https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Mon, 12 Oct 2026 19:48:30 +0000 https://cve.radiocsirt.org/vuln/euvd-2026-280130 EUVD-2026-280130 https://cve.radiocsirt.org/vuln/euvd-2026-280130 https://cve.radiocsirt.org/vuln/fkie_cve-2026-34760 <p>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.</p> <p>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.</p> https://cve.radiocsirt.org/vuln/fkie_cve-2026-34760 https://cve.radiocsirt.org/vuln/ghsa-6c4r-fmh3-7rh8 <p><strong>Affected:</strong> PyPI: vllm</p> <p>## 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).</p> <p>https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478 ## Attack Scenario and Impact</p> <p>### 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</p> <p>**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.</p> <p>**Attack Methodology:**</p> <p>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 conte…</p> <p><strong>Affected:</strong> PyPI: vllm</p> <p>## 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).</p> <p>https://github.com/librosa/librosa/blob/af8c839fb15317fa2712ea66e7a22da6a9267b32/librosa/core/audio.py#L478 ## Attack Scenario and Impact</p> <p>### 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</p> <p>**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.</p> <p>**Attack Methodology:**</p> <p>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 conte…</p> https://cve.radiocsirt.org/vuln/ghsa-6c4r-fmh3-7rh8 https://cve.radiocsirt.org/vuln/pysec-2026-2299 <p><strong>Affected:</strong> PyPI: vllm</p> <p>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.</p> <p><strong>Affected:</strong> PyPI: vllm</p> <p>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.</p> https://cve.radiocsirt.org/vuln/pysec-2026-2299 https://cve.radiocsirt.org/vuln/wid-sec-w-2026-0987 <p>Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Dateien zu manipulieren, Sicherheitsmaßnahmen zu umgehen, vertrauliche Informationen offenzulegen oder einen Denial-of-Service-Zustand herbeizuführen.</p> <p>Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Dateien zu manipulieren, Sicherheitsmaßnahmen zu umgehen, vertrauliche Informationen offenzulegen oder einen Denial-of-Service-Zustand herbeizuführen.</p> https://cve.radiocsirt.org/vuln/wid-sec-w-2026-0987