Agentic AI Red Teaming: Hands-On Security Testing

About this course

This course contains the use of artificial intelligence.

AI agents do more than answer questions. They plan, call tools, store memories, coordinate with other agents, and take action. Every capability creates a new security boundary. This hands-on course, Agentic AI Red Teaming: Hands-On Security Testing, teaches you to evaluate those boundaries and uncover failures before they reach production.

You'll learn agentic AI security, AI red teaming, LLM security testing, and adversarial AI evaluation through a realistic enterprise case study: BrightDesk, a fictional multi-agent customer support platform. Instead of relying on slides alone, you'll work with an isolated AI agent testing sandbox, synthetic records, mock APIs, local tools, and repeatable tests. See how an apparently harmless instruction can redirect a plan, trigger an inappropriate tool call, or contaminate later decisions.

What You'll LearnMap AI agent attack surfaces across planning loops, prompts, tool execution, external data, long-term memory, and multi-agent orchestration.

Test prompt injection, goal hijacking, instruction override, multi-turn attacks, and gradual goal drift without touching real customer systems.

Evaluate tool-call exploitation, unsafe tool chaining, excessive permissions, weak authorization, and untrusted tool responses.

Investigate memory poisoning, persistent state attacks, context manipulation, and retrieval-based threats in RAG workflows.

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