Resource Exhaustion Affecting vllm-cu129 package, versions <0.28.0-r0
Threat Intelligence
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-MINIMOSLATEST-VLLMCU129-19925930
- published19 Sept 2026
- disclosed16 Sept 2026
Introduced: 16 Sep 2026
NewCVE-2026-69147 (opens in a new tab)How to fix?
Upgrade Minimos:latest vllm-cu129 to version 0.28.0-r0 or higher.
NVD Description
Note: Versions mentioned in the description apply only to the upstream vllm-cu129 package and not the vllm-cu129 package as distributed by Minimos.
See How to fix? for Minimos:latest relevant fixed versions and status.
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://www.cve.org/CVERecord?id=CVE-2026-69147
- https://github.com/advisories/GHSA-8pw2-6jv3-mj5j
- https://osv.dev/vulnerability/MINI-6cp8-3j5g-h76r
- https://osv.dev/vulnerability/CVE-2026-69147
- 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