CVE-2026-34760

low-risk
Published 2026-04-02

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.

Do I need to act?

-
0.06% chance of exploitation
EPSS score — low exploit probability
-
Not on CISA KEV list
No confirmed active exploitation reported to CISA
?
Patch status unknown
Check vendor advisories for fix availability and mitigation guidance
5
CVSS 5.9/10 Medium
NETWORK / HIGH complexity
23
/ 100
low-risk
Severity 18/34 · Moderate
Exploitability 0/34 · Minimal
Exposure 5/34 · Minimal