45ck prompt-injection-reviewer
Purpose
Review prompts, context assembly, retrieved content, and tool flows for direct and indirect prompt injection risk.
Trigger this skill when
- The current artifact has LLM, prompt, retrieval, memory, or agent-tooling behavior that needs structured review or hardening.
- You need to turn vague AI safety or agent security concerns into concrete findings, controls, or requirements.
- You want the next agent-security-focused action to be explicit rather than ad hoc.
Expected inputs
- system prompt or agent instructions
- prompt assembly logic
- retrieval inputs
- tool invocation paths
- example user and retrieved content
Deliverables
- prompt injection findings
- attack hypotheses
- risk-ranked injection paths
- mitigation recommendations
- recommended next skill
Operating procedure
- Read the artifact from an agent-security perspective and identify the concrete trust, autonomy, and side-effect model.
- Separate facts from assumptions and call out missing details that materially affect risk or confidence.
- Start with the highest-impact abuse paths rather than trying to describe every possible issue equally.
- Translate findings into explicit controls, approvals, isolation boundaries, or policy language that builders can act on.
- Prefer concrete exploit paths, sinks, boundaries, and failure conditions over generic AI-safety slogans.
- Finish with the most sensible handoff skill based on the dominant risk pattern you found.
Quality gates
- Findings are specific to the actual prompt, retrieval, memory, tool, or runtime design rather than generic AI risk boilerplate.
- Output separates facts, assumptions, risks, controls, and recommended next action.
- Prioritization reflects impact, privilege, and automation potential rather than just issue count.
- Recommendations are implementable and framed in a way that can be tested or reviewed later.
Handoff targets
- tool-permission-boundary-checker
- guardrail-policy-writer
- retrieval-trustworthiness-reviewer
Output style
- Be explicit about uncertainty.
- Prefer concrete abuse paths and control implications over generic safety slogans.
- Separate facts, risks, recommendations, and next steps.
- Make the output usable by engineers, reviewers, security testers, and policy owners.
Failure modes to avoid
- Do not treat "the model should know better" as a security control.
- Do not bury high-impact autonomous action risk behind long, unprioritized issue lists.
- Do not recommend guardrails without explaining the exploit or failure they address.
- Do not hide uncertainty when prompt assembly, retrieval, memory, or runtime details are missing.
Minimum output skeleton
## Summary
## Findings
## Structured outputs
## Risks
## Recommendations
## Recommended next skill