Deserialization in Monai Medical Open Network For Ai

CVE-2025-58756

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.

Vulnerability class: Insecure Deserialization

EPSS: 0.007 (49.1th percentile) — read the EPSS interpretation.

CVSS v3 metric

CVSS v3 base score 8.8 (High). Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H.

Affected products

Weakness classification (CWE)

Public proof-of-concept exploits

References

Frequently asked questions

What is CVE-2025-58756?
CVE-2025-58756 is a high-severity vulnerability in Monai Medical Open Network For Ai, classified under Deserialization of Untrusted Data. CVSS score: 8.8/10. Published 2025-09-09.
How severe is CVE-2025-58756?
High severity. CVSS v3 base score is 8.8 out of 10.
Is CVE-2025-58756 known to be exploited?
1 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.