Resource Exhaustion The advisory has been revoked - it doesn't affect any version of package py3-vllm-cuda-12.4  (opens in a new tab)

The Chainguard security team deemed this advisory irrelevant for Chainguard:latest.

Note: Versions mentioned in the description apply only to the upstream py3-vllm-cuda-12.4 package and not the py3-vllm-cuda-12.4 package as distributed by Chainguard.

vLLM is an inference and serving engine for large language models. Prior to 0.28.0, request bodies for Chat Completions and Responses can set media_io_kwargs.video.video_backend to pynvvideocodec, and MediaConnector.fetch_video forwards that choice to VideoMediaIO even when startup configuration selected a software decoder. The engine's _reserve_mm_ipc_gpu_memory logic budgets decoder memory only from static configuration, so the request-selected VIDEO_LOADER_REGISTRY backend can create a CUDA context, decoder surfaces, and decoded-frame allocations that were not removed from the engine's KV-cache budget. An attacker able to submit video requests to a video-capable GPU deployment with PyNvVideoCodec installed can exhaust shared GPU memory, causing request failures, worker crashes, or denial of service. The first release containing the fix is version 0.28.0.

• https://github.com/vllm-project/vllm/commit/283893c72292ede38d277e3cd2b9b64c3e4f1dda
• https://github.com/vllm-project/vllm/commit/ba22152096b2484faa3579624a253d54804d876d
• https://github.com/vllm-project/vllm/pull/47259
• https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j