CVE-2025-14287 Lfprojects CVE debrief

Who should care

Organizations using MLflow in their development environments, CI/CD pipelines, or cloud deployments should be aware of this vulnerability. Specifically, those who use the `--container` parameter of the CLI or rely on user-supplied container image names are at risk. Security teams and developers responsible for maintaining and securing MLflow installations should prioritize patching to version v3.7.0 or later.

Technical summary

The vulnerability exists due to improper sanitization of user-supplied input in the `mlflow/sagemaker/__init__.py` file. An attacker can exploit this by providing a malicious container image name through the `--container` CLI parameter, leading to arbitrary command execution. The issue is addressed in MLflow version v3.7.0. Affected versions are before v3.7.0, and the vulnerability is tracked under CVE-2025-14287.

Defensive priority

High priority should be given to patching MLflow installations to version v3.7.0 or later. In the interim, defenders should closely monitor and restrict usage of the `--container` parameter, ensure proper input validation and sanitization are in place, and consider implementing compensating controls such as Web Application Firewalls (WAFs) to detect and prevent exploitation attempts.

Recommended defensive actions

• Patch MLflow to version v3.7.0 or later immediately.
• Restrict and monitor usage of the `--container` CLI parameter.
• Implement input validation and sanitization for container image names.
• Consider deploying Web Application Firewalls (WAFs) to detect and prevent exploitation attempts.
• Conduct a thorough inventory check of MLflow installations and their exposure.
• Develop and implement a compensating controls strategy.

Evidence notes

The CVE-2025-14287 vulnerability is confirmed to exist in MLflow versions before v3.7.0. The issue is caused by the direct interpolation of user-supplied container image names into shell commands without proper sanitization. Official sources, including NVD and CVE.org, provide details on the vulnerability's impact and CVSS score. Red Hat also provides additional information and potential mitigations for affected systems.

Sources and references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.