HighVulnerabilityLLM-specific

CVE-2026-114053: vLLM arbitrary Python execution via mm_processor_kwargs code_revision

Source: NVD(opens in a new tab)
Identifier: CVE-2026-114053
Published: October 20, 2026
Record updated: October 21, 2026

Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.

Summary

vLLM from 0.7.3 before 0.31.0 forwards the request-supplied mm_processor_kwargs.code_revision to Transformers AutoProcessor.from_pretrained, letting clients choose which revision of a model's dynamic processor code is imported when trust_remote_code is enabled. An attacker who controls a revision in the served model repository can send a multimodal request to the unauthenticated /tokenize endpoint and execute arbitrary Python in the vLLM API process.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 8.1(high)
CVSS vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
Attack vector: network
Attack complexity: high
Privileges required: none
User interaction: none
EPSS: Not scored. FIRST had not published an EPSS score for this CVE when it was last checked on October 21, 2026.
Disclosure date: October 20, 2026

Weaknesses and techniques

Weakness (CWE): CWE-94
Attack pattern (CAPEC): CAPEC-242
AI threat technique (MITRE ATLAS): AML.T0010

Classification

Attack type: Supply chain,
Other
Attack sophistication: Moderate
AI component targeted: Inference
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2026-114053

First tracked: October 21, 2026 at 02:08 AM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 95% (self-assessed)

Topics

Inference infrastructure

Summary

Mitigation

Topics

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