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CVE-2026-34760

Estado: AnalizadaAlta (7.1)—

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0.

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.

AV:N/PR:L indica acceso remoto con privilegios (T1210). La manipulación de audio en modelos de IA afecta integridad de datos procesados (T1565.002), aunque el impacto exacto es indirecto.

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

Vulnerability ScanningThreat Intelligence ProgramPrivileged Account ManagementNetwork SegmentationEncrypt Sensitive InformationDisable or Remove Feature or Program

Tecnologías afectadas (1)

Vllm — Vllm

CWE

Referencias

JSON original (NVD)

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{
  "id": "CVE-2026-34760",
  "cveTags": [],
  "metrics": {
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-34760",
          "role": "CISA Coordinator",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "version": "2.0.3",
          "timestamp": "2026-04-03T14:42:25.211772Z"
        }
      }
    ],
    "cvssMetricV31": [
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        "type": "Secondary",
        "source": "security-advisories@github.com",
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          "scope": "UNCHANGED",
          "version": "3.1",
          "baseScore": 5.9,
          "attackVector": "NETWORK",
          "baseSeverity": "MEDIUM",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L",
          "integrityImpact": "HIGH",
          "userInteraction": "NONE",
          "attackComplexity": "HIGH",
          "availabilityImpact": "LOW",
          "privilegesRequired": "LOW",
          "confidentialityImpact": "NONE"
        },
        "impactScore": 4.2,
        "exploitabilityScore": 1.6
      },
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        "source": "nvd@nist.gov",
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          "baseSeverity": "HIGH",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L",
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  "published": "2026-04-02T20:16:25.437",
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      "tags": [
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      "tags": [
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  ],
  "vulnStatus": "Analyzed",
  "weaknesses": [
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      "type": "Secondary",
      "source": "security-advisories@github.com",
      "description": [
        {
          "lang": "en",
          "value": "CWE-20"
        }
      ]
    }
  ],
  "descriptions": [
    {
      "lang": "en",
      "value": "vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0."
    },
    {
      "lang": "es",
      "value": "vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLMs). Desde la versión 0.5.5 hasta antes de la versión 0.18.0, Librosa por defecto utiliza numpy.mean para el downmixing mono (to_mono), mientras que el estándar internacional ITU-R BS.775-4 especifica un algoritmo de downmixing ponderado. Esta discrepancia resulta en inconsistencia entre el audio escuchado por humanos (p. ej., a través de auriculares/altavoces normales) y el audio procesado por modelos de IA (que infra a través de Librosa, como vllm, transformer). Este problema ha sido parcheado en la versión 0.18.0."
    }
  ],
  "lastModified": "2026-07-24T21:10:00.143",
  "configurations": [
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          "cpeMatch": [
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              "versionEndExcluding": "0.18.0",
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          "operator": "OR"
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}