CVE-2025-58756: high-severity vulnerability in Project-MONAI MONAI
MONAI's unsafe torch usage may lead to arbitrary code execution
Published · Updated
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21Vexday Risk Score
No sign of exploitation. No public exploitation artifact known so far.
ssvc Trackcvss 8.8epss 0.8%
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:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Affected products
Project-MONAI · MONAIRelated CVEs — Project-MONAI MONAI
In the same product, most dangerous first.
CVE-2026-100844HIGHMONAI before 1.6.0 OS Command Injection via dataset_name_or_idEPSS 0.9%CVE-2025-58757HIGHMONAI's unsafe use of Pickle deserialization may lead to RCEEPSS 0.6%CVE-2025-58755HIGHMONAI has path traversal issue that may lead to arbitrary file writesEPSS 0.6%CVE-2026-109360HIGHMONAI 0.4.0 before 1.6.1 RCE via SENet Pretrained Weights Over Plaintext HTTPEPSS 0.6%CVE-2026-119578HIGHMONAI 1.6.0 before 1.6.1 Code Execution via Auto3DSeg algo_from_json()EPSS 0.6%CVE-2026-48705CRITICALMONAI: MONAILabel Server Crash and Silent Inference Corruption via Crafted DICOM (pydicom DS/IS Validation Bypass)EPSS 0.5%