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Medical Open Network For Ai CVE-2025-58756

HIGH
Deserialization of Untrusted Data (CWE-502)
2025-09-09 security-advisories@github.com GHSA-6vm5-6jv9-rjpj
8.8
CVSS 3.1 · GitHub Advisory
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Severity by source

GitHub Advisory PRIMARY
8.8 HIGH
AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Primary rating from GitHub Advisory · only source for this CVE.

CVSS VectorGitHub Advisory

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Lifecycle Timeline

4
Patch released
Mar 31, 2026 - 21:13 nvd
Patch available
Analysis Generated
Mar 28, 2026 - 19:11 vuln.today
PoC Detected
Sep 19, 2025 - 15:26 vuln.today
Public exploit code
CVE Published
Sep 09, 2025 - 00:15 nvd
HIGH 8.8

Blast Radius

ecosystem impact
† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth
  • 1 pypi packages depend on monai (1 direct, 0 indirect)

Ecosystem-wide dependent count for version 1.5.1.

DescriptionGitHub Advisory

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.

AnalysisAI

MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.

Technical ContextAI

This vulnerability is classified as Deserialization of Untrusted Data (CWE-502), which allows attackers to execute arbitrary code through malicious serialized objects. 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. Affected products include: Monai Medical Open Network For Ai.

RemediationAI

No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Avoid deserializing untrusted data. Use safe serialization formats (JSON). Implement integrity checks and type allowlists.

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CVE-2025-58756 vulnerability details – vuln.today

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