Your RSA-2048 keys break in 2030. Find every one of them before attackers do.
🐍
🐍 PyPI
Not in CISA KEV
MEDIUM severity

CVE-2026-47155 — vllm

MEDIUMFix: vllm-project/vllm@d26a28a

CVE-2026-47155 is a medium-severity (CVSS 6.5) CWE-345 vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.

vLLM: Artifact Pin Decay in vLLM allows pinned deployments to load unpinned code, weights, and processors

Also known asGHSA-3ww4-5jv9-j5gmPYSEC-2026-2301
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Oct 22, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for CVE-2026-47155.

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs15th percentile — riskier than 15% of all scored CVEsHighest risk
0.00%0.25%0.50%0.75%0.1%0.2%0.2%0.2%0.2%Jul 26Sep 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

CVE-2026-47155 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 394,415 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

1 pkg affected
🐍vllm

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

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:1045-1051 and :1058-1064
    • _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.
  • vllm/model_executor/model_loader/gguf_loader.py:58-60
    • The direct-file GGUF form repo/file.gguf calls hf_hub_download(repo_id=repo_id, filename=filename) without passing revision.
  • 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 passing model_config.revision.
  • vllm/model_executor/models/kimi_audio.py:92-95
    • Kimi-Audio loads Whisper config from the whisper-large-v3 subfolder without a revision argument.
  • vllm/model_executor/models/kimi_audio.py:425-430
    • Kimi-Audio declares same-repository whisper-large-v3 secondary weights with revision=None.
  • vllm/model_executor/model_loader/default_loader.py:287-301
    • The default loader preserves model_config.revision for 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.py L111-L114 — 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.
  • vllm/model_executor/models/kimi_audio.py L425-L430 — 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.
  • vllm/model_executor/models/kimi_audio.py L92-L95 — Kimi-Audio loads Whisper config from the same repository's 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.
  • vllm/model_executor/models/registry.py L1058-L1064 — 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.
  • vllm/model_executor/model_loader/gguf_loader.py L58-L60 — 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.
  • vllm/model_executor/models/registry.py L1045-L1051 — 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.
  • vllm/model_executor/models/roberta.py L203-L209 — 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.

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.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIvllmall versions0.22.0pip install --upgrade 'vllm==0.22.0'

Affected Products

1 product · 1 configurations
Application
vllmvllm
< 0.22.0
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update vllm to 0.22.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-47155 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatModerate

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…

Workaround published by Red Hat
Upgrade to a vLLM build containing the fix (>= 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.
Source: Red Hat security advisory for CVE-2026-47155 (CC BY 4.0)
ProductFixed inAdvisory
Red Hat AI Inference Server 3.3rhaiis/vllm-cuda-rhel9:1787161382RHSA-2026:59138
Red Hat AI Inference Server 3.3rhaiis/vllm-rocm-rhel9:1787161803RHSA-2026:59139
Red Hat AI Inference Server 3.3rhaiis/vllm-spyre-rhel9:1787161776RHSA-2026:60363
Red Hat Enterprise Linux AI 3.3rhelai3/disk-image-cuda-rhel9:1788290314RHSA-2026:62335
Red Hat Enterprise Linux AI 3.3rhelai3/bootc-aws-cuda-rhel9:1788273908RHSA-2026:62336

Frequently Asked Questions

### Summary 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 invarian
O3 Security · Impact-Aware SCA

Is CVE-2026-47155 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2026-47155: vllm RCE — Fixed in 0.22.0