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Review reviewCritical

CVE-2026-22807

vllm-project vllm, Red Hat AI Inference Server 3.2, Red Hat AI Inference Server 3.3

vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacker-controlled Python code in a model repo/path to execute at server startup. An attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve arbitrary code execution on the vLLM host during model load. This happens before any request handling and does not require API access. Version 0.14.0 fix...

CVSS
9.8
EPSS
0.73%
50.6% percentile
CISA KEV
Not listed
Published
2026.01.22
PRIORITY ASSESSMENT

Review review

The CVSS severity warrants an early asset and exposure review.

Known exploitationNot established by KEV
Exploit probability0.73%
Technical severityCVSS 9.8

Vulnerability overview

vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacker-controlled Python code in a model repo/path to execute at server startup. An attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve arbitrary code execution on the vLLM host during model load. This happens before any request handling and does not require API access. Version 0.14.0 fix...

Affected product and versions

Product
vllm-project vllm, Red Hat AI Inference Server 3.2, Red Hat AI Inference Server 3.3
Affected versions
>= >= 0.10.1, < 0.14.0, >= 0.10.1 < 0.14.0
Fixed versions
0.14.0

Recommended response sequence

Confirm exposure before applying a vendor-supported change.

Full remediation guide
  1. 1
    Identify

    Confirm that vllm-project vllm, Red Hat AI Inference Server 3.2, Red Hat AI Inference Server 3.3 and an affected version are present.

  2. 2
    Prioritize

    Combine exploitation signals with asset exposure and business criticality.

  3. 3
    Remediate

    Follow the vendor advisory or supported update path and preserve rollback options.

  4. 4
    Verify

    Recheck the version, service health, access paths, and relevant logs.

Technical data

CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE
CWE-94
CVE-2026-22807 — vllm-project vllm, Red Hat AI Inference Server 3.2, Red Hat AI Inference Server 3.3 | SECUFOCUS NOW