mlflow
Pass
Audited by Gen Agent Trust Hub on Oct 11, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides standard instructional documentation for the MLflow library without any detected malicious patterns or injection attempts.
- [CREDENTIALS_UNSAFE]: No hardcoded credentials or secrets were found. The skill explicitly promotes the best practice of using environment variables (
MLFLOW_TRACKING_URI) instead of hardcoding sensitive configuration. - [DATA_EXFILTRATION]: Network activity is limited to a local tracking URI (
http://localhost:5000) commonly used for development, with no indicators of unauthorized data exfiltration. - [DYNAMIC_EXECUTION]: The code examples demonstrate standard machine learning workflows; no unsafe uses of
eval(),exec(), or untrusted deserialization were identified.
Audit Metadata