CVE-2025-14287 is a command injection vulnerability classified under CWE-94 (Improper Control of Generation of Code) found in the MLflow open-source platform, specifically in versions before 3.7.0. The flaw exists in the mlflow/sagemaker/__init__.py file between lines 161-167, where user input provided via the --container parameter is directly interpolated into shell commands executed using Python's os.system() function without proper sanitization or validation. This unsafe coding practice allows an attacker to craft malicious container image names containing shell metacharacters or commands, resulting in arbitrary command execution on the host system. The vulnerability affects various deployment environments where MLflow is used, including local development setups, continuous integration/continuous deployment (CI/CD) pipelines, and cloud-based machine learning workflows. The CVSS v3.0 base score is 7.5, reflecting a high severity due to network attack vector, high impact on confidentiality, integrity, and availability, and requiring user interaction but no privileges or authentication. Although no known exploits have been reported in the wild, the vulnerability poses a significant risk because MLflow is widely adopted for managing machine learning lifecycle processes, and compromised systems could lead to data breaches, system takeover, or disruption of ML workflows.

The impact of CVE-2025-14287 is substantial for organizations using MLflow in their machine learning operations. Successful exploitation can lead to remote code execution on systems running MLflow, potentially allowing attackers to gain unauthorized access, exfiltrate sensitive data, manipulate or corrupt machine learning models and data, disrupt ML workflows, or pivot to other parts of the network. This can result in loss of confidentiality, integrity, and availability of critical ML infrastructure. Organizations relying on MLflow for production ML pipelines, especially those integrated with cloud services or automated CI/CD environments, face increased risk of operational disruption and data compromise. The vulnerability also poses a risk to intellectual property embedded in ML models and datasets. Given MLflow’s growing adoption across industries, the threat surface is broad, affecting sectors such as technology, finance, healthcare, and any organization leveraging ML for business-critical applications.

To mitigate CVE-2025-14287, organizations should upgrade MLflow to version 3.7.0 or later where the vulnerability is patched. If immediate upgrading is not feasible, apply the following mitigations: (1) Avoid using the --container parameter with untrusted input; restrict usage to trusted users only. (2) Implement input validation and sanitization on container image names before they are passed to shell commands, ensuring no shell metacharacters or injection vectors are present. (3) Replace os.system() calls with safer alternatives such as subprocess.run() with argument lists to avoid shell interpretation. (4) Employ runtime security controls such as container isolation, least privilege execution, and monitoring for anomalous command executions. (5) Restrict network access to MLflow services to trusted networks and users to reduce exposure. (6) Conduct code audits and penetration testing focusing on command injection vectors in MLflow deployments. (7) Monitor logs for suspicious CLI usage patterns involving the --container parameter. These targeted steps go beyond generic advice by focusing on the specific vulnerable code path and operational context.