### 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 Recommendations Precompute all placeholder positions and expansion lengths upfront. Replace dynamic list concatenation with a single preallocated array. ```python # Pseudocode for O(n) solution new_input_ids = [] for token in input_ids: if token is placeholder: new_input_ids.extend([token] * precomputed_length) else: new_input_ids.append(token) ```
### 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 Recommendations Precompute all placeholder positions and expansion lengths upfront. Replace dynamic list concatenation with a single preallocated array. ```python # Pseudocode for O(n) solution new_input_ids = [] for token in input_ids: if token is placeholder: new_input_ids.extend([token] * precomputed_length) else: new_input_ids.append(token) ```
Update vllm to 0.8.5 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Quadratic Time Complexity in Input Token Processing leads to denial of service affects vllm (pip). Severity is medium. ### 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 Recommendations Precompute all placeholder positions and expansion lengths upfront. Replace dynamic list concatenation with a single preallocated array. ```python # Pseudocode for O(n) solution new_input_ids = [] for token in input_ids: if token is placeholder: new_input_ids.extend([token] * precomputed_length) else: new_input_ids.append(token) ```
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.8.0,<0.8.5 | 0.8.5 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate vllm to 0.8.5 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Quadratic Time Complexity in Input Token Processing leads to denial of service affects vllm (pip). Severity is medium. ### 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 Recommendations Precompute all placeholder positions and expansion lengths upfront. Replace dynamic list concatenation with a single preallocated array. ```python # Pseudocode for O(n) solution new_input_ids = [] for token in input_ids: if token is placeholder: new_input_ids.extend([token] * precomputed_length) else: new_input_ids.append(token) ```
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.8.0,<0.8.5 | 0.8.5 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard