## 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 - [ITU-R BS.775-4 Standard](https://www.itu.int/dms_pubrec/itu-r/rec/bs/R-REC-BS.775-4-202212-I!!PDF-E.pdf) - [Librosa Source Code](https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478) - [Librosa securty report](https://github.com/librosa/librosa/security/advisories/GHSA-vfm7-86xr-5mrh) ## Fixes - https://github.com/vllm-project/vllm/pull/37058, which removes the librosa dependency from vLLM.
## 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 - [ITU-R BS.775-4 Standard](https://www.itu.int/dms_pubrec/itu-r/rec/bs/R-REC-BS.775-4-202212-I!!PDF-E.pdf) - [Librosa Source Code](https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478) - [Librosa securty report](https://github.com/librosa/librosa/security/advisories/GHSA-vfm7-86xr-5mrh) ## Fixes - https://github.com/vllm-project/vllm/pull/37058, which removes the librosa dependency from vLLM.
Update vllm to 0.18.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models affects vllm (pip). Severity is medium. ## 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 - [ITU-R BS.775-4 Standard](https://www.itu.int/dms_pubrec/itu-r/rec/bs/R-REC-BS.775-4-202212-I!!PDF-E.pdf) - [Librosa Source Code](https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478) - [Librosa securty report](https://github.com/librosa/librosa/security/advisories/GHSA-vfm7-86xr-5mrh) ## Fixes - https://github.com/vllm-project/vllm/pull/37058, which removes the librosa dependency from vLLM.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.5.5,<0.18.0 | 0.18.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate vllm to 0.18.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models affects vllm (pip). Severity is medium. ## 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 - [ITU-R BS.775-4 Standard](https://www.itu.int/dms_pubrec/itu-r/rec/bs/R-REC-BS.775-4-202212-I!!PDF-E.pdf) - [Librosa Source Code](https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478) - [Librosa securty report](https://github.com/librosa/librosa/security/advisories/GHSA-vfm7-86xr-5mrh) ## Fixes - https://github.com/vllm-project/vllm/pull/37058, which removes the librosa dependency from vLLM.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.5.5,<0.18.0 | 0.18.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard