Vulnerability · CVE-2026-34760
vLLM is an inference and serving engine for large language models (LLMs).
Published Apr 2, 2026 · catalog updated Jul 24, 2026 · vLLM is an inference
Check if you run it
No confirmed exploitation is on record for CVE-2026-34760. EPSS is 0.48%. Act only if a listed version of vLLM is an inference is installed.
Gaps on this record
- The stored product name is a package slug. The name on this page is taken from the title (vLLM is an inference).
- No version range is stored. A customer cannot check an installed version against this record.
- No vendor fix or required action is stored. Compare the linked advisory before you change production.
- Severity
- High7.1
- Known exploitation
- Not in CISA/VulnCheck KEV
- EPSS
- 0.48%
- Remediation
- Advisory link
- Affected products
- cpe vllm/vllm
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.
Are you affected?
Enter the version you run. The result uses only the ranges stored on this record.
The check returns affected, not affected, or incomplete. It does not guess a version that is missing from the record.
- cpe vllm/vllmversion unspecified
How to fix CVE-2026-34760
Recommended remediation
A patch or advisory reference is linked. Open the original publisher for availability by product version.
Official advisories
What to do
Three questions, each answered from this record only. Confirm the vendor advisory before you patch.
Should you patch?
No confirmed exploitation is on record for CVE-2026-34760. EPSS is 0.48%. Act only if a listed version of vLLM is an inference is installed.
Which versions are affected?
Affected version ranges are not on this record. Compare the installed version with the linked vendor advisory before you change production.
What should an analyst verify?
CWE-20: Improper Input Validation. EPSS 0.48%. There is no CISA KEV listing. No Exploit-DB or Metasploit reference is linked. Use the source links for the original advisory.
Code
The project this record applies to. Exploit listings stay in References.
Timeline
Detection, updates, and resolution for this record.
Apr 2, 2026 · NIST NVD
Detected
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 au
Jul 24, 2026 · NIST NVD
Updated
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 au
Sources
- CVE.orgVulnerability recordCVE-2026-34760
Official CVE identifier
cve.org - FIRST EPSSExploitation probabilityCVE-2026-34760
30-day exploitation probability 0.5%
first.org - NIST NVDVulnerability recordCVE-2026-34760
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. Th
nvd.nist.gov
References
Freshness
Last verified in this catalog Oct 18, 2026. Significant claims (KEV, EPSS, CVSS, affected ranges) come from the source rows above — open the original publisher for the authoritative advisory.