CVE-2026-69147
vLLM: Request-selected PyNvVideoCodec GPU decode bypasses static VRAM reservation
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.
INFO
Published Date :
Sept. 16, 2026, 6:17 p.m.
Last Modified :
Oct. 7, 2026, 2:16 p.m.
Remotely Exploit :
Yes !
Source :
[email protected]
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS | 134c704f-9b21-4f2e-91b3-4a467353bcc0 | |||||
| CVSS 3.1 | MEDIUM | [email protected] |
Solution
- Update vLLM to version 0.28.0 or later.
- Ensure PyNvVideoCodec is not used for video processing.
- Remove GPU memory budget checks for video decoding.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-69147.
| URL | Resource |
|---|---|
| https://github.com/vllm-project/vllm/commit/283893c72292ede38d277e3cd2b9b64c3e4f1dda | Patch |
| https://github.com/vllm-project/vllm/commit/ba22152096b2484faa3579624a253d54804d876d | Patch |
| https://github.com/vllm-project/vllm/pull/47259 | Issue Tracking Patch |
| https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j | Exploit Vendor Advisory |
| https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j | Exploit Vendor Advisory |
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-69147 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-69147
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-69147 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-69147 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
Initial Analysis by [email protected]
Oct. 07, 2026
Action Type Old Value New Value Added CPE Configuration OR *cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* versions up to (excluding) 0.28.0 Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/commit/283893c72292ede38d277e3cd2b9b64c3e4f1dda Types: Patch Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/commit/ba22152096b2484faa3579624a253d54804d876d Types: Patch Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/pull/47259 Types: Issue Tracking, Patch Added Reference Type CISA-ADP: https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j Types: Exploit, Vendor Advisory Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j Types: Exploit, Vendor Advisory -
CVE Modified by 134c704f-9b21-4f2e-91b3-4a467353bcc0
Sep. 16, 2026
Action Type Old Value New Value Added Reference https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j Added SSVC {'id': 'CVE-2026-69147', 'role': 'CISA Coordinator', 'options': [{'exploitation': 'poc'}, {'automatable': 'no'}, {'technicalImpact': 'partial'}], 'version': '2.0.3', 'timestamp': '2026-09-16T18:37:08.090589Z'} -
New CVE Received by [email protected]
Sep. 16, 2026
Action Type Old Value New Value Added 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. Added CVSS V3.1 AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H Added CWE CWE-400 Added CWE CWE-770 Added Affected New affected value received. <a href="https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/69xxx/CVE-2026-69147.json">CVE-2026-69147</a> Added Reference https://github.com/vllm-project/vllm/commit/283893c72292ede38d277e3cd2b9b64c3e4f1dda Added Reference https://github.com/vllm-project/vllm/commit/ba22152096b2484faa3579624a253d54804d876d Added Reference https://github.com/vllm-project/vllm/pull/47259 Added Reference https://github.com/vllm-project/vllm/security/advisories/GHSA-8pw2-6jv3-mj5j