Vulnerability · Published Sep 16, 2026 · Assigned by GitHub_M

CVE-2026-69147vLLM: Request-selected PyNvVideoCodec GPU decode bypasses static VRAM reservation

Medium 6.5

In plain English

A medium-severity uncontrolled resource consumption in vllm from vllm-project. Proof-of-concept exploit code is public. The chance it is exploited in the next 30 days is estimated at 0.6% (higher than 48% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.

Severity (CVSS 3.1)6.5Medium
Exploit odds (30 days)0.6%top 52.0% of all CVEs
Exploited in the wildNo reportsCISA SSVC: poc
Attack toolingNoneno public tooling known

Official 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.

Affected packages & fixed versions

EcosystemPackageFixed inAdvisory
PyPIvllm0.28.0GHSA-8pw2-6jv3-mj5j
PyPIvllm0.28.0PYSEC-2026-4178

Affected products (as reported)

VendorProductVersions
vllm-project vllm < 0.28.0

Weakness: CWE-400, CWE-770 · Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

References