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bdu:2026-03424

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fkie_cve-2025-46560

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.

GHSA-vc6m-hm49-g9qg — vLLM: Quadratic Time Complexity in Input Token Processing leads to denial of service

Affected: PyPI: vllm

### Summary A critical performance vulnerability has been identified 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.

### Details Affected Component: input_processor_for_phi4mm function. https://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197

The code modifies the input_ids list in-place using input_ids = input_ids[:i] + tokens + input_ids[i+1:]. Each concatenation operation copies the entire list, leading to O(n) operations per replacement. For k placeholders expanding to m tokens, total time becomes O(kmn), approximating O(n²) in worst-case scenarios.

### PoC Test data demonstrates exponential time growth: ```python test_cases = [100, 200, 400, 800, 1600, 3200, 6400] run_times = [0.002, 0.007, 0.028, 0.136, 0.616, 2.707, 11.854] # seconds ``` Doubling input size increases runtime by ~4x (consistent with O(n²)).

### Impact Denial-of-Service (DoS): An attacker could submit inputs with many placeholders (e.g., 10,000 <|audio_1|> tokens), causing CPU/memory exhaustion. Example: 10,000 placeholders → ~100 million operations.

### Remediation Recom…

PYSEC-2026-2022 — phi4mm: Quadratic Time Complexity in Input Token Processing leads to denial of service