CVE-2026-34760

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.

The following products are affected by CVE-2026-34760 vulnerability.

Here, you will find a curated list of external links that provide in-depth information to CVE-2026-34760.

URL	Resource
https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4
https://github.com/vllm-project/vllm/pull/37058
https://github.com/vllm-project/vllm/releases/tag/v0.18.0
https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8
https://nvd.nist.gov/vuln/detail/CVE-2026-34760
https://www.cve.org/CVERecord?id=CVE-2026-34760