CVE-2026-34760 - vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

CVE-2026-34760 - vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

https://cvefeed.io/rssfeed/latest.atom / Fri, 04/03/2026 - 00:10
CVE ID :CVE-2026-34760
Published : April 2, 2026, 8:16 p.m. | 50 minutes ago
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.
Severity: 5.9 | MEDIUM
Visit the link for more details, such as CVSS details, affected products, timeline, and more...

About

Kenya Education Network CERT(KENET-CERT) is a Cybersecurity Emergency Response Team and Co-ordination Center operated by the National Research and Education Network of Kenya. KENET-CERT coordination center promotes awareness on cybersecurity incidences as well as coordinates and assists member institutions in responding effectively to cyber security threats and incidences. KENET-CERT works closely with Kenya's National CIRT coordination center (CIRT/CC) as a sector CIRT for the academic institutions. KENET promotes use of ICT in Teaching, Learning and Research in Higher Education Institutions in Kenya. KENET aims to interconnect all the Universities, Tertiary and Research Institutions in Kenya by setting up a cost effective and sustainable private network with high speed access to the global Internet. KENET also facilitates electronic communication among students and faculties in member institutions, share learning and teaching resources by collaboration in Research and Development of Educational content.