CVE-2026-34760 MEDIUM

CVE-2026-34760: vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

Vendor Vllm-Project
Product vllm
Weakness CWE-20 · Input validation
Published April 2, 2026
Last update April 3, 2026

CVSS base score

5.9/10
Attack vector Network
Attack complexity High
Privileges required Low
User interaction None
Confidentiality None
Integrity High

CVSS vector

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

What the vulnerability does

01Description

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0.

Key dates

02Disclosure timeline

April 2, 2026 CVE published
April 3, 2026 Record updated