CVE-2023-6909 in mlflow
Summary
by MITRE • 12/18/2023
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
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Analysis
by VulDB Data Team • 06/24/2026
This vulnerability represents a classic path traversal flaw that existed in the mlflow/mlflow repository prior to version 2.9.2, allowing attackers to access files outside of intended directories through carefully crafted file paths containing the sequence '\..\'. The issue stems from insufficient input validation and sanitization within the application's file handling mechanisms, where user-supplied paths are not properly normalized or restricted before being processed. This type of vulnerability falls under CWE-22 Path Traversal and aligns with ATT&CK technique T1059.007 Command and Scripting Interpreter: Python, as it enables arbitrary code execution through file access manipulation.
The technical implementation of this flaw allows an attacker to navigate directories beyond the intended scope by exploiting the directory traversal pattern that resolves parent directory references in file paths. When mlflow processes file requests containing sequences like '\..\filename', the application fails to properly sanitize these inputs, potentially allowing access to sensitive files such as configuration data, credentials, or system files that should remain protected. The vulnerability becomes particularly dangerous when combined with other attack vectors, as it can be used to escalate privileges and gain unauthorized access to critical system resources.
Operationally, this path traversal vulnerability presents significant risks to organizations using mlflow for machine learning model management and experiment tracking. Attackers could leverage this weakness to access stored model artifacts, training data, or configuration files that may contain sensitive information such as API keys, database credentials, or proprietary research data. The impact extends beyond simple data exposure, as successful exploitation can lead to full system compromise through the execution of malicious code loaded from accessible file paths. This vulnerability directly impacts the confidentiality and integrity of machine learning workflows and can potentially be used as a foothold for further attacks within the infrastructure.
Mitigation strategies should focus on implementing robust input validation and sanitization mechanisms that normalize all file paths before processing them within the application. Organizations should ensure that mlflow is updated to version 2.9.2 or later where this vulnerability has been addressed through proper path validation and restriction of parent directory references. Additional defensive measures include implementing strict file access controls, employing secure coding practices that prohibit the use of user-supplied paths without proper sanitization, and deploying application firewalls or web application security solutions that can detect and block suspicious path traversal attempts. The remediation should also include regular security testing and code reviews to identify similar vulnerabilities in other components of the machine learning platform ecosystem.