Description
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to inefficient list concatenation operations, the algorithm exhibits quadratic time complexity (O(n²)), allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.
Problem types
CWE-1333: Inefficient Regular Expression Complexity
Product status
References
github.com/...t/vllm/security/advisories/GHSA-vc6m-hm49-g9qg
github.com/...t/vllm/security/advisories/GHSA-vc6m-hm49-g9qg
github.com/...6e8744f4e/vllm/model_executor/models/phi4mm.py