MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.1, a Path Traversal (Zip Slip) vulnerability exists in MONAI's `_download_from_ngc_private()` function. The function uses `zipfile.ZipFile.extractall()` without path validation, while other similar download functions in the same codebase properly use the existing `safe_extract_member()` function. This vulnerability allows attackers to exploit the system, posing significant risks to organizations that rely on this toolkit.
The CVSS score for this vulnerability is 5.3, categorizing it as medium severity. This score reflects its potential impact, particularly due to its high attack complexity, which requires user interaction to exploit. As such, organizations should be aware of the risks involved and take necessary precautions to safeguard their systems.
Although there is currently no public exploit confirmed for this vulnerability, the nature of path traversal vulnerabilities means they can often be leveraged by attackers to gain unauthorized access to sensitive data. The urgency for defenders is clear: organizations should prioritize patching immediately.
The issue has been addressed in commit 4014c8475626f20f158921ae0cf98ed259ae4d59, which provides a fix for the vulnerability. Organizations using affected versions are advised to update to the latest version to mitigate this risk.
Risk to organizations includes unauthorized access to sensitive information, as successful exploitation of this vulnerability could lead to significant data breaches. Given the importance of healthcare data, the impact could be severe, necessitating immediate attention.
Vulnerability Details
The vulnerability in question is classified as a Path Traversal (Zip Slip) vulnerability, specifically affecting MONAI's `_download_from_ngc_private()` function. It has been assigned a CVSS score of 5.3, indicating a medium severity. The affected product is MONAI, with the vendor being Project MONAI. The vulnerability was disclosed on January 7, 2026, and the relevant CWE classification is CWE-22.
Technical Analysis
The root cause of this vulnerability is the improper use of `zipfile.ZipFile.extractall()`, which lacks adequate path validation. This oversight allows path traversal, enabling attackers to potentially extract files to arbitrary locations on the filesystem. The attack vector is network-based, with high complexity required for exploitation, as user interaction is necessary. No privileges are needed to exploit this vulnerability, making it accessible to a wide range of attackers. The integrity impact is classified as high, while confidentiality and availability impacts are noted as none.
Risk & Impact Analysis
The deployment of MONAI in healthcare imaging applications presents a considerable risk due to the potential for unauthorized access to sensitive patient data. The blast radius of a successful attack could encompass multiple systems, leading to widespread data exposure. Given the medium severity and the potential impact on confidentiality and integrity, organizations must assess their use of this toolkit and implement necessary remediation measures.
Organizations should schedule remediation and prioritize this vulnerability in their patch management cycle. The urgency is underscored by the lack of known exploits but the high potential for exploitation in real-world scenarios.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions prior to vendor patch are affected, specifically versions up to and including 1.5.1 of the MONAI toolkit.
Mitigation & Remediation
Organizations should ensure they apply the patch provided in commit 4014c8475626f20f158921ae0cf98ed259ae4d59 to mitigate this vulnerability. In cases where immediate patching is not feasible, implementing workarounds such as input validation on file paths and restricting the types of files that can be processed may help reduce exposure. Regular configuration hardening and network controls should also be practiced.
Detection Guidance
Monitoring logs for unusual file activities and behavioral anomalies during file extraction processes can help identify potential exploitation attempts. Implementing network signatures to detect suspicious download requests may also be beneficial.
AppSecure Threat Intelligence Insight
The significance of this vulnerability lies in its potential to expose sensitive healthcare data, a critical concern for organizations operating in this domain. The lack of current exploitation does not diminish the importance of timely remediation. Security teams should remain vigilant, as this vulnerability reflects broader trends in software development practices, particularly around input validation and secure coding techniques.
For more insights on vulnerability management, organizations may consider reviewing our vulnerability management program and exploring our various penetration testing services to enhance their security posture.
Additionally, understanding the implications of vulnerabilities like this can assist organizations in adapting their security strategies accordingly.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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