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CVE-2026-34760
MEDIUM5.9
Description
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.
CVE Details
CVSS v3.1 Score5.9
SeverityMEDIUM
CVSS VectorCVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L
Attack VectorNETWORK
ComplexityHIGH
Privileges RequiredLOW
User InteractionNONE
Published4/2/2026
Last Modified4/3/2026
Sourcenvd
Honeypot Sightings0
Weaknesses (CWE)
CWE-20
References
https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4(security-advisories@github.com)
https://github.com/vllm-project/vllm/pull/37058(security-advisories@github.com)
https://github.com/vllm-project/vllm/releases/tag/v0.18.0(security-advisories@github.com)
https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8(security-advisories@github.com)
IOC Correlations
No correlations recorded
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