# CVE-2026-34760: vLLM-project vLLM improper input validation in audio downmixing

Source: https://junglewise.ai/threats/cve-2026-34760-vllm-project-vllm-improper-input-validation-in-audio-downmixing  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-04-02. Updated: 2026-07-24  
License: CC BY 4.0 with a link back

CVE-2026-34760 · Severity: medium · CVSS 5.9 · Published 2026-04-02

vLLM is a high-performance engine used to run and serve large language models (LLMs), including those that process audio. A vulnerability in its audio processing component causes a discrepancy between how humans hear audio and how the AI model interprets it due to non-standard audio mixing. This could lead to the AI model misinterpreting audio commands or data, potentially affecting the integrity of automated decisions or transcriptions.

Technologies: [vllm (PyPI)](https://junglewise.ai/threats/technologies/pypi-vllm), [Vllm](https://junglewise.ai/threats/technologies/vllm). Vendors: [PyPI](https://junglewise.ai/threats/vendors/pypi), [Vllm](https://junglewise.ai/threats/vendors/vllm).

## Quick answers

**Is CVE-2026-34760 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-34760 being exploited in the wild.

**How severe is CVE-2026-34760?** Medium severity, with a CVSS score of 5.9, published 2 April 2026.

**Which products and versions are affected?** vllm (PyPI) ECOSYSTEM:introduced=0.5.5,fixed=0.18.0.

**Is there a fix for CVE-2026-34760?** Yes. vllm (PyPI): upgrade to 0.18.0 or later. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2026-34760 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-34760?** Check whether you run vllm (PyPI) or Vllm at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## How to fix

- vllm (PyPI): upgrade to 0.18.0 or later

  ```sh
  pip install "vllm>=0.18.0"
  ```


## Technical details

vLLM (versions 0.5.5 to 0.17.x) utilized the Librosa library for audio processing, which defaulted to using 'numpy.mean' for mono downmixing (to_mono). This deviates from the international standard ITU-R BS.775-4, which specifies a weighted downmixing algorithm. This discrepancy creates a 'semantic gap' where the audio processed by the AI model (via Librosa) does not match the audio heard by humans. An attacker could potentially exploit this inconsistency to provide audio that is interpreted differently by the model than by a human reviewer. The issue was addressed in version 0.18.0 by removing the Librosa dependency and replacing it with pyav for audio processing.

## Affected products

- vllm-project vllm >= 0.5.5, < 0.18.0

## Identifiers

- CVE-2026-34760
- GHSA-6c4r-fmh3-7rh8
- PYSEC-2026-2299

## Timeline

- 2026-03-21: patched: Fix merged into main branch via PR 37058
- 2026-04-02: disclosed: CVE-2026-34760 published

## References

- [https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4](https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4)
- [https://github.com/vllm-project/vllm/pull/37058](https://github.com/vllm-project/vllm/pull/37058)
- [https://github.com/vllm-project/vllm/releases/tag/v0.18.0](https://github.com/vllm-project/vllm/releases/tag/v0.18.0)
- [https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8](https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8)

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