airflow-dag-patterns
Pass
Audited by Gen Agent Trust Hub on Aug 11, 2026
Risk Level: SAFE
Full Analysis
- [COMMAND_EXECUTION]: The skill provides examples of
PythonOperatorand@taskdecorators which execute Python code. This is the primary function of Apache Airflow and is intended behavior for workflow orchestration within the user's environment. - [DATA_EXFILTRATION]: Contains a reference to
https://api.example.com/healthwithin a custom sensor example inresources/implementation-playbook.md. This uses a standard placeholder domain for demonstration purposes and does not attempt to exfiltrate sensitive data. - [DYNAMIC_EXECUTION]: The
resources/implementation-playbook.mdfile demonstrates dynamic DAG generation by updating the Pythonglobals()dictionary. This is a standard and documented practice in Apache Airflow for programmatically creating multiple DAG objects from configuration files. - [INDIRECT_PROMPT_INJECTION]: The skill demonstrates patterns for ingesting external data from S3 buckets using
pandas, which represents a potential surface for indirect injection if the source data is attacker-controlled. - Ingestion points: Data is read via
pd.read_csv()from S3 paths in the TaskFlow and Dynamic DAG examples inresources/implementation-playbook.md. - Boundary markers: None present in the boilerplate code samples; users are expected to implement their own data validation.
- Capability inventory: The templates include capabilities for arbitrary Python execution (
PythonOperator), network requests (requests), and cloud storage access (S3KeySensor). - Sanitization: No input validation or sanitization logic is included in the provided illustrative code snippets.
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