vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
Metrics
Affected Vendors & Products
Advisories
| Source | ID | Title |
|---|---|---|
Github GHSA |
GHSA-58v5-2m8f-94pr | vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion |
Fixes
Solution
No solution given by the vendor.
Workaround
No workaround given by the vendor.
References
History
Tue, 06 Oct 2026 01:15:00 +0000
| Type | Values Removed | Values Added |
|---|---|---|
| First Time appeared |
Vllm-project
Vllm-project vllm |
|
| Vendors & Products |
Vllm-project
Vllm-project vllm |
Mon, 05 Oct 2026 23:15:00 +0000
| Type | Values Removed | Values Added |
|---|---|---|
| Description | vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0. | |
| Title | vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion | |
| Weaknesses | CWE-400 | |
| References |
| |
| Metrics |
cvssV3_1
|
Projects
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Status: PUBLISHED
Assigner: GitHub_M
Published:
Updated: 2026-10-05T23:01:54.972Z
Reserved: 2026-10-05T19:11:07.948Z
Link: CVE-2026-105760
No data.
Status : Received
Published: 2026-10-05T23:17:02.903
Modified: 2026-10-05T23:17:02.903
Link: CVE-2026-105760
No data.
OpenCVE Enrichment
Updated: 2026-10-06T01:00:09Z
Weaknesses
Github GHSA