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.
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History
Wed, 30 Apr 2025 00:45:00 +0000
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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. | |
Title | vLLM phi4mm: Quadratic Time Complexity in Input Token Processing leads to denial of service | |
Weaknesses | CWE-1333 | |
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Metrics |
cvssV3_1
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Status: PUBLISHED
Assigner: GitHub_M
Published: 2025-04-30T00:24:53.750Z
Updated: 2025-04-30T00:24:53.750Z
Reserved: 2025-04-24T21:10:48.174Z
Link: CVE-2025-46560

No data.

Status : Received
Published: 2025-04-30T01:15:52.097
Modified: 2025-04-30T01:15:52.097
Link: CVE-2025-46560

No data.