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VulnerabilityPublished

CVE-2026-34760: vllm · Improper Input Validation

Affects vllmSource CVE Database V5

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Not known to be exploited · CVSS 5.9 Medium : upgrade to 0.18.0 when convenient.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 3.1 base score
5.9Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.2% chance of exploitation in 30 days – higher than 18% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.18.0
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 2 Apr 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.

What to do

Upgrade vllm and vllm-project/vllm to 0.18.0.

Affected software

ProductAffectedFixed in
vllm-projectvllmAffected
  • >=0.5.5 <0.18.0
Fixed in
  • 0.18.0
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threatsAffected
  • <0.18.0 >=0.5.5
Fixed in
  • 0.18.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

A fix is available in vLLM version 0.18.0. Users should upgrade to this version or later to resolve the improper input validation issue related to audio downmixing. No additional mitigation steps are indicated.

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.

References

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