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Hacking AI Agents: Hands-On LLM Red Teaming
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Hacking AI Agents: Hands-On LLM Red Teaming

Attack and harden a real LLM agent running on your own machine. Watch each technique run live, then run it yourself.
Created byDr. Amar Massoud
Last updated 10/2026
English
English

What you'll learn

  • Map the attack surface of an LLM app or agent and scope an authorized engagement
  • Run direct and indirect prompt injection and reproduce a data leak on a live local target
  • Bypass safety filters with persona, encoding, multi-turn, and crescendo techniques
  • Chain injection, memo poisoning, and tool abuse into an unauthorized money-movement kill chain
  • Run extraction and poisoning attacks and write a detector for the poisoned data
  • Layer defenses (guardrails, context separation, tool authorization) and re-run the attacks to measure the change
  • Automate red teaming with garak, PyRIT, and promptfoo, and gate a build in CI
  • Write a red-team report mapped to OWASP LLM 2026, MITRE ATLAS, and NIST AI RMF, and follow responsible disclosure

Course content

8 sections • 34 lectures • 1h 23m total length

  • Why AI Red Teaming — and Why Hands-On3:43
  • Where LLM Attacks Live — Tokens, Context, Tools3:28
  • DEMO: Meet the Target — Stride & its agent "Sol"1:19
  • Set Up Your Red-Team Lab1:21
  • Quiz: Foundations & Your Attack Range
  • Assignment 1 — Scope Your Engagement and Map Sol's Attack Surface

Requirements

  • A computer that can run a small local model (about 8 GB RAM recommended) and Python 3.11 or newer
  • Free software only: Ollama with a small local model; Docker is optional
  • Basic comfort with a terminal and reading short Python scripts
  • No prior AI security experience needed; general IT or security awareness helps

Description

This course contains the use of artificial intelligence.

Most AI security courses show slides. In this course we attack a real LLM agent. We build a deliberately vulnerable banking assistant on our own computer, then break it, harden it, and break it again.

The course combines 17 concept lectures, 9 live demos, and 8 hands-on labs. In the live demos we run each attack against a local target and watch the result on screen. In the labs we reproduce the attacks ourselves with downloadable starter files. Section quizzes, assignments, and a final practice exam help us check what we learned.

The lab application runs entirely on your own machine with a free local model. You need no paid API keys, no cloud account, and no real data. Every technique is practiced only against this local training target.

We cover prompt injection, hidden context exposure, jailbreaks and evasion, data extraction and poisoning, vector and embedding weaknesses, agent tool abuse, defenses, and automated red teaming with garak, PyRIT, and promptfoo. The content is mapped to the OWASP Top 10 for LLM Applications 2026 and MITRE ATLAS.

All techniques are for authorized security testing and education only.

Included: 17 concept lectures, 9 live demos against a local model, 8 hands-on labs with downloadable files, 8 section quizzes (47 questions), 8 assignments, and a 30-question final practice exam.

Who this course is for:

  • Security professionals, penetration testers, and red teamers moving into AI systems
  • Developers and engineers building LLM apps or agents who want to test them
  • AppSec, GRC, and AI governance practitioners who need hands-on understanding of AI risks
  • Students who learn best by watching attacks run and then repeating them

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