prompt-engineering-patterns
Pass
Audited by Gen Agent Trust Hub on Mar 2, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [PROMPT_INJECTION]: The skill contains logic to interpolate data into system and user prompts, which establishes a surface for indirect prompt injection.\n
- Ingestion points: External data is processed through the
rendermethods inPromptTemplateandConditionalTemplate(defined inreferences/prompt-templates.md) and within theevaluate_promptmethod inscripts/optimize-prompt.py.\n - Boundary markers: Although the templates use descriptive sections (e.g.,
Context:,Input:), they do not employ robust isolation techniques or specific instructions to the model to ignore potential directives embedded within the data.\n - Capability inventory: The skill interacts with external LLM APIs via the
openailibrary and performs local file writing for result persistence (optimization_results.json).\n - Sanitization: There is no evidence of variable sanitization, filtering, or delimiter-escaping to prevent malicious input from overriding intended prompt logic.
Audit Metadata