CVE-2025-14287: MLflow Command Injection RCE Vulnerability

CVE-2025-14287 Overview

A command injection vulnerability exists in mlflow/mlflow versions before v3.7.0, specifically in the mlflow/sagemaker/__init__.py file at lines 161-167. The vulnerability arises from the direct interpolation of user-supplied container image names into shell commands without proper sanitization, which are then executed using os.system(). This allows attackers to execute arbitrary commands by supplying malicious input through the --container parameter of the CLI. The issue affects environments where MLflow is used, including development setups, CI/CD pipelines, and cloud deployments.

Critical Impact

Attackers can achieve arbitrary command execution on systems running vulnerable MLflow versions by injecting malicious payloads through the container image name parameter, potentially compromising ML infrastructure, CI/CD pipelines, and cloud deployments.

Affected Products

• MLflow versions prior to v3.7.0
• MLflow SageMaker integration module (mlflow/sagemaker/__init__.py)
• Environments utilizing MLflow CLI with the --container parameter

Discovery Timeline

• 2026-03-16 - CVE CVE-2025-14287 published to NVD
• 2026-03-16 - Last updated in NVD database

Technical Details for CVE-2025-14287

Vulnerability Analysis

This command injection vulnerability (CWE-94: Improper Control of Generation of Code) occurs due to unsafe handling of user-supplied input in the MLflow SageMaker integration module. The vulnerable code path exists within mlflow/sagemaker/__init__.py at lines 161-167, where container image names provided by users are directly interpolated into shell command strings without any input validation or sanitization.

The use of os.system() to execute these constructed command strings creates a direct pathway for command injection attacks. When an attacker supplies a maliciously crafted container image name containing shell metacharacters or command separators, the injected commands are executed with the same privileges as the MLflow process.

This vulnerability is particularly concerning in machine learning environments where MLflow is commonly deployed in development workstations, automated CI/CD pipelines, and cloud infrastructure—all of which may have elevated privileges or access to sensitive resources.

Root Cause

The root cause of this vulnerability is the unsafe use of string interpolation combined with os.system() for shell command execution. The vulnerable code directly embeds user-controlled input (the container image name from the --container CLI parameter) into a shell command string without implementing proper input validation, sanitization, or parameterization.

This violates secure coding practices that mandate treating all user input as untrusted and using safe alternatives to shell command execution, such as subprocess calls with argument arrays that prevent shell interpretation.

Attack Vector

The attack vector is network-based, requiring user interaction. An attacker can exploit this vulnerability by convincing a user or automated system to execute an MLflow CLI command with a malicious --container parameter value. The malicious input can include shell metacharacters such as semicolons, backticks, or $() constructs to break out of the intended command context and inject arbitrary commands.

In CI/CD environments, exploitation could occur if the container name is sourced from an untrusted location such as a configuration file, environment variable, or external input that an attacker can influence. This makes the vulnerability particularly dangerous in automated deployment pipelines where user interaction may not be required if the attacker can control upstream configuration sources.

The vulnerability mechanism involves unsanitized user input being passed to os.system() for shell command execution. For detailed technical analysis of the exploitation vector, refer to the Huntr Bug Bounty Report.

Detection Methods for CVE-2025-14287

Indicators of Compromise

• Unusual process spawning from MLflow-related processes, particularly child processes with suspicious command patterns
• Unexpected network connections originating from MLflow worker processes
• Log entries showing container names with shell metacharacters (;, |, $(), backticks)
• File system modifications or new files created in unexpected locations by MLflow processes

Detection Strategies

• Monitor MLflow CLI invocations for --container parameters containing shell metacharacters or command injection patterns
• Implement application-level logging to capture all container name values passed to the SageMaker integration
• Deploy endpoint detection solutions to identify anomalous process trees spawned from Python/MLflow processes
• Use static analysis tools to scan MLflow configurations and CI/CD pipeline definitions for potentially malicious container names

Monitoring Recommendations

• Enable verbose logging for MLflow SageMaker integration operations
• Configure SIEM rules to alert on command injection patterns in MLflow-related logs
• Monitor system call activity on hosts running MLflow for suspicious os.system() invocations
• Implement file integrity monitoring on MLflow deployment hosts to detect unauthorized modifications

How to Mitigate CVE-2025-14287

Immediate Actions Required

• Upgrade MLflow to version v3.7.0 or later immediately
• Audit all MLflow deployments to inventory instances running vulnerable versions
• Review CI/CD pipelines and automation scripts for MLflow --container parameter usage
• Implement input validation at the application layer to restrict container name formats to expected patterns

Patch Information

The vulnerability is addressed in MLflow version v3.7.0 and later. Organizations should upgrade to the latest stable release to receive this security fix along with any additional security improvements. The patch likely replaces the unsafe os.system() call with a safer alternative such as subprocess.run() with proper argument handling that prevents shell interpretation of user input.

For additional details about this vulnerability and the fix, refer to the Huntr Bug Bounty Report.

Workarounds

• Implement strict input validation on all container names before passing them to MLflow commands
• Use wrapper scripts that sanitize the --container parameter by removing or escaping shell metacharacters
• Deploy MLflow in isolated environments with minimal privileges to limit the impact of potential command execution
• Restrict network access for MLflow deployment hosts to reduce the risk of data exfiltration following exploitation

# Example input validation wrapper for MLflow CLI
# Validate container name contains only allowed characters before execution
CONTAINER_NAME="$1"
if [[ ! "$CONTAINER_NAME" =~ ^[a-zA-Z0-9._/-]+$ ]]; then
    echo "Error: Invalid container name format detected"
    exit 1
fi
mlflow sagemaker --container "$CONTAINER_NAME"