engineering-autonomous-optimization-architect
Pass
Audited by Gen Agent Trust Hub on Aug 10, 2026
Risk Level: SAFE
Full Analysis
- [PROMPT_INJECTION]: The skill implements an 'LLM-as-a-Judge' architecture that processes real user data to evaluate model performance, creating a theoretical surface for indirect prompt injection.
- Ingestion points: 'real user data' is passed to the optimizeAndRoute function (SKILL.md) for shadow testing.
- Boundary markers: The skill does not explicitly define delimiters for separating untrusted data within evaluation prompts.
- Capability inventory: The skill routes traffic to external provider APIs and autonomously updates weights based on results.
- Sanitization: No specific sanitization or filtering logic is defined for the incoming user data. The risk is mitigated by the skill's focus on strict mathematical scoring and financial circuit breakers.
- [DATA_EXFILTRATION]: The skill communicates with well-known LLM providers (OpenAI, Anthropic, Gemini) for its primary purpose. No evidence of data exfiltration to unauthorized or suspicious third-party domains was detected.
- [COMMAND_EXECUTION]: The provided example code demonstrates logic for provider execution and cost monitoring. It does not perform arbitrary shell command execution or expose dangerous system-level capabilities.
Audit Metadata