High severity · published Apr 2, 2026
CVE-2026-34760
vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models
7.1
CVSS
0.3%
EPSS
No
On KEV
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 vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Confidentiality
None
Integrity
High
Availability
Low
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.3% · higher than 19% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
VL
vllm · vllm-project
>= 0.5.5, < 0.18.0 · source: vendor, NVD
References
NVD
https://nvd.nist.gov/vuln/detail/CVE-2026-34760
CVE.org
https://www.cve.org/CVERecord?id=CVE-2026-34760
Patch
https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4
Issue Tracking
https://github.com/vllm-project/vllm/pull/37058
Release Notes
https://github.com/vllm-project/vllm/releases/tag/v0.18.0
Vendor Advisory
https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8
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