genai-acceptance-review

name	genai-acceptance-review
description	Review workflow for AI/LLM output usage to prevent over-trust, injection, and unsafe automation.

GenAI Acceptance Review

When to use

Use this skill when a system consumes LLM output to make decisions or perform actions.

Inputs to collect (if available)

• What the model output is used for (advisory vs actionable)
• Tools/capabilities available to the system (file writes, network calls, deploys)
• Data entering prompts (PII/secrets? retrieved content sources?)
• Approval model (human-in-the-loop? step-up auth?)

Threats to consider

• Prompt injection (content causes the model to ignore instructions)
• Over-trust / tool misuse (model output drives privileged actions)
• Data leakage (secrets/PII included in prompts or outputs)
• Indirect injection via retrieved content (RAG, web pages, PDFs)

Step-by-step process

1. Map the AI boundary
   • Where prompts are built, where tools are called, what data enters/leaves.
2. Classify outputs
   • Advisory: suggestions for humans
   • Actionable: used by code to execute, write files, call APIs, change permissions
3. Apply controls by class
   • Advisory: disclaimers, human review, logging with redaction
   • Actionable: strict schema validation, allow-lists, capability gating, step-up approvals
4. Prompt & retrieval hardening
   • Separate system instructions from untrusted content
   • Use structured output (JSON schema) and reject invalid outputs
   • Limit context sources; sanitize retrieved content where possible
5. Add misuse tests
   • Include injection strings and verify they don’t trigger privileged actions
6. Document safe usage
   • Clear rules for what the model may decide vs what code must enforce

Output

• Boundary diagram (textual is fine)
• Control recommendations (prevent/detect/respond)
• Test cases for injection and over-trust scenarios

Repo integration (optional)

Related prompt:

• check-for-unvalidated-genai-acceptances.prompt.md

Output format

• Boundary map: where untrusted content enters, where model output leaves
• Threats: top 5 with likelihood/impact
• Controls: prevent/detect/respond mapped to advisory vs actionable use
• Validation: misuse/prompt-injection test scenarios

Examples

• “LLM suggests shell commands that CI executes” → require allow-listed command templates + schema validation + human approval for privileged operations.