CVE-2025-15031: RCE — Patch Guide

Overview

A significant security vulnerability, tracked as CVE-2025-15031, has been identified in MLflow, an open-source platform for managing the machine learning lifecycle. This flaw resides in the component that handles the extraction of machine learning models packaged as tar.gz archives. Due to insufficient validation, a malicious archive can be crafted to write files outside the intended directory, a classic path traversal attack.

Vulnerability Details

The vulnerability is in MLflow’s pyfunc model loading process. When MLflow extracts a model from a tar.gz file, it uses the Python tarfile.extractall() function without properly checking the paths of files inside the archive. An attacker can create a tar file containing entries with absolute paths (like /etc/passwd) or relative paths using .. sequences (like ../../malicious.py). When extracted, these files are written to the corresponding location on the server’s filesystem, escaping the designated temporary or model directory.

Impact and Risk

This vulnerability is rated HIGH with a CVSS score of 8.1. The primary risk is arbitrary file overwrite, which can lead to severe consequences:

• Remote Code Execution (RCE): By overwriting critical system files or Python modules loaded by the MLflow process, an attacker can achieve full command execution on the host server.
• Data Corruption or Theft: Sensitive configuration files, other models, or system files can be altered or deleted.
• System Compromise: In multi-tenant MLflow deployments (like shared tracking servers), this flaw could allow one user to compromise the environment of another user or the entire platform.

The risk is particularly critical in any scenario where MLflow ingests models or artifacts from untrusted sources.

Remediation and Mitigation

The MLflow maintainers have released patches addressing this vulnerability. Immediate action is required.

1. Primary Action: Update MLflow. Upgrade to the latest patched version of MLflow. Consult the official MLflow GitHub repository or release notes for the specific version that includes the fix for CVE-2025-15031.
2. Temporary Mitigation: If an immediate update is not possible, restrict the use of the pyfunc model flavor with untrusted tar.gz artifacts. Implement strict source control for all models loaded into MLflow and ensure they originate from trusted, internal sources only.
3. General Security Practice: Always run MLflow services with the minimum necessary operating system permissions. This practice can limit the damage scope of a successful exploitation by restricting which files the process can overwrite.

Staying current with security updates is crucial for all software in your stack. For examples of the importance of timely patching, review recent advisories for other platforms, such as the Apple Patches WebKit Same-Origin Policy Bypass, the Apple backports for older iOS devices, and the detailed analysis of the Coruna iOS Exploit Kit.