AI Red Teaming Playbook: Automated Adversarial Testing
AI Red Teaming Playbook: Automated Adversarial Testing
A practical guide to testing your AI agents and LLM apps for security and compliance risks.
AI agents don’t just answer questions. They call tools, access data and take actions in connected systems. Security testing built for deterministic code can’t keep up: the same prompt can fail today and succeed tomorrow with minor rephrasing, and every model swap, prompt update or new tool connection changes how an agent behaves.
This playbook gives security teams a framework for red teaming AI agents and LLM apps across their full lifecycle.
What’s inside:
- Key risks to AI agents: prompt injection, tool misuse, cascading failures, memory poisoning, system prompt extraction and fragile intent, and how attackers chain them together
- Where pen testing falls short: what traditional testing catches and what it misses with AI systems
- The AI red teaming lifecycle: what to test at each stage, from scope and planning through production
AI agents don’t just answer questions. They call tools, access data and take actions in connected systems. Security testing built for deterministic code can’t keep up: the same prompt can fail today and succeed tomorrow with minor rephrasing, and every model swap, prompt update or new tool connection changes how an agent behaves.
This playbook gives security teams a framework for red teaming AI agents and LLM apps across their full lifecycle.
What’s inside:
- Key risks to AI agents: prompt injection, tool misuse, cascading failures, memory poisoning, system prompt extraction and fragile intent, and how attackers chain them together
- Where pen testing falls short: what traditional testing catches and what it misses with AI systems
- The AI red teaming lifecycle: what to test at each stage, from scope and planning through production
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AI agents don’t just answer questions. They call tools, access data and take actions in connected systems. Security testing built for deterministic code can’t keep up: the same prompt can fail today and succeed tomorrow with minor rephrasing, and every model swap, prompt update or new tool connection changes how an agent behaves.
This playbook gives security teams a framework for red teaming AI agents and LLM apps across their full lifecycle.
What’s inside:
- Key risks to AI agents: prompt injection, tool misuse, cascading failures, memory poisoning, system prompt extraction and fragile intent, and how attackers chain them together
- Where pen testing falls short: what traditional testing catches and what it misses with AI systems
- The AI red teaming lifecycle: what to test at each stage, from scope and planning through production