https://db.gcve.eu Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Fri, 30 Oct 2026 17:36:16 +0000 https://db.gcve.eu/vuln/fkie_cve-2026-115913 <p>vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the Phi-4 multimodal audio encoder in phi4mm_audio.py and phi4mm_utils.py uses eager attention by default, allowing a remote client to submit one legal long audio clip that causes MultiHeadedAttention.forward_attention to materialize and retain full attention matrices in self.attn. The scaled dot-product attention branch does not materialize these matrices. The resulting quadratic GPU-memory use can cause an out-of-memory condition and terminate EngineCore for all users. This issue is fixed in version 0.32.0.</p> <p>vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the Phi-4 multimodal audio encoder in phi4mm_audio.py and phi4mm_utils.py uses eager attention by default, allowing a remote client to submit one legal long audio clip that causes MultiHeadedAttention.forward_attention to materialize and retain full attention matrices in self.attn. The scaled dot-product attention branch does not materialize these matrices. The resulting quadratic GPU-memory use can cause an out-of-memory condition and terminate EngineCore for all users. This issue is fixed in version 0.32.0.</p> https://db.gcve.eu/vuln/fkie_cve-2026-115913 https://db.gcve.eu/vuln/ghsa-8397-8qf6-f2gm <p><strong>Affected:</strong> PyPI: vllm</p> <p>### Summary</p> <p>The Phi-4 multimodal audio encoder (`phi4mm_audio.py` / `phi4mm_utils.py`) runs its conformer attention in **eager mode by default**: the official HF config of `microsoft/Phi-4-multimodal-instruct` does not set `use_pt_scaled_dot_product_attention`, so the default `False` applies (`phi4mm_audio.py:166`, `:876`; `phi4mm_utils.py:1669`). The eager path fully materializes the attention probability matrix of shape `[n_chunks, 16, 500, 500]` per layer — and then **stores it on the module**: `self.attn = attn` (`phi4mm_utils.py:1802`, a `torch.jit.Attribute` declared at `:1693`), keeping a live reference for every one of the 24 encoder layers.</p> <p>The only gates on audio length are the server-side limits `VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=25` and `VLLM_MAX_AUDIO_DECODE_DURATION_S=600` (`envs.py:83-84`) — i.e. a 10-minute clip is a **legal input by default**. A 600 s clip becomes ~60,000 mel frames, which the encoder unfolds into 500-frame chunks (`phi4mm_audio.py:996`, `max_seq_len = 500`), giving `n_chunks ≈ 120`. Each layer therefore materializes a `120×16×500×500` matrix (~1–2 GiB depending on dtype), and the module attribute keeps all completed layers&#39; matrices alive, so GPU memory climbs monotonically **within a single request** until an allocation fails.</p> <p>Verified end-to-end on the official `vllm/vllm-openai:v0.31.0` image: one 19.2 MB silent 600 s WAV → worker `torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.29 GiB ... 501.75 MiB free ... 22.09 GiB a…</p> <p><strong>Affected:</strong> PyPI: vllm</p> <p>### Summary</p> <p>The Phi-4 multimodal audio encoder (`phi4mm_audio.py` / `phi4mm_utils.py`) runs its conformer attention in **eager mode by default**: the official HF config of `microsoft/Phi-4-multimodal-instruct` does not set `use_pt_scaled_dot_product_attention`, so the default `False` applies (`phi4mm_audio.py:166`, `:876`; `phi4mm_utils.py:1669`). The eager path fully materializes the attention probability matrix of shape `[n_chunks, 16, 500, 500]` per layer — and then **stores it on the module**: `self.attn = attn` (`phi4mm_utils.py:1802`, a `torch.jit.Attribute` declared at `:1693`), keeping a live reference for every one of the 24 encoder layers.</p> <p>The only gates on audio length are the server-side limits `VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=25` and `VLLM_MAX_AUDIO_DECODE_DURATION_S=600` (`envs.py:83-84`) — i.e. a 10-minute clip is a **legal input by default**. A 600 s clip becomes ~60,000 mel frames, which the encoder unfolds into 500-frame chunks (`phi4mm_audio.py:996`, `max_seq_len = 500`), giving `n_chunks ≈ 120`. Each layer therefore materializes a `120×16×500×500` matrix (~1–2 GiB depending on dtype), and the module attribute keeps all completed layers&#39; matrices alive, so GPU memory climbs monotonically **within a single request** until an allocation fails.</p> <p>Verified end-to-end on the official `vllm/vllm-openai:v0.31.0` image: one 19.2 MB silent 600 s WAV → worker `torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.29 GiB ... 501.75 MiB free ... 22.09 GiB a…</p> https://db.gcve.eu/vuln/ghsa-8397-8qf6-f2gm