CVE-2025-46560 Information

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.

Reference

https://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197 https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg

Related CNNVD

CNNVD-202506-3133 (Published: 2025-06-25)