CVE-2026-34760

high

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.

References

https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8

https://github.com/vllm-project/vllm/releases/tag/v0.18.0

https://github.com/vllm-project/vllm/pull/37058

https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4

Details

Source: Mitre, NVD

Published: 2026-04-02

Updated: 2026-05-11

Risk Information

CVSS v2

Base Score: 7.5

Vector: CVSS2#AV:N/AC:L/Au:S/C:N/I:C/A:P

Severity: High

CVSS v3

Base Score: 7.1

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

Severity: High

EPSS

EPSS: 0.00057