CVE-2026-47155
Received Received - Intake
Supply Chain Integrity Flaw in vLLM

Publication date: 2026-06-22

Last updated on: 2026-06-22

Assigner: GitHub, Inc.

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.
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Meta Information
Published
2026-06-22
Last Modified
2026-06-22
Generated
2026-06-23
AI Q&A
2026-06-23
EPSS Evaluated
N/A
NVD
CVE-2026-47155
Affected Vendors & Products
Currently, no data is known.
Helpful Resources
Exploitability
CWE
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KEV
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CWE ID Description
CWE-345 The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.
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Executive Summary

The vulnerability in vLLM prior to version 0.22.0 involves its revision pinning controls not consistently applying to all artifacts loaded for a model. This means that even if a deployment specifies a particular model revision using --revision or --code-revision, vLLM can still load dynamic code, GGUF files, image processors, retrieval side weights, or subfolder weights/configurations from an unpinned or default revision.

As a result, operators might believe they are serving a reviewed and fixed model revision, but vLLM could be using nested or sibling artifacts from outside that reviewed revision, leading to a supply-chain integrity issue.

This vulnerability was fixed in version 0.22.0 of vLLM.

Impact Analysis

This vulnerability can impact you by undermining the integrity of the model deployment. Even if you specify a particular model revision, the system might load unreviewed or unintended artifacts, which can alter the model's behavior.

This can lead to unexpected or incorrect model outputs, potentially causing reliability and trust issues in applications relying on the model.

Since the issue is related to supply-chain integrity, it could also expose the deployment to risks from malicious or compromised artifacts being loaded without the operator's knowledge.

Mitigation Strategies

To mitigate this vulnerability, upgrade vLLM to version 0.22.0 or later, where the issue with revision pinning controls has been fixed.

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