Vulnerability 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.
CVSS Score
MEDIUM
Affected Products
| Vendor | Product | Versions |
|---|---|---|
| Vllm | Vllm | >= 0.5.5, < 0.18.0 |
Related Weaknesses (CWE)
References
- https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9bPatch
- https://github.com/vllm-project/vllm/pull/37058Issue Tracking
- https://github.com/vllm-project/vllm/releases/tag/v0.18.0Release Notes
- https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8Vendor Advisory
FAQ
What is CVE-2026-34760?
CVE-2026-34760 is a vulnerability with a CVSS score of 5.9 (MEDIUM). 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 int...
How severe is CVE-2026-34760?
CVE-2026-34760 has been rated MEDIUM with a CVSS base score of 5.9/10. Review the CVSS metrics above for detailed severity breakdown.
Is there a patch for CVE-2026-34760?
Check the references section above for vendor advisories and patch information. Affected products include: Vllm Vllm.