CVE-2025-58756
BaseFortify
Publication date: 2025-09-09
Last updated on: 2025-09-19
Assigner: GitHub, Inc.
Description
Description
CVSS Scores
EPSS Scores
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Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| monai | medical_open_network_for_ai | to 1.5.0 (inc) |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-502 | The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
This vulnerability in MONAI arises from insecure deserialization when loading model checkpoints using PyTorch's torch.load function without the secure parameter weights_only=True. Attackers can craft malicious checkpoint files that, when loaded, execute arbitrary code on the host system. Although some parts of MONAI use secure loading, other parts still load checkpoints insecurely, allowing remote attackers to run arbitrary commands via deserialization of untrusted data. [1]
How can this vulnerability impact me? :
The vulnerability allows an attacker to execute arbitrary commands on the host system remotely without user interaction, potentially compromising confidentiality, integrity, and availability of the system. This can lead to unauthorized access, data manipulation, or disruption of services, especially if untrusted checkpoints are loaded to reduce training time or costs. [1]
How can this vulnerability be detected on my network or system? Can you suggest some commands?
This vulnerability can be detected by checking for the presence of files created by the proof-of-concept exploit, such as the file '/tmp/hacker2.txt', which indicates arbitrary code execution via malicious checkpoint loading. Additionally, monitoring for unexpected file creations or suspicious activity during checkpoint loading in MONAI could help detect exploitation attempts. Specific commands to check for the proof-of-concept artifact include: `ls /tmp/hacker2.txt` to see if the file exists. Monitoring logs or using system auditing tools to detect unexpected executions during model checkpoint loading is also recommended. [1]
What immediate steps should I take to mitigate this vulnerability?
Immediate mitigation steps include using safe loading methods that enforce the parameter `weights_only=True` or equivalent secure parameters when loading models in MONAI. Avoid loading checkpoints from untrusted or external sources without proper validation. Since no fixed versions are available as of the publication date, these precautions are critical to prevent exploitation. [1]