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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",
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},
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{
"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",
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"0.18.0",
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"0.19.0",
"0.19.1",
"0.2.0",
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"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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"0.8.4",
"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: 2026-08-26 16:44A 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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"id": "CWE-824",
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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: 2026-08-26 16:44A 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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"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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},
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},
{
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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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"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
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"title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
},
{
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"name": "Inclusion of Functionality from Untrusted Control Sphere"
},
"discovery_date": "2026-06-22T23:00:54.705583+00:00",
"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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"summary": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac",
"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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],
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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"
},
{
"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"
},
{
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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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{
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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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},
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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: 2026-08-26 16:44A 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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"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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]
},
"references": [
{
"category": "self",
"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-55574"
},
{
"category": "external",
"summary": "RHBZ#2497509",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497509"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-55574",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-55574"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55574",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55574"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/2b3006076c5e9bc4cda9e03e3641388de3c5c286",
"url": "https://github.com/vllm-project/vllm/commit/2b3006076c5e9bc4cda9e03e3641388de3c5c286"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/pull/45118",
"url": "https://github.com/vllm-project/vllm/pull/45118"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-rwxx-mrjm-wc2m",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-rwxx-mrjm-wc2m"
}
],
"release_date": "2026-07-06T20:05:31.003000+00:00",
"remediations": [
{
"category": "vendor_fix",
"date": "2026-08-26T16:25:47+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
"product_ids": [
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
],
"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:60363"
},
{
"category": "workaround",
"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.",
"product_ids": [
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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 AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
"Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
]
}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API"
}
]
}
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.