ml-pipeline-automation

No obvious malware or intentional backdoor is present. The code implements legitimate KFP/MLflow workflows, but deployment and tracking endpoints are contacted over unauthenticated HTTP, deployment writes to a production path without visible authorization, and untrusted serialized models or artifacts could enable code execution when loaded. Restrict and authenticate pipeline inputs and service endpoints, use TLS, enforce MLflow and deployment authorization, validate artifacts, and avoid loading untrusted joblib/MLflow models.