CVE-2024-3848 Scanner

Product Overview:

MLflow is an open-source platform designed for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. It is commonly used by data scientists and machine learning engineers to track, share, and manage their machine learning models. It provides tools to record and compare results, package code into reproducible runs, and manage deployments. MLflow supports multiple machine learning frameworks and integrates with various cloud platforms. The platform is widely adopted for end-to-end model management. However, it is important to note that vulnerabilities in MLflow could lead to security risks for users who rely on it for critical machine learning tasks.

Vulnerability Overview:

This vulnerability is a path traversal issue in MLflow versions prior to 2.11.0. The vulnerability arises from improper validation of URL fragments used in artifact URLs. An attacker can exploit this by inserting a '#' character into the URL, allowing the attacker's path to bypass the validation process. This could lead to the ability to access files outside the intended directory, including sensitive configuration files and SSH keys, posing a significant risk to system integrity and confidentiality. It was a bypass of the previously addressed CVE-2023-6909, which makes this vulnerability particularly impactful for older versions of MLflow.

Vulnerability Details:

The path traversal vulnerability exists in the way MLflow handles artifact URLs. When the application processes the URL, it fails to properly validate the fragment part of the URL after the '#' character. This allows an attacker to inject a path that is processed as a filesystem path, bypassing any restrictions on file access. The malicious URL could reference critical files, such as configuration files or private SSH keys, allowing attackers to disclose sensitive information. The vulnerability stems from insufficient validation of the fragment portion, and attackers can exploit this to access arbitrary files stored on the server. The attack is possible even if the user does not have authorized access to the system, making it a significant threat for unpatched systems.

Possible Effects:

If exploited, this vulnerability could lead to the unauthorized disclosure of sensitive data. Attackers may gain access to internal configuration files, SSH keys, or other critical information that could be used for further attacks or system compromise. The risk of information leakage could affect the security of entire systems relying on MLflow. This could result in data breaches, privilege escalation, or unauthorized access to cloud environments. Additionally, such vulnerabilities could undermine trust in the security of MLflow, potentially causing reputation damage for organizations using it.

REFERENCES

• Huntr Report
• CVE-2024-3848