Arbitrary Command Injection Affecting mlflow package, versions [2.11.0,2.22.5)[3.0.0rc0,3.8.1)
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Start learning- Snyk IDSNYK-PYTHON-MLFLOW-15825746
- published30 Mar 2026
- disclosed30 Mar 2026
- creditCole Murray
Introduced: 30 Mar 2026
CVE-2025-15379 (opens in a new tab)How to fix?
Upgrade mlflow to version 2.22.5, 3.8.1 or higher.
Overview
mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
Affected versions of this package are vulnerable to Arbitrary Command Injection in the _install_model_dependencies_to_env function. An attacker can execute arbitrary commands by supplying a crafted model artifact containing malicious dependency specifications in the python_env.yaml file, which are unsafely interpolated into a shell command during model deployment with env_manager=LOCAL.
Note: The vulnerable path is reached during model deployment, where MLflow reads dependency entries from the model artifact and installs them in the local environment.