With default static video configuration, mm_config.media_io_kwargs["video"] does not name PyNvVideoCodec and VLLM_VIDEO_LOADER_BACKEND defaults to OpenCV/software decoding. The worker therefore reserves no PyNv decoder/CUDA-context bytes. A later request can still set media_io_kwargs.video.video_backend="pynvvideocodec" and reach the GPU decoder path because that runtime field is intentionally honored by VideoMediaIO.
PoV
An ordinary multimodal inference request can carry the backend override in the request body:
The bounded check validates current source markers, simulates the exact static reservation predicate, and compares vulnerable and negative-control configurations. Key output:
The negative control is important: when PyNvVideoCodec is selected statically, the worker reserves 2066953011 bytes per API process for decoder surfaces plus CUDA context. The vulnerable case reserves 0 bytes for the same decoder overhead because PyNvVideoCodec is selected only by the later request. A second control with static OpenCV plus mm_ipc_gpu_memory_gb=0.25 reserves only the raw-frame semaphore budget and still does not reserve PyNv decoder/CUDA-context bytes.
Impact
An attacker who can submit video requests to a vLLM deployment with PyNvVideoCodec available can force frontend GPU decoding even when the engine did not reserve memory for that decoder during startup. On high-utilization serving deployments, the unreserved CUDA context, retained decoder surfaces, and decoded-frame allocations can reduce or exhaust GPU memory that the engine assumed was available for weights, activations, or KV cache, causing request failures, worker crashes, or service-level denial of service.
Suggested severity is Medium with conservative CVSS v3.1 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H (6.5). If a deployment exposes the affected API without authentication, PR:N would raise the deployment-specific score. Suggested weaknesses are CWE-770 (Allocation of Resources Without Limits or Throttling) and CWE-400 (Uncontrolled Resource Consumption). This should not be rated Low because the affected resource is shared GPU memory in the serving path and the code already treats the PyNv decoder/CUDA-context footprint as large enough to reserve at startup when statically configured.
Limitations: exploitation requires a GPU deployment where PyNvVideoCodec is installed and usable, and the request must reach a video-capable model/path. The issue does not claim code execution, data disclosure, or SSRF.
Suggested Fix
Do not allow untrusted request fields to select a GPU decoder that was not included in startup memory reservation. The simplest fix is to reject request-level media_io_kwargs.video.video_backend="pynvvideocodec" unless the static server configuration already selected PyNvVideoCodec and reserved its decoder/CUDA-context budget.
If dynamic backend selection remains supported, split software and GPU decoder policies: allow request selection among CPU/software decoders only, require an explicit operator allowlist for GPU decoders, and include every request-selectable GPU decoder in the startup reservation predicate. Add regression coverage for static OpenCV startup config plus request-level PyNvVideoCodec override, and preserve the negative control where static PyNvVideoCodec configuration reserves decoder/CUDA-context bytes.
Affected Package/Versions
Package: vllm from vllm-project/vllm.
Confirmed affected: current main at ddd3855a28a561a5bb54d380c6e6b8b1e883cc4a.
Introduced by: af16446bf39de047ab57649c933063cf1cbf1e50, Vram semaphore infra (#44465), committed 2026-06-26T17:32:51-07:00.
Release status checked: git tag --contains af16446bf returned no release tags in the fresh checkout. GitHub repository metadata reported latest published release v0.23.0 published 2026-06-15T05:27:20Z; the local v0.24.0 tag also does not contain the introducing commit. The affected range should therefore be current main builds containing af16446bf until fixed, rather than a confirmed released-version range.
Advisory History
Public vLLM advisories checked included audio decompression-bomb DoS, unbounded video/jpeg frame-count DoS, MediaConnector SSRF, video processing RCE, multimodal embedding DoS/RCE, GGUF GPU memory exposure, multimodal hashing, and other request-parameter DoS classes. None matched request-selected PyNvVideoCodec or the static VRAM reservation mismatch.
Prior local/private vLLM report families checked included request-level media_io_kwargs reopening video/jpeg frame fanout, GLM video metadata amplification, and audio media decode duration-limit bypass. Those reports share the request-level media kwargs boundary, but they target CPU/media decode limits or model metadata amplification. This report targets a different privileged asset and fix surface: GPU decoder selection after engine startup memory reservation.
Focused GitHub issue/PR searches for pynvvideocodec, mm_ipc_gpu_memory, video_backend media_io_kwargs, Vram semaphore infra, and frontend multimodal GPU decoding found the PyNvVideoCodec zero-copy RFC, an old do-not-review prototype, merged PR #44465, and an unrelated TorchCodec backend PR. No public issue or PR described this security boundary.