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CVE-2025-58756

Estado: AnalizadaAlta (8.8)—

MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in `model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)` in monai/bundle/scripts.py , `weights_only=True` is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available.

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.

Vulnerabilidad de deserialización insegura (CWE-502) en carga de checkpoints con PR:L permite ejecución remota de código en servicios de red adyacente; PyTorch torch.load() sin restricciones ejecuta código arbitrario durante deserialización.

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

Threat Intelligence ProgramPrivileged Account ManagementExecution PreventionDisable or Remove Feature or ProgramApplication Isolation and SandboxingExploit Protection

Tecnologías afectadas (1)

Monai — Medical Open Network FOR AI

CWE

Referencias

JSON original (NVD)

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{
  "id": "CVE-2025-58756",
  "cveTags": [],
  "metrics": {
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          "role": "CISA Coordinator",
          "options": [
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          "version": "2.0.3",
          "timestamp": "2025-09-09T13:13:04.127686Z"
        }
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          "baseSeverity": "HIGH",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
          "integrityImpact": "HIGH",
          "userInteraction": "NONE",
          "attackComplexity": "LOW",
          "availabilityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "confidentialityImpact": "HIGH"
        },
        "impactScore": 5.9,
        "exploitabilityScore": 2.8
      }
    ]
  },
  "affected": [
    {
      "source": "security-advisories@github.com",
      "affectedData": [
        {
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          "product": "MONAI",
          "versions": [
            {
              "status": "affected",
              "version": "<= 1.5.0"
            }
          ]
        }
      ]
    }
  ],
  "published": "2025-09-09T00:15:32.457",
  "references": [
    {
      "url": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj",
      "tags": [
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        "Vendor Advisory"
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "vulnStatus": "Analyzed",
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      "description": [
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        }
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    }
  ],
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      "value": "MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in `model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)` in monai/bundle/scripts.py , `weights_only=True` is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available."
    }
  ],
  "lastModified": "2026-06-17T09:44:53.017",
  "configurations": [
    {
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