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.
Attack Parameters
Attack Vector
Network
Can be exploited remotely
Attack Complexity
Low
Easy to exploit
Privileges Required
None
No privileges needed
User Interaction
None
No user interaction needed
Impact Assessment
Confidentiality
None
No data leak
Integrity
None
No data modification
Availability
Low
Partial disruption
CVSS Vector v3.1
Weakness Type (CWE)
Vulnerable Products
vllm-project:vllm
References 4
https://github.com/vllm-project/vllm/security/advisories/GHSA-58v5-2m8f-94pr
https://github.com/vllm-project/vllm/security/advisories/GHSA-58v5-2m8f-94pr
https://github.com/vllm-project/vllm/pull/54935
https://github.com/vllm-project/vllm/pull/54935
https://github.com/vllm-project/vllm/commit/8b6de0eb9a09ef53f20cf06bd4d17ee264b…
https://github.com/vllm-project/vllm/commit/8b6de0eb9a09ef53f20cf06bd4d17ee264b9c2a7
https://github.com/vllm-project/vllm/releases/tag/v0.30.0
https://github.com/vllm-project/vllm/releases/tag/v0.30.0