CVE-2026-34760
MEDIUMDescription
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.
How to fix
Remediation is compiled from vendor and distribution security advisories. Always confirm against the linked source for your exact version and platform.
CVSS v3 Vector
Exploitability
Impact
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L
Exploit Intelligence
Low risk: more likely to be exploited than 18% of all known CVEs.
References
Related Vulnerabilities
Other CWE-20 (Improper Input Validation) vulnerabilities, ordered by exploit likelihood. View all
| CVE | Severity | CVSS | EPSS | Exploited | Fix |
|---|---|---|---|---|---|
| CVE-2024-3400 | Critical | 10.0 | 100% | KEV + Ransom | - |
| CVE-2021-45105 | Medium | 5.9 | 100% | - | Fix |
| CVE-2021-44228 | Critical | 10.0 | 100% | KEV + Ransom | Fix |
| CVE-2021-21985 | Critical | 9.8 | 100% | KEV + Ransom | Fix |
| CVE-2018-7600 | Critical | 9.8 | 100% | KEV + Ransom | Fix |
| CVE-2020-3452 | High | 7.5 | 100% | KEV | Fix |
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Markdown
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Start freeThis product uses NVD data but is not endorsed or certified by the NVD. EPSS scores courtesy of FIRST.org (https://www.first.org/epss). Source: CISA KEV Catalog. Data as of 2026-05-11.