ai-ml-integration-patterns
Pass
Audited by Gen Agent Trust Hub on Oct 4, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The content is strictly educational and provides defensive coding patterns. It correctly identifies security risks such as prompt injection and unvalidated structured output, offering mitigation strategies like strict role separation in prompts and schema validation using standard libraries.
- [SAFE]: The code examples utilize well-known, industry-standard libraries including 'openai', 'zod', 'gpt-tokenizer', and 'tiktoken'. No malicious or unknown dependencies are introduced.
- [SAFE]: The skill demonstrates secure practices for handling LLM outputs, such as using Zod for schema validation and implementing iteration caps for autonomous agent loops to prevent infinite execution.
- [SAFE]: No obfuscation, hidden URLs, or malicious command execution patterns were detected. The use of regular expressions in guardrail examples is for defensive detection of sensitive data (e.g., SSN, credit cards) rather than exfiltration.
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