machine-learning-ops-ml-pipeline
Pass
Audited by Gen Agent Trust Hub on Jun 23, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [PROMPT_INJECTION]: The skill uses the
$ARGUMENTSvariable to pass user requirements directly into prompts for multiple sub-agents (Data Engineer, Data Scientist, etc.). This structure allows user-supplied text to potentially influence or override the instructions intended for the specialized agents. - [PROMPT_INJECTION]: The following evidence chain for indirect prompt injection was identified:
- Ingestion points: User input enters the system via the
$ARGUMENTSvariable in the main SKILL.md file and is propagated to sub-agents in Phase 1. - Boundary markers: Absent. The user input is interpolated directly into the sub-agent prompt strings without delimiters or instructions to ignore embedded commands.
- Capability inventory: The sub-agents are prompted to generate executable Python code (using frameworks like PyTorch and FastAPI), Kubernetes manifests, and automation scripts (CI/CD pipelines).
- Sanitization: No sanitization, validation, or escaping of the
$ARGUMENTSinput is performed before interpolation into the sub-agent prompts.
Audit Metadata