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.
| Métrique | Valeur | Interprétation |
|---|---|---|
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AV (Vecteur d'Attaque)
Attack Vector
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Network (N)
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Exploit possible à distance via réseau (HTTP, API, etc.) |
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AC (Complexité d'Attaque)
Attack Complexity
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Low (L)
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Peu de conditions requises pour exploiter |
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PR (Privilèges Requis)
Privileges Required
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Low (L)
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Authentifié avec compte utilisateur basique |
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UI (Interaction Utilisateur)
User Interaction
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None (N)
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Aucune interaction utilisateur nécessaire |
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S (Périmètre)
Scope
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Unchanged (U)
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Impact reste dans le périmètre du composant compromis |
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C (Impact Confidentialité)
Confidentiality Impact
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None (N)
|
Aucune divulgation d'informations |
|
I (Impact Intégrité)
Integrity Impact
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None (N)
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Aucune modification de données possible |
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A (Impact Disponibilité)
Availability Impact
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High (H)
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Interruption totale du service possible |
vLLM: Quadratic Time Complexity in Input Token Processing​ leads to denial of service
vLLM phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service