A flaw was found in vLLM, an inference and serving engine for large language models. An unauthenticated client can exploit this by submitting a small, compressed audio input that expands into a very large memory allocation. This bypasses existing duration guards, leading to an out-of-memory condition and causing a worker crash. This vulnerability results in a Denial of Service (DoS) for deployments serving audio-capable models.

vLLM is an inference and serving engine for large language models. Prior to 0.24.0, the input_audio handling path for /v1/chat/completions calls AudioMediaIO.load_bytes or AudioMediaIO.load_file without passing VLLM_MAX_AUDIO_DECODE_DURATION_S to the shared audio decoder. An unauthenticated client can therefore submit a small compressed audio input that expands into a very large float32 PCM allocation, bypassing the duration guard already used by /v1/audio/transcriptions and causing an out-of-memory worker crash. Inline data URLs reach this path without being bounded by VLLM_AUDIO_FETCH_TIMEOUT. The issue affects deployments serving an audio-capable model, and authentication changes only the deployment-specific reachability. This issue is fixed in version 0.24.0.

vLLM is an inference and serving engine for large language models. Pri ...

vLLM: Unauthenticated audio decompression-bomb DoS in /v1/chat/completions