CVE-2023-2780 in mlflow
Summary
by MITRE • 05/18/2023
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 06/10/2023
The vulnerability identified as CVE-2023-2780 represents a path traversal flaw within the mlflow/mlflow repository that affects versions prior to 2.3.1. This issue manifests when the system processes file paths containing the sequence '\..filename' which can be exploited to access files outside the intended directory structure. The vulnerability stems from inadequate input validation and sanitization of file path parameters, allowing malicious actors to manipulate directory traversal sequences and gain unauthorized access to sensitive files or directories. Path traversal vulnerabilities of this nature are particularly dangerous in machine learning platforms where data security and access control are paramount for protecting intellectual property and sensitive training datasets.
The technical implementation of this vulnerability occurs within the file handling mechanisms of the mlflow platform where user-provided path strings are not properly normalized or validated before being processed. When the system encounters the '\..filename' pattern, it fails to properly resolve the path components, allowing an attacker to navigate to parent directories and potentially access system files or other users' data. This flaw operates at the operating system level where path resolution is handled, making it particularly challenging to mitigate without proper input sanitization. The vulnerability aligns with CWE-22 Path Traversal and can be categorized under ATT&CK technique T1059 Command and Scripting Interpreter where adversaries may leverage such flaws to execute unauthorized file access operations. The vulnerability is particularly concerning in cloud-based machine learning environments where multiple users share the same platform and data isolation is critical for maintaining security boundaries.
The operational impact of CVE-2023-2780 extends beyond simple unauthorized file access to potentially compromise entire machine learning workflows and data integrity. Attackers could exploit this vulnerability to access training datasets, model weights, configuration files, or even system credentials stored within the mlflow platform. In production environments, this could lead to intellectual property theft, model poisoning attacks, or complete system compromise if the platform has elevated privileges. The vulnerability is especially dangerous when mlflow is deployed in multi-tenant environments where isolation between different user groups is essential. Organizations using mlflow for sensitive applications such as financial modeling, healthcare analytics, or defense-related machine learning projects face significant risk exposure. The impact is amplified when considering that mlflow is widely adopted in enterprise settings where data governance and compliance requirements are stringent, making such vulnerabilities particularly costly from both security and regulatory perspectives.
Mitigation strategies for CVE-2023-2780 should prioritize immediate version updates to mlflow 2.3.1 or later where the path traversal vulnerability has been addressed through proper input validation and path normalization. Organizations should implement comprehensive input sanitization measures that validate and normalize all file path parameters before processing, ensuring that directory traversal sequences are properly detected and rejected. Network segmentation and access control measures should be strengthened to limit direct file system access to mlflow components, while implementing principle of least privilege for all platform users and services. Regular security audits should be conducted to identify and remediate similar vulnerabilities in other components of the machine learning stack, particularly in file handling and data ingestion modules. Additionally, organizations should consider implementing automated monitoring solutions that can detect anomalous file access patterns or attempts to exploit path traversal vulnerabilities in real-time, providing early warning capabilities for potential security incidents. The remediation process should include thorough testing of the updated platform to ensure that legitimate functionality remains intact while the vulnerability is effectively patched.