CVE-2026-34760vLLM is an inference and serving engine for large language models (LLMs).
Updated under an hour ago ()
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No exploitation reported and no EPSS estimate yet.
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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
- 7.1 HIGH (v3.1, NVD)CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L
- Weakness (CWE)
- CWE-20
- Known exploited
- Not in CISA's KEV catalog
- EPSS (exploitation in the next 30 days)
- Not loaded for this CVE
- Assigned by
- Not given
- NVD analysis
- Analyzed
- Record last updated
- Jul 24, 2026
- First seen by InfoSec Signals
- Oct 9, 2026
Affected products
vllm vllm vllm:vllm | from 0.5.5 before 0.18.0 | NVD |
References
- https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4patch
- 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
The CVE record on cve.org NVD's entry
The CVE record is authoritative. Products marked “from the name” were identified from the CNA's free-text names, not a CPE, and are matched less certainly. This product uses the NVD API but is not endorsed or certified by the NVD.