CVE-2024-27132
Lfprojects
CVE-2024-27132: MLflow Cross-Site Scripting (XSS) Vulnerability. Discover a critical security flaw in MLflow where insufficient sanitization allows for XSS attacks when executing untrusted recipes, potentially leading to client-side Remote Code Execution (RCE) in Jupyter Notebook environments. This vulnerability, CVE-2024-27132, arises from the inadequate sanitization of template variables within MLflow, enabling attackers to inject malicious scripts. The SVRS score is 30 indicating a moderate risk, but given the potential for RCE, a thorough assessment is recommended. Successful exploitation could compromise user sessions and allow unauthorized actions within the MLflow and Jupyter Notebook environments. Immediate patching is crucial to mitigate potential risks and safeguard against malicious attacks leveraging this vulnerability. Prioritize updates to ensure the security of your MLflow deployment.
Description
CVE-2024-27132 is a cross-site scripting (XSS) vulnerability affecting MLflow, an open-source platform for managing machine learning projects. This vulnerability arises from insufficient sanitization of user-supplied input when running untrusted recipes within a Jupyter Notebook environment. The lack of proper sanitization allows malicious scripts to be injected, potentially leading to client-side remote code execution (RCE). Despite a CVSS score of 7.5, SOCRadar's SVRS assigns a score of 30, indicating that while the vulnerability is exploitable, its immediate impact is considered less critical.
Key Insights
- Client-Side RCE Potential: The vulnerability allows malicious actors to execute arbitrary code on the client-side through XSS, potentially gaining control of the user's machine.
- Untrusted Recipes: The vulnerability affects scenarios where users execute recipes created by untrusted sources. This emphasizes the importance of validating the origin and content of recipes before execution.
- Jupyter Notebook Environment: The vulnerability specifically targets Jupyter Notebook environments, making it relevant for organizations utilizing this popular data science tool.
- Active Exploitation: This vulnerability is currently "In The Wild," meaning malicious actors are actively exploiting it in real-world attacks.
Mitigation Strategies
- Input Sanitization: Implement strict input validation and sanitization measures to prevent the injection of malicious scripts. Regularly review and update these mechanisms to ensure they remain effective.
- Recipe Validation: Implement a process for validating the origin and content of recipes before executing them. This can include source code review, reputation checks, and sandboxing execution environments.
- Regular Updates: Ensure all MLflow components, including Jupyter Notebook extensions, are updated to the latest versions. Patching vulnerabilities is a critical aspect of mitigating security risks.
- Security Awareness Training: Educate users on the dangers of XSS vulnerabilities and the importance of validating the origin of content before execution.
Additional Information
If users have additional queries regarding this incident, they can use the 'Ask to Analyst' feature, contact SOCRadar directly, or open a support ticket for more information if necessary.
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CWE Details
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