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?
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0.06% chance of exploitation
EPSS score — low exploit probability
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Not on CISA KEV list
No confirmed active exploitation reported to CISA
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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