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CVE-2025-46560

Estado: AnalizadaAlta (7.5)—

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.

CVSS

Probabilidad de explotación (EPSS)

EPSS (Exploit Prediction Scoring System, de FIRST) estima la probabilidad de que una vulnerabilidad sea explotada en 30 días. Complementa a CVSS (impacto) y a CISA KEV (explotación confirmada).

🎯 Técnicas ATT&CK

Cómo se explota esta vulnerabilidad y qué consigue el atacante, en el lenguaje de MITRE ATT&CK.

Aplicación LLM expuesta en red (AV:N, PR:N, UI:N) con complejidad algoritmica O(n²) que permite agotamiento de recursos mediante entrada maliciosa especializada.

Inferido por nuestro agente de análisis a partir de la descripción oficial, el vector CVSS y la CWE, y comprobado por un supervisor. Puede contener errores.

🛡️ Mitigaciones ATT&CK que cubren estas técnicas

Filter Network TrafficVulnerability ScanningPrivileged Account ManagementNetwork SegmentationLimit Access to Resource Over NetworkApplication Isolation and Sandboxing

Tecnologías afectadas (1)

Vllm — Vllm

CWE

Referencias

JSON original (NVD)

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{
  "id": "CVE-2025-46560",
  "cveTags": [],
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          "role": "CISA Coordinator",
          "options": [
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              "automatable": "no"
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            {
              "technicalImpact": "partial"
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          "version": "2.0.3",
          "timestamp": "2025-04-30T13:09:10.349287Z"
        }
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          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
          "integrityImpact": "NONE",
          "userInteraction": "NONE",
          "attackComplexity": "LOW",
          "availabilityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "confidentialityImpact": "NONE"
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  "published": "2025-04-30T01:15:52.097",
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  "vulnStatus": "Analyzed",
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      "description": [
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          "lang": "en",
          "value": "CWE-1333"
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  "descriptions": [
    {
      "lang": "en",
      "value": "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."
    },
    {
      "lang": "es",
      "value": "vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en memoria para LLM. Las versiones a partir de la 0.8.0 y anteriores a la 0.8.5 se ven afectadas por una vulnerabilidad crítica de rendimiento en la lógica de preprocesamiento de entrada del tokenizador multimodal. El código reemplaza dinámicamente los tokens de marcador de posición (p. ej., &lt;|audio_|&gt;, &lt;|image_|&gt;) con tokens repetidos basados ??en longitudes precalculadas. Debido a las ineficientes operaciones de concatenación de listas, el algoritmo presenta una complejidad temporal cuadrática (O(n²)), lo que permite a los actores maliciosos activar el agotamiento de recursos mediante entradas especialmente manipuladas. Este problema se ha corregido en la versión 0.8.5."
    }
  ],
  "lastModified": "2026-06-17T09:26:37.837",
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