width. Pixel access such as Image.tobytes(), getpixel, convert, or save can then read beyond the mapped region, disclosing adjacent process memory or causing a fault.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
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| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — | ||
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
A flaw was found in Pillow, a Python imaging library. When processing a specially crafted font file, the library's font compilation function does not adequately check for excessive memory allocation. This oversight allows a remote attacker to trigger an unreasonable consumption of system memory, leading to a denial of service (DoS) for the application.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
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| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
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| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — | ||
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to cause a Denial of Service (DoS) by providing a specially crafted BDF font file. The library's image processing function fails to properly validate dimensions from the font file, bypassing a critical security check designed to prevent excessive memory usage. This oversight can lead to the system consuming an unreasonable amount of memory, making it unavailable to legitimate users.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
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A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted GD 2.x image file. The GdImageFile._open() function reads image dimensions without proper validation, leading to excessive memory allocation. This can result in a Denial of Service (DoS) due to C-heap exhaustion.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
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A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the library's image processing functions, specifically when handling image coordinates near certain limits. This flaw, a native heap out-of-bounds write, could lead to a denial of service (DoS), making the affected system or application unavailable.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
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| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the PdfParser.PdfStream.decode() function when processing a crafted FlateDecode PDF stream. By providing a specially designed PDF file, the attacker could cause the application to exhaust available memory, leading to a denial of service (DoS).
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted JPEG2000 image file. Due to incorrect calculation of memory requirements for image tiles, processing this file can lead to excessive memory consumption, resulting in a denial of service (DoS) through out-of-memory failures.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform's declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A flaw was found in pyasn1, a generic ASN.1 library for Python. The Basic Encoding Rules (BER) decoder, used by CER and DER codecs, processes long-form tags by accumulating continuation octets without an upper bound. A remote attacker can exploit this by providing a specially crafted input, leading to the construction of an arbitrarily large integer. This can cause the CPU cost to grow quadratically, resulting in a Denial of Service (DoS) for any application decoding untrusted BER, CER, or DER input.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
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|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A flaw was found in pyasn1, a Python library for Abstract Syntax Notation One (ASN.1). The BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs. A remote attacker could exploit this by providing a specially crafted payload containing an OID with many arcs, leading to excessive CPU consumption and a denial of service (DoS) in applications that decode untrusted ASN.1 data. The corresponding encoders also exhibit this quadratic behavior when re-encoding attacker-supplied values.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
A remote attacker can exploit this by providing specially crafted BER/CER/DER-encoded ASN.1 data with a large exponent in the REAL value. When the application subsequently prints, logs, compares, or performs arithmetic on the decoded value, this can cause excessive CPU and memory consumption, leading to a denial of service (DoS).
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64 | — |
Vendor Fix
fix
Workaround
|
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64 | — |
Workaround
|
|
| Unresolved product id: Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64 | — |
Workaround
|
{
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}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
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"title": "aiohttp: AIOHTTP: Arbitrary code execution via untrusted input to CookieJar.load()"
},
{
"cve": "CVE-2026-41523",
"cwe": {
"id": "CWE-617",
"name": "Reachable Assertion"
},
"discovery_date": "2026-06-22T23:01:00.799590+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2491582"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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{
"category": "external",
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},
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"category": "external",
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"version": "3.1"
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"title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
},
{
"cve": "CVE-2026-42338",
"cwe": {
"id": "CWE-79",
"name": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)"
},
"discovery_date": "2026-05-12T21:01:14.436876+00:00",
"ids": [
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"title": "Vulnerability description"
},
{
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"title": "Vulnerability summary"
},
{
"category": "other",
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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},
{
"cve": "CVE-2026-47155",
"cwe": {
"id": "CWE-829",
"name": "Inclusion of Functionality from Untrusted Control Sphere"
},
"discovery_date": "2026-06-22T23:00:54.705583+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2491580"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Moderate impact. The flaw is a supply-chain integrity issue when operators pin a HuggingFace model revision but vLLM may still load nested artifacts from an unpinned revision. It affects Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI images that ship vLLM versions prior to 0.22.0. KServe control-plane components that bundle vLLM as a library are not affected.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
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{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6",
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}
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"scores": [
{
"cvss_v3": {
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"attackVector": "NETWORK",
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"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
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"version": "3.1"
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"title": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls"
},
{
"cve": "CVE-2026-53923",
"cwe": {
"id": "CWE-824",
"name": "Access of Uninitialized Pointer"
},
"discovery_date": "2026-06-22T23:00:51.742636+00:00",
"ids": [
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}
],
"notes": [
{
"category": "description",
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"title": "Vulnerability description"
},
{
"category": "summary",
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"title": "Vulnerability summary"
},
{
"category": "other",
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"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"name": "Out-of-bounds Read"
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{
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{
"cve": "CVE-2026-59886",
"cwe": {
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"title": "Vulnerability summary"
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"title": "Statement"
},
{
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"category": "vendor_fix",
"date": "2026-09-01T21:55:16+00:00",
"details": "The container images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.3",
"product_ids": [
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64"
],
"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:62336"
},
{
"category": "workaround",
"details": "When processing untrusted ASN.1 data with pyasn1, avoid calling prettyPrint(), str(), float(), int(), or performing comparisons or arithmetic on decoded Real (ASN.1 REAL type) objects. Instead, inspect the raw (mantissa, base, exponent) tuple directly. Where logging decoded ASN.1 structures is necessary, filter out or sanitize Real-typed values before conversion.",
"product_ids": [
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64"
]
}
],
"scores": [
{
"cvss_v3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"products": [
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:db445687f68381ba4348925b32b7782fb3424eefd0a6e58e200a9ad69685551a_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:82f19ded4eab2cd19f81187b2318897daa180a0edf04eaed9b45c3d9d27b2fe5_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:e78b6e0cc5eeb8a46beda69660499f08322fa0d59d382dcd1349af2a0e96e352_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:5f4fc4b62366cd6599d914e31b51a35f7072ea4f5aaae077769b69a0c4cde849_arm64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:6689af3952e683d9a963a884b3b35979483560e6610fea0ccdda20d14ca51dee_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:7a63d3116445dfc773d9b004b4c31e2f3ee7ef244a783c2e3d16582e285b35e5_amd64",
"Red Hat Enterprise Linux AI 3.3:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:baba8ef26e81ee1cb529c894b5f0587588537de87d3a29267fc243812c29fa5a_amd64"
]
}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "pyasn1: pyasn1: Denial of Service via crafted ASN.1 REAL values"
}
]
}
WID-SEC-W-2026-1889
Vulnerability from csaf_certbund - Published: 2026-06-10 22:00 - Updated: 2026-06-10 22:00| Product | Identifier | Version | Remediation |
|---|---|---|---|
|
Open Source vllm <0.22.0
Open Source / vllm
|
<0.22.0 |
{
"document": {
"aggregate_severity": {
"text": "mittel"
},
"category": "csaf_base",
"csaf_version": "2.0",
"distribution": {
"tlp": {
"label": "WHITE",
"url": "https://www.first.org/tlp/"
}
},
"lang": "de-DE",
"notes": [
{
"category": "legal_disclaimer",
"text": "Das BSI ist als Anbieter f\u00fcr die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch daf\u00fcr verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgf\u00e4ltig im Einzelfall zu pr\u00fcfen."
},
{
"category": "description",
"text": "Open Source vLLM ist eine Open-Source-Bibliothek f\u00fcr schnelle und effiziente Inferenz von Large Language Models (LLMs).",
"title": "Produktbeschreibung"
},
{
"category": "summary",
"text": "Ein entfernter, anonymer Angreifer kann eine Schwachstelle in vllm ausnutzen, um Dateien zu manipulieren.",
"title": "Angriff"
},
{
"category": "general",
"text": "- Linux\n- UNIX",
"title": "Betroffene Betriebssysteme"
}
],
"publisher": {
"category": "other",
"contact_details": "csaf-provider@cert-bund.de",
"name": "Bundesamt f\u00fcr Sicherheit in der Informationstechnik",
"namespace": "https://www.bsi.bund.de"
},
"references": [
{
"category": "self",
"summary": "WID-SEC-W-2026-1889 - CSAF Version",
"url": "https://wid.cert-bund.de/.well-known/csaf/white/2026/wid-sec-w-2026-1889.json"
},
{
"category": "self",
"summary": "WID-SEC-2026-1889 - Portal Version",
"url": "https://wid.cert-bund.de/portal/wid/securityadvisory?name=WID-SEC-2026-1889"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-3ww4-5jv9-j5gm vom 2026-06-10",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
}
],
"source_lang": "en-US",
"title": "vllm: Schwachstelle erm\u00f6glicht Manipulation von Daten",
"tracking": {
"current_release_date": "2026-06-10T22:00:00.000+00:00",
"generator": {
"date": "2026-06-11T11:16:00.878+00:00",
"engine": {
"name": "BSI-WID",
"version": "1.6.0"
}
},
"id": "WID-SEC-W-2026-1889",
"initial_release_date": "2026-06-10T22:00:00.000+00:00",
"revision_history": [
{
"date": "2026-06-10T22:00:00.000+00:00",
"number": "1",
"summary": "Initiale Fassung"
}
],
"status": "final",
"version": "1"
}
},
"product_tree": {
"branches": [
{
"branches": [
{
"branches": [
{
"category": "product_version_range",
"name": "\u003c0.22.0",
"product": {
"name": "Open Source vllm \u003c0.22.0",
"product_id": "T055280"
}
},
{
"category": "product_version",
"name": "0.22.0",
"product": {
"name": "Open Source vllm 0.22.0",
"product_id": "T055280-fixed",
"product_identification_helper": {
"cpe": "cpe:/a:vllm:vllm:0.22.0"
}
}
}
],
"category": "product_name",
"name": "vllm"
}
],
"category": "vendor",
"name": "Open Source"
}
]
},
"vulnerabilities": [
{
"cve": "CVE-2026-47155",
"product_status": {
"known_affected": [
"T055280"
]
},
"release_date": "2026-06-10T22:00:00.000+00:00",
"title": "CVE-2026-47155"
}
]
}
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.