AI Red Teaming Course: 100 Labs, Attack & Defend LLMs

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

Adversarial AI Security is a hands-on course for security engineers, AI engineers, and red teamers who want to understand how LLMs and AI agents actually get attacked, and how to defend them. Across 100 labs, run entirely against your own local, authorized sandbox, you learn to execute prompt injection, jailbreaks, RAG poisoning, and agentic exploits, then build the layered defenses that stop them.

Most “AI security” content stops at a jailbreak screenshot. That tells you a model can be tricked. It doesn’t tell you whether your application, your RAG pipeline, your agent’s tool access, or your audit trail would survive the same attack in production. The gap between “I got the model to say something bad” and “I can defend a production AI system under a real red-team engagement” is the entire point of this course.

How the course works

Every lab runs against a local, intentionally vulnerable range that you build and control.

Every offensive technique in this course is taught for the purpose of understanding and building defenses, never for use against systems you do not own or have explicit authorization to test.

What you’ll work with

Docker, Kubernetes, Terraform, and Ollama for a local, air-gapped LLM sandbox

Prompt injection, DAN-style jailbreaks, token smuggling, and encoding bypass techniques

Llama Guard, NeMo Guardrails, and Pydantic-based output validation

ChromaDB and Milvus, RAG poisoning, and RBAC-protected retrieval

Model Context Protocol (MCP), agentic tool calling, and gVisor sandboxingmTLS, OpenAPI contract validation, and cryptographic approval gates

Differential privacy, PII redaction, and model extraction and inversion concepts

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