Agentic Red-Teaming: Hands-On AI Agent Security

About this course

AI agents can plan, use tools, remember information and delegate work. Securing them requires more than testing a single prompt: you need to understand how decisions turn into actions, how failures spread, and whether defenses hold under a repeatable test.

Agentic Red-Teaming is a practical, advanced course for cybersecurity engineers, security analysts and DevSecOps engineers who want to assess and harden agentic systems. You will work through an evolving fictional service-desk application, HarborDesk, using synthetic data, mock tools and a controlled local environment.

What you will learn

Map agent architecture, attacker-controlled surfaces and trust boundaries.

Design authorized, reproducible single-turn and multi-turn assessment cases.

Test prompt injection, planning, tool use, memory, retrieval, agent handoffs and human approval workflows.

Use traces, gateway decisions and state changes to distinguish attempted violations from executed unauthorized actions.

Measure downstream propagation and the impact on legitimate user tasks.

Turn evidence into clear findings, practical mitigations and defensive regression tests.

Build a professional assessment workflow

Across 24 stages and 118 lessons, you will develop an attack-surface register, threat model, scenario cards, clean-run baselines, trace bundles, scoring sheets and actionable reports. Each stage combines focused instruction with demonstrations, practice and evidence you can review. The emphasis is on explaining what happened, reproducing it reliably and checking that a fix works.

Who this course is for

Cybersecurity engineers and analysts assessing AI-enabled applications.

DevSecOps engineers adding agent security checks to development workflows.

Security practitioners who understand prompt injection and want to move into tool-using and multi-agent systems.

Before you begin

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