vLLM — Vulnerability (CVE-2026-34760)
Date: 2026-04
Affected: vllm/vllm
Attack vector: other
Category: vulnerability-disclosure
Severity: Medium
CVE: CVE-2026-34760
CWE: CWE-20
CVSS: 5.9 (CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L)

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.


Mitigations:

Architectural layers (MAESTRO):