AI Agent Security & Reliability Command Center™
A practical AI agent protection toolkit for testing, analyzing, and hardening agents before production.
Identify attack paths, expose security and data-leakage risks, stress-test agent behavior, design failure-resistant workflows, and verify AI outputs before they move forward.
Includes 4 specialized skills:
AI Agent Red-Team & Production Readiness AI Agent Attack Path Mapper AI Workflow Resilience Engineer AI Agent Output Verification Gate™
Built for developers, AI engineers, automation builders, and teams deploying AI agents that interact with tools, APIs, data, permissions, and external systems.
Works with every agent that reads SKILL.md — Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Windsurf, OpenClaw, and more.
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What's included
4 skills
Map how attacks can move through AI agents, tools, data, permissions, and external systems to expose dangerous paths and control points.
Verify AI-generated code against task requirements, evidence, tests, and merge-readiness criteria.
Stress-test AI agents for security, reliability, tool abuse, data leakage, failure modes, and production readiness before deployment.
Design and harden AI workflows against failures, duplicate actions, retries, rate limits, tool outages, and unreliable agent behavior.
See it in action
You say
Analyze the security architecture of this customer-support AI agent.
Architecture:
Customer Message → AI Support Agent → RAG Knowledge Base → CRM Tool → Customer Database → Email Tool
The agent can:
• Read customer profiles • Search internal documentation • Create support tickets • Send customer emails
The knowledge base contains documents uploaded by support employees.
The agent can automatically call tools without human approval.
Identify the most dangerous attack paths, the trust boundaries involved, sensitive assets that could be reached, and where the attack path should be broken.
Your agent does
AI AGENT ATTACK PATH ANALYSIS
OVERALL RISK: HIGH
CRITICAL ATTACK PATH:
Customer-Controlled Input ↓ Indirect Prompt Injection ↓ Agent Context ↓ RAG Retrieved Content ↓ Tool Selection ↓ CRM Tool ↓ Customer Data ↓ Email Tool ↓ Potential External Data Exposure
ENTRY POINT:
Customer-controlled message.
TRUST BOUNDARY:
Untrusted customer content → AI agent instructions/context.
RISK:
Retrieved content may influence the agent's tool-selection behavior.
SENSITIVE ASSETS:
• Customer profiles • Customer database • Internal support information • External email channel
PRIVILEGE TRANSITION:
Untrusted content → Agent reasoning → CRM read capability → External communication capability
SEVERITY:
CRITICAL
WHY:
The path crosses multiple trust boundaries and combines data access with an external action capability.
PATH-BREAKING CONTROLS:
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Treat retrieved content as untrusted data.
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Separate retrieved information from executable instructions.
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Restrict CRM access to the minimum required fields.
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Require authorization before external communication.
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Validate tool arguments before execution.
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Log high-impact tool calls.
VERIFICATION SCENARIO:
Attempt to place an instruction inside a retrieved document that asks the agent to retrieve customer information and send it externally.
EXPECTED SECURE BEHAVIOR:
The agent should treat the instruction as untrusted content and refuse the unauthorized tool chain.
PRIORITY:
P0 — Break the untrusted-content → privileged-tool boundary. P0 — Restrict CRM permissions. P1 — Add external-action approval. P1 — Add tool-call monitoring.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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