CVE-2025-15379

[CVE catalog]

Severity

Critical · CVSS 10CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Published

Jan 1, 2025, 12:00:00 AM UTC

Source

[NVD] · [CVE record]

EPSS

2.36% · Oct 25, 2026

A flaw was found in MLflow. When deploying a model with `env_manager=LOCAL`, MLflow's model serving container initialization code, specifically the `_install_model_dependencies_to_env()` function, reads dependency specifications from the model artifact's `python_env.yaml` file. An attacker can supply a malicious model artifact, leading to command injection as these specifications are directly interpolated into a shell command without proper sanitization. This allows for arbitrary command execution on systems deploying the malicious model. mlflow: MLflow: Arbitrary command execution via command injection in model serving container initialization. This vulnerability in MLflow allows for arbitrary command execution when deploying a model with `env_manager=LOCAL`. An attacker can supply a malicious model artifact containing a crafted `python_env.yaml` file, leading to command injection during container initialization. During the deployment of the model MLflow passes the maliciously crafted requirement string to the shell command without properly sanitization of possible command injections, as consequence the attacker may be able to execute arbitrary code at the same permission level as the user running the MLflow process. Red Hat Product Security team has rated this vulnerability as having the impact of Important. Although it allows arbitrary code to successfully exploit this flaw the attacker depends on MLFlow having a non-default deployment option (env_manager=LOCAL), besides requiring enough privileges and access to deploy the malicious model or trick the user to download and deploy the malicious model. The CVSS score(s) listed for this vulnerability do not reflect the associated product's status, and are included for informational purposes to better understand the severity of this vulnerability.