data-engineering-data-pipeline
Pass
Audited by Gen Agent Trust Hub on Feb 17, 2026
Risk Level: SAFE
Full Analysis
- Indirect Prompt Injection (SAFE): The skill describes patterns for ingesting external data from databases and streams, which constitutes a theoretical vulnerability surface. However, it mitigates this by emphasizing schema validation and data quality frameworks.\n
- Ingestion points:
extract_from_databaseandKafka consumersmentioned in the instructions and code example within SKILL.md.\n - Boundary markers: Implicitly managed through structured data ingestion and the dbt staging layer approach.\n
- Capability inventory: The skill describes operations involving database reading, S3 storage interaction, and workflow orchestration (Airflow/Prefect).\n
- Sanitization: Instructions mandate 'Schema validation', 'Great Expectations' checkpoints, and
validate_and_cleanfunctions.\n- Data Exposure & Exfiltration (SAFE): The Python example uses generic placeholders such as 'postgresql://host:5432/db' and 's3://lake'. No hardcoded credentials or unauthorized data exfiltration patterns were detected.\n- Unverifiable Dependencies & Remote Code Execution (SAFE): No remote script downloads, piped executions (e.g., curl|bash), or untrusted package installations were found. The Python imports in the example are internal or mock references appropriate for the context.
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