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      <title>CVE-2025-58756 — MONAI's unsafe torch usage may lead to arbitrary code execution</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2025-58756</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Project-MONAI MONAI&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Project-MONAI MONAI&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;</content:encoded>
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