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CVE-2025-58756: A Critical Deserialization Vulnerability in MONAI AI Toolkit

Overview

CVE-2025-58756 is a critical vulnerability discovered in the MONAI (Medical Open Network for AI) toolkit, a popular AI solution for healthcare imaging. This vulnerability, stemming from an insecure loading method, can potentially lead to an attacker executing malicious code, compromising the system and potentially leading to data leaks. This vulnerability is particularly worrisome due to MONAI’s widespread usage in the healthcare sector, making it a prime target for cybercriminals seeking sensitive medical data.

Vulnerability Summary

CVE ID: CVE-2025-58756
Severity: Critical – CVSS 8.8
Attack Vector: Remote
Privileges Required: None
User Interaction: None
Impact: Potential system compromise and data leakage

Affected Products

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Product | Affected Versions

MONAI | Up to and including 1.5.0

How the Exploit Works

The vulnerability lies in the way MONAI loads checkpoints. While the `model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)` in monai/bundle/scripts.py is loaded securely, there are other instances in the project where checkpoints are loaded insecurely. This insecure method could be exploited when users attempt to reduce training time and costs by loading pre-trained models downloaded from other platforms. If a malicious actor can manipulate these pre-trained models or checkpoints, they can introduce malicious content that, when loaded, triggers a deserialization vulnerability, leading to arbitrary code execution.

Conceptual Example Code

A conceptual example of how this vulnerability might be exploited is an attacker crafting a malicious pre-trained model or checkpoint. When this model is loaded by the victim, the malicious code gets executed. Below is a simplified example:

# Attacker crafts a model with malicious code
class MaliciousModel:
def __reduce__(self):
return (os.system, ('cat /etc/passwd > /tmp/passwd_copy',))
checkpoint = {
'model': MaliciousModel(),
# other legit data
}
# Victim loads the model
torch.load('malicious_checkpoint.pth')

In this example, the malicious model, when deserialized, executes the `os.system` function with the argument `’cat /etc/passwd > /tmp/passwd_copy’`, copying the content of `/etc/passwd` to a temporary file.
Please note that this is a simplified example and the actual exploitation may involve more complex steps and obfuscation techniques.

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Disclaimer:

The information and code presented in this article are provided for educational and defensive cybersecurity purposes only. Any conceptual or pseudocode examples are simplified representations intended to raise awareness and promote secure development and system configuration practices.

Do not use this information to attempt unauthorized access or exploit vulnerabilities on systems that you do not own or have explicit permission to test.

Ameeba and its authors do not endorse or condone malicious behavior and are not responsible for misuse of the content. Always follow ethical hacking guidelines, responsible disclosure practices, and local laws.
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