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Description

vLLM 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.

PUBLISHED Reserved 2026-05-18 | Published 2026-06-22 | Updated 2026-06-22 | Assigner GitHub_M




MEDIUM: 6.5CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N

Problem types

CWE-345: Insufficient Verification of Data Authenticity

Product status

< 0.22.0
affected

References

github.com/...t/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm

github.com/vllm-project/vllm/pull/42616

github.com/...ommit/d26a28ab033697f55a1414b5b0435de7cd6045b6

huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac

cve.org (CVE-2026-47155)

nvd.nist.gov (CVE-2026-47155)

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