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CVE-2026-47155
Published:June 22, 2026
Updated:June 29, 2026
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.
Affected Packages
https://github.com/vllm-project/vllm.git (GITHUB):
Affected version(s) >=v0.1.0 <v0.22.0
Fix Suggestion:
Update to version v0.22.0
vllm (PYTHON):
Affected version(s) >=0.0.1 <0.22.0
Fix Suggestion:
Update to version 0.22.0
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CVSS v4
Base Score:
8.3
Attack Vector
NETWORK
Attack Complexity
HIGH
Attack Requirements
NONE
Privileges Required
NONE
User Interaction
NONE
Vulnerable System Confidentiality
LOW
Vulnerable System Integrity
HIGH
Vulnerable System Availability
NONE
Subsequent System Confidentiality
NONE
Subsequent System Integrity
NONE
Subsequent System Availability
NONE
CVSS v3
Base Score:
6.5
Attack Vector
NETWORK
Attack Complexity
HIGH
Privileges Required
NONE
User Interaction
NONE
Scope
UNCHANGED
Confidentiality
LOW
Integrity
HIGH
Availability
NONE
Weakness Type (CWE)
Insufficient Verification of Data Authenticity
EPSS
Base Score:
0.15