vLLM version prior to 0.30.0 allows callers to set large fps and max_frames values in media_io_kwargs for the GLMGA video backend, creating a huge frame-index list that consumes disproportionate CPU time and memory, reflecting vulnerabilities identified as CWE-400 and CWE-770. The resulting resource exhaustion can cause the inference service to become unresponsive, effectively denying service to legitimate users.

vLLM is an inference engine for large language models. The vulnerability applies to all releases of the vllm-project's vllm package before the 0.30.0 release. Any deployment using earlier versions and referencing the GLMGA video backend is at risk.

The CVSS score of 5.3 indicates moderate severity. The EPSS score of <1% indicates a very low likelihood of exploitation, and the vulnerability is not listed in CISA KEV. The likely attack vector is remote, via an API call that supplies a crafted media_io_kwargs payload. An attacker can trigger the vulnerability by submitting a request with oversized fps or max_frames values to the GLMGA backend, causing resource exhaustion in the shared media-loading executor.