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vLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models

  • Disclosed: 02.04.2026
  • Updated: 11.10.2026 14:18

Issue Description

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).

https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478

Attack Scenario and Impact

LFE (Low-Frequency Effects) Channel Exploit

Attackers can craft special multichannel audio files containing:

  1. Normal content in front channels (L/R)
  2. Either interference signals or hidden content in the LFE channel

Notice: It is worth noting that not only the LFE channel is excluded, but in fact, channels beyond the 6th (such as rear surround channels, overhead channels, height speakers, etc.) are also not supported.

Attack Methodology:

Attackers can create specially engineered multichannel audio with LFE interference, where front channels (L/R) contain normal content while the LFE channel carries interference signals or hidden content. When played on consumer devices that ignore LFE channels, only the normal content is heard. However, when processed by AI systems using Librosa (which mixes all channels), the LFE interference affects speech recognition feature extraction or masks critical detection features. This enables malicious content to bypass AI detection while still reaching end users, potentially compromising voice authentication systems, evading content moderation, or disrupting speech recognition accuracy.

Potential Exploitation Scenarios:

  • Voice authentication systems may be tricked into accepting anomalous audio
  • Content moderation systems may fail to detect prohibited content hidden in LFE channels
  • Speech recognition systems may produce incorrect transcriptions

Note: torch.audio implements this correctly. Failure to do so may lead to inconsistencies between training and test audio, resulting in performance degradation.

Resources

Fixes

Affected software

Click a technology to see all vulnerabilities affecting it.

PlatformPackageVulnerable versionsFixed in
Python (PyPI) vllm >= 0.5.5, < 0.18.0 0.18.0

References

CVSS score 5.9/10
CVSS vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L
Weakness (CWE)
CWE-20
Platform
Python (PyPI)
Package
vllm
Disclosed
02.04.2026
Updated
11.10.2026 14:18

Sources and licenses

OSV / GitHub Advisory Database - PYSEC-2026-2299 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - GHSA-6c4r-fmh3-7rh8 - CC BY 4.0

Vulnerability history: vllm

All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.

All (94) in the archive
Total entries
94
First → latest
09.2024 10.2026
Without a fix
5
Exploited in attacks
0

Severity breakdown

  • Critical 9
  • High 23
  • Medium 47
  • Low 4
  • Unrated 11

Disclosures per year

Severity:
  1. A flaw has been found in vllm-project vLLM up to 0.26.0. This vulnerability affects unknown code of the file rust/src/parser/src/unified/gemma4.rs of the component Gemma4UnifiedParser. Executing a man
    High · 7.5 CVE-2026-103241 < 0.27.0 0.27.0
  2. vLLM versions 0.22.0 through 0.23.0 fail to validate stop_token_ids against vocabulary bounds in Rust HTTP and gRPC frontends, allowing out-of-vocabulary token IDs to reach MinTokensLogitsProcessor. A
    High · 7.5 CVE-2026-100652 >= 0.22.0, < 0.24.0 0.24.0
  3. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    High · 7.5 CVE-2026-54234 >= 0.17.1, < 0.24.0 0.24.0
  4. vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends
    High · 7.5 CVE-2026-55574 < 0.24.0 0.24.0
  5. vLLM denial of service via prompt embeds on M-RoPE models
    High CVE-2026-55514 >= 0.12.0, < 0.24.0 0.24.0
  6. vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package.
    High · 8.8 CVE-2026-54232 < 0.22.1 0.22.1
  7. vLLM: OpenAI auth bypass
    Critical · 9.1 CVE-2026-48746 >= 0.3.0, < 0.22.0 0.22.0
  8. vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution
    High · 7.5 CVE-2026-41523 < 0.22.0 0.22.0
  9. vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processin
    High · 7.5 CVE-2026-5497 >= 0.8.0, < 0.19.0 0.19.0
  10. vLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models this entry
    Medium · 5.9 CVE-2026-34760 >= 0.5.5, < 0.18.0 0.18.0
  11. vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out
    High · 8.8 CVE-2026-27893 >= 0.10.1, < 0.18.0 0.18.0
  12. vLLM has RCE In Video Processing
    Critical · 9.8 CVE-2026-22778 >= 0.8.3, < 0.14.1 0.14.1
  13. vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector
    High · 7.1 CVE-2026-24779 < 0.14.1 0.14.1
  14. vLLM affected by RCE via auto_map dynamic module loading during model initialization
    High · 8.8 CVE-2026-22807 >= 0.10.1, < 0.14.0 0.14.0
  15. vLLM introduced enhanced protection for CVE-2025-62164
    High · 8.8 CVE-2026-56340 >= 0.10.2, < 0.13.0 0.13.0
  16. vLLM vulnerable to remote code execution via transformers_utils/get_config
    High · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  17. vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs
    High · 6.5 CVE-2025-62372 >= 0.5.5, < 0.11.1 0.11.1
  18. vLLM deserialization vulnerability leading to DoS and potential RCE
    High · 8.8 CVE-2025-62164 >= 0.10.2, < 0.11.1 0.11.1
  19. vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
    High · 7.1 CVE-2025-6242 >= 0.5.0, < 0.11.0 0.11.0
  20. vLLM is vulnerable to timing attack at bearer auth
    High · 7.5 CVE-2025-59425 < 0.11.0 0.11.0
  21. vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder
    High · 8.8 CVE-2025-9141 >= 0.10.0, < 0.10.1.1 0.10.1.1
  22. vllm API endpoints vulnerable to Denial of Service Attacks
    High · 7.5 CVE-2025-48956 >= 0.1.0, < 0.10.1.1 0.10.1.1
  23. vLLM Allows Remote Code Execution via PyNcclPipe Communication Service
    Critical · 9.8 CVE-2025-47277 >= 0.6.5, < 0.8.5 0.8.5
  24. Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration
    High · 8.0 CVE-2025-30165 >= 0.5.2, < 0.10.0 0.10.0
  25. vLLM Vulnerable to Remote Code Execution via Mooncake Integration
    Critical · 10.0 CVE-2025-32444 >= 0.6.5, < 0.8.5 0.8.5
  26. Data exposure via ZeroMQ on multi-node vLLM deployment
    High · 7.5 CVE-2025-30202 >= 0.5.2, < 0.8.5 0.8.5
  27. CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0
    Critical · 9.8 ghsa-ggpf-24jw-3fcw < 0.8.0 0.8.0
  28. vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object
    Critical · 9.8 CVE-2024-9052 <= 0.8.1 no fix
  29. vLLM Deserialization of Untrusted Data vulnerability
    Critical · 9.8 CVE-2024-11041 <= 0.6.2 no fix
  30. vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
    Critical · 9.8 CVE-2024-9053 <= 0.6.0 no fix
  31. vLLM Allows Remote Code Execution via Mooncake Integration
    Critical · 9.0 CVE-2025-29783 >= 0.6.5, < 0.8.0 0.8.0
  32. vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator
    High · 7.5 CVE-2025-24357 < 0.7.0 0.7.0
  33. vLLM denial of service vulnerability
    High · 7.5 CVE-2024-8768 < 0.5.5 0.5.5

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