ml-pipeline-automation
Pass
Audited by Gen Agent Trust Hub on Sep 15, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONDYNAMIC_EXECUTIONCOMMAND_EXECUTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill ingests external data for model training and validation, creating a potential surface for indirect injection if data sources are compromised.
- Ingestion points: Python scripts in
SKILL.mdandreferences/airflow-patterns.mdload CSV data from paths like/data/raw/using pandas. - Boundary markers: There are no explicit delimiters or specific markers used to separate data from instructions during the file ingestion phase.
- Capability inventory: The skill utilizes
PythonOperatorandBashOperatorfor script execution, performs network requests via Slack webhooks and MLflow tracking servers, and performs file system writes. - Sanitization: Extensive data quality validation logic, including schema checks and statistical validation, is provided in
references/pipeline-monitoring.mdas a primary mitigation. - [DYNAMIC_EXECUTION]: The skill uses templates to generate executable scripts and dynamically registers tasks within the orchestration environment.
- Pattern evidence:
SKILL.mdcontains instructions to write Python DAG definitions directly to the filesystem.references/airflow-patterns.mddemonstrates dynamic DAG generation using theglobals()registry. - [COMMAND_EXECUTION]: The skill requires the execution of shell commands to initialize Airflow databases, start background services, and trigger pipeline runs. These are standard operations for the described workflow but represent active system interaction capabilities.
Audit Metadata