Description (CNA)
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.
Affected (CNA)
| Vendor / product | Versions |
|---|---|
| vllm-project / vllm | affected >= 0.8.0, < 0.8.5 |
CWE
CWE-1333
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg
- https://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197
ADP enrichment
No English description supplied.