{"id":"CVE-2026-69147","title":"vLLM is an inference and serving engine for large language models","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 …","severity":"medium","cvss":6.5,"cvssVector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","cwe":["CWE-400","CWE-770"],"vendor":"vllm-project","product":"vllm","affected":["vllm < 0.28.0"],"published":"2026-09-16","updated":"2026-09-16","sourceUpdated":"2026-09-16T19:17:26.113","source":"NVD","sourceUrl":"https://nvd.nist.gov/vuln/detail/CVE-2026-69147","references":[{"url":"https://github.com/vllm-project/vllm/commit/283893c72292ede38d277e3cd2b9b64c3e4f1dda","label":"security-advisories@github.com"},{"url":"https://github.com/vllm-project/vllm/commit/ba22152096b2484faa3579624a253d54804d876d","label":"security-advisories@github.com"},{"url":"https://github.com/vllm-project/vllm/pull/47259","label":"security-advisories@github.com"},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j","label":"security-advisories@github.com"},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j","label":"134c704f-9b21-4f2e-91b3-4a467353bcc0"},{"url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-69147.json"},{"url":"https://access.redhat.com/security/cve/CVE-2026-69147"},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2535581"},{"url":"https://www.cve.org/CVERecord?id=CVE-2026-69147"},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2026-69147"},{"url":"https://github.com/vllm-project/vllm/releases/tag/v0.25.0"},{"url":"https://github.com/advisories/GHSA-8pw2-6jv3-mj5j"},{"url":"https://github.com/vllm-project/vllm"}],"tags":["nvd","cve.org","exploit-available","csaf","vex","red-hat","ghsa","pip","osv"],"exploitAvailable":true,"ssvc":{"exploitation":"poc","automatable":"no","technicalImpact":"partial","timestamp":"2026-09-16T18:37:08.090589Z"},"ingestedAt":"2026-09-16T18:01:17.455Z","aliases":["GHSA-8pw2-6jv3-mj5j"],"ecosystem":"pip","patched":["vllm 0.28.0"],"epss":0.0046,"epssPercentile":0.39005,"slug":"CVE-2026-69147","body":"## Overview\n\nvLLM 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.\n\n## Remediation\n\nRefer to the linked advisories for vendor-supplied fixes and affected version ranges.\n\n## Vendor advisories\n\n- **Red Hat VEX** · Moderate · affected: Red Hat AI Inference Server, Red Hat Enterprise Linux AI (RHEL AI) 3, Red Hat OpenShift AI (RHOAI) · no fix planned: Red Hat AI Inference Server, Red Hat Enterprise Linux AI (RHEL AI) 3, Red Hat OpenShift AI (RHOAI) · updated 2026-09-16 · [vex](https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-69147.json)\n\n## Package advisory (CVE-2026-69147)\n\nAffected packages:\n\n- `vllm < 0.28.0`\n\nPatched in:\n\n- `vllm 0.28.0`\n\nSource: https://github.com/advisories/GHSA-8pw2-6jv3-mj5j","depth":"twilight","depthScore":48,"depthScoreParts":{"impact":35.8,"likelihood":0.1,"exploitation":12,"ransomware":0},"changes":[{"seq":205676,"id":"CVE-2026-69147","ts":1789585391200,"field":"exploit_available","old":"false","new":"true"}]}