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Medium

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

Type: advisorySources: OSV

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.

Products
Not specified
Affected versions
Not specified
Fixed versions
Not specified
CVEs
CVE-2026-69147
Confirmed fixed CVEs
Not specified
Updated in this feed
4 Nov 2026, 16:25 UTC · Revision 1

Source evidence

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

core-osv · Original source

Published 16 Sept 2026, 17:49 UTC

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.

osv:CVE-2026-69147
Complete source record
{
  "id": "6e9f7c5064f4cfe118bb0eee85288625038ae8dc242370b6d204364611d40718",
  "url": "https://osv.dev/vulnerability/CVE-2026-69147",
  "method": "structured",
  "payload": {
    "kind": "advisory",
    "title": "vLLM: Request-selected PyNvVideoCodec GPU decode bypasses static VRAM reservation",
    "summary": "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.",
    "evidence": "osv:CVE-2026-69147",
    "products": [],
    "severity": "medium",
    "structured": {
      "id": "CVE-2026-69147",
      "aliases": [
        "CVE-2026-69147",
        "GHSA-8pw2-6jv3-mj5j",
        "PYSEC-2026-4178"
      ],
      "metrics": [
        {
          "type": "CVSS_V3",
          "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H"
        }
      ],
      "affected": [
        {
          "ranges": [
            {
              "repo": "https://github.com/vllm-project/vllm",
              "type": "GIT",
              "events": [
                {
                  "introduced": "0"
                },
                {
                  "fixed": "2cf0a6915ce544dc493a0990f2ea38d81601128a"
                },
                {
                  "fixed": "283893c72292ede38d277e3cd2b9b64c3e4f1dda"
                },
                {
                  "fixed": "ba22152096b2484faa3579624a253d54804d876d"
                }
              ],
              "database_specific": {
                "extracted_events": [
                  {
                    "cpe": "cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*",
                    "range": [
                      {
                        "introduced": "0"
                      },
                      {
                        "fixed": "0.28.0"
                      }
                    ],
                    "source": "CPE_RANGE"
                  },
                  {
                    "range": [
                      {
                        "fixed": "283893c72292ede38d277e3cd2b9b64c3e4f1dda"
                      }
                    ],
                    "source": "REFERENCES_COMMIT"
                  },
                  {
                    "range": [
                      {
                        "fixed": "ba22152096b2484faa3579624a253d54804d876d"
                      }
                    ],
                    "source": "REFERENCES_COMMIT"
                  }
                ]
              }
            }
          ],
          "versions": [
            "v0.23.1rc0",
            "v0.22.1rc0",
            "v0.21.1rc0",
            "v0.20.2rc0",
            "v0.20.1rc0",
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            "v0.16.0rc1",
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            "v0.16.0rc0",
            "v0.15.0rc1",
            "v0.14.0rc1",
            "v0.14.0rc0",
            "v0.13.0rc1",
            "v0.11.1",
            "v0.11.1rc6",
            "v0.11.1rc5",
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            "v0.11.1rc3",
            "v0.11.1rc2",
            "v0.11.1rc1",
            "v0.11.1rc0",
            "v0.11.0rc1",
            "v0.10.2rc2",
            "v0.10.2rc1",
            "v0.10.1rc1",
            "v0.10.0rc2",
            "v0.10.0",
            "v0.10.0rc1",
            "v0.9.2rc1",
            "v0.9.1rc2",
            "v0.9.1",
            "v0.9.1rc1",
            "v0.9.0",
            "v0.8.4",
            "v0.8.3rc1",
            "v0.8.2",
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            "v0.7.0",
            "v0.6.6.post1",
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            "v0.6.3",
            "v0.6.2",
            "v0.6.1.post2",
            "v0.6.1.post1",
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            "v0.1.4",
            "v0.1.3",
            "v0.1.2",
            "v0.1.1",
            "v0.1.0",
            "submission"
          ],
          "database_specific": {
            "source": "https://storage.googleapis.com/cve-osv-conversion/osv-output/CVE-2026-69147.json"
          }
        }
      ],
      "modified": "2026-10-28T02:30:37.318854922Z",
      "provider": "osv",
      "references": [
        "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/69xxx/CVE-2026-69147.json",
        "https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j",
        "https://nvd.nist.gov/vuln/detail/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"
      ],
      "provider_metadata": {
        "cwe_ids": [
          "CWE-400",
          "CWE-770"
        ],
        "cna_assigner": "GitHub_M",
        "osv_generated_from": "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/69xxx/CVE-2026-69147.json"
      }
    },
    "related_cves": [
      {
        "value": "CVE-2026-69147",
        "evidence": "/id"
      }
    ],
    "security_relevant": true,
    "severity_evidence": "/severity/0/score"
  },
  "source_id": "core-osv",
  "document_id": 504641,
  "published_at": "2026-09-16T17:49:20.427+00:00"
}