CVE-2026-34760 | vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models
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.
Conclusion & alert: CVE-2026-34760 is rated Low Risk (30/100): CVSS Medium severity, with low exploitation likelihood (EPSS 0.06%).Mandatory action: Monitor for updates and reassess as exploit intelligence or EPSS changes.
Risk is dynamic; we continuously reassess and refresh what is shown on this page as upstream context changes.
Exploit prediction scoring system (EPSS) score for CVE-2026-34760
EPSS lead: Daily EPSS estimates relative likelihood of exploitation; percentile ranks this CVE among scored vulnerabilities (higher = more severe relative rank).