CVE-2023-6568 in mlflow
Summary
by MITRE • 12/07/2023
Cross-site Scripting (XSS) - Reflected in GitHub repository mlflow/mlflow prior to 2.9.0.
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Analysis
by VulDB Data Team • 12/30/2023
Cross-site scripting vulnerabilities represent one of the most pervasive and dangerous web application security flaws, with reflected XSS specifically targeting user input that gets immediately reflected back to the browser without proper sanitization. The vulnerability discovered in the mlflow/mlflow repository prior to version 2.9.0 demonstrates a classic reflected XSS pattern where malicious scripts can be injected through user-controllable parameters and executed within the context of other users' browsers. This type of vulnerability falls under CWE-79 which specifically addresses improper neutralization of input during web page generation, making it a fundamental weakness in web application security architecture. The affected mlflow platform, designed for machine learning lifecycle management, was susceptible to attacks where an attacker could craft malicious URLs containing script payloads that would be executed when legitimate users accessed those links, potentially leading to session hijacking, data theft, or unauthorized actions within the mlflow environment.
The technical implementation of this reflected XSS vulnerability stems from insufficient input validation and output encoding mechanisms within the mlflow web application's parameter processing logic. When user-provided parameters are directly incorporated into HTML responses without proper sanitization or encoding, attackers can inject malicious JavaScript code that executes in the victim's browser context. The vulnerability likely manifests in endpoints that handle user input for filtering, sorting, or display purposes where query parameters are reflected back to the client-side without appropriate HTML escaping or content security policy enforcement. This flaw allows attackers to exploit the trust relationship between the web application and its users by executing arbitrary code within the victim's browser, potentially accessing sensitive data or performing unauthorized operations on behalf of the user. The ATT&CK framework categorizes this as a web application attack vector under T1059.007 for scripting languages, specifically targeting the execution of malicious payloads through vulnerable input handling mechanisms.
The operational impact of this reflected XSS vulnerability extends beyond simple code injection, creating significant risks for organizations relying on mlflow for their machine learning operations. Attackers could exploit this weakness to steal user sessions, access confidential model data, manipulate experiment results, or even compromise the underlying infrastructure if the mlflow platform has elevated privileges. The vulnerability affects not only individual users but also organizational security posture since compromised accounts could lead to unauthorized access to sensitive machine learning models and datasets. Organizations using unpatched versions of mlflow face potential regulatory compliance issues as reflected XSS vulnerabilities are commonly cited in security audits and penetration testing reports, particularly within industries handling sensitive data such as finance, healthcare, or government sectors. The attack surface is broad since any endpoint that processes user input for display purposes could be vulnerable, making comprehensive patching essential for maintaining secure operations.
Mitigation strategies for this reflected XSS vulnerability require immediate implementation of proper input validation and output encoding practices throughout the mlflow application. Organizations should implement Content Security Policy headers to restrict script execution and enforce proper HTML escaping on all user-controllable inputs before rendering them in web responses. The recommended approach involves upgrading to mlflow version 2.9.0 or later where the vulnerability has been addressed through improved input sanitization mechanisms, alongside implementing comprehensive security testing including automated scanning for XSS vulnerabilities during development cycles. Security teams should also establish regular vulnerability assessment procedures and ensure that all dependencies are kept current with security patches. Additional protective measures include implementing web application firewalls to detect and block suspicious input patterns, establishing secure coding guidelines that emphasize proper input validation, and conducting regular security training for developers to prevent similar issues in future releases. The remediation process should follow established security frameworks such as OWASP's secure coding practices and incorporate automated testing tools to identify potential XSS vulnerabilities before deployment to production environments, ensuring that the mlflow platform maintains its integrity and protects user data from malicious exploitation attempts.