- CVSS
- MEDIUM · 6.5v3.1CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Published
- 2026-09-16
- Weakness
- CWE-400, CWE-770
- Source
- nvd.nist.gov/vuln/detail/CVE-2026-69147
Description
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.
References
- 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
- https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2026-69147 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.