8.8
CVE-2025-58756
- EPSS 0.68%
- Veröffentlicht 08.09.2025 23:39:55
- Zuletzt bearbeitet 19.09.2025 15:26:29
- Quelle security-advisories@github.com
- CVE-Watchlists
- Unerledigt
MONAI's unsafe torch usage may lead to arbitrary code execution
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.
Daten sind bereitgestellt durch National Vulnerability Database (NVD)
Monai ≫ Medical Open Network For Ai Version <= 1.5.0
| Typ | Quelle | Score | Percentile |
|---|---|---|---|
| EPSS | FIRST.org | 0.68% | 0.477 |
| Quelle | Base Score | Exploit Score | Impact Score | Vector String |
|---|---|---|---|---|
| security-advisories@github.com | 8.8 | 2.8 | 5.9 |
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
|
CWE-502 Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj