📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🤖 THE OFFENSIVE AI OPERATOR
PREMIUM
Part of the Offensive AI Operator — 30-Day Course
Setup · Day 0 of 30 · range not yet built
⚠️ Authorised targets only. The range you build today exists so every attack in this course lands on infrastructure you own: local containers, locally hosted models, and your own budget-capped cloud account. Nothing here is ever pointed at someone else’s systems. Building the lab correctly is the first act of doing this work legally.
Ninety percent of people who quit a hands-on security course quit on setup — not on the hard material, on the lab that wouldn’t build. So before we touch a single attack, we’re going to build your AI red team lab properly, pin every moving part so it behaves the same in six months as it does today, and end with one command that green-checks the whole thing. Get this hour right and the next thirty days just work. Skip it, or rush it, and you’ll spend Day 1 debugging Docker instead of learning to break models. Let’s build the range.
🎯 What You’ll Build in Setup
A pinned host: Kali/Ubuntu 24, Docker, Python 3.11 — reproducible, not “latest”
Local models via Ollama that behave identically every run
The provided vulnerable LLM app, running and reachable
Burp wired as your proxy, and a safe budget-capped cloud account
A passing verify.sh — your green light to start Day 1
⏱️ ~60 min · 3 exercises · gates the entire course Before you start, you need:
- A machine with 16 GB RAM recommended (8 GB works with a smaller quantised model), ~40 GB free disk, and virtualization enabled.
- Comfort in a terminal and with Burp — this course assumes a working pentester. If that’s not you yet, start with our general hacking lab setup first.
- A credit/debit card for a free-tier cloud account (used read-mostly, capped — you should not incur real charges if you follow the alarm step).
AI Red Team Lab Setup 2026: Build Your Range in One Hour
- Why the range is built this way
- The build: seven steps to a hot range
- The one command that proves it works
- What your range will and won’t teach you
- What breaks in the real world
- Failure states — the ten setup problems
- Your deliverable
Welcome to the setup module for the Offensive AI Operator. This is Day 0 — un-numbered, because it’s not a lesson, it’s the workbench every lesson sits on. By the end you’ll have a complete, pinned AI red team lab and a green verify.sh. Then, on Day 1, we start mapping. If you’ve built a traditional lab before — our legal home-lab guide covers that world — this is the AI-specific version, and the differences matter.
Why the AI red team lab is built this way
Before the commands, spend two minutes on the why, because it explains every choice you’re about to make and it’s the difference between a lab you understand and a lab you copy-pasted. Three decisions define this range, and each one is a deliberate answer to a way AI labs usually rot.
Decision one — everything runs locally
You could point this course at a paid API and learn plenty. We don’t, for three reasons that all matter. Cost: firing thousands of attack iterations at a metered API turns a course into a bill. Determinism: a hosted model updates on someone else’s schedule, so the exploit you learn on Tuesday can vanish on Thursday when the vendor ships a new version — I’ll show you that failure mode later and it’s maddening. And terms of service: hammering a commercial endpoint with jailbreak payloads is a fast way to get an account banned, and it blurs the ethical line this course is careful to keep sharp. Local open models via Ollama solve all three at once — free, frozen, and entirely yours to abuse.
There’s a fourth reason I’ll say plainly because it’s easy to miss: running the model yourself means you can see inside. When the target is somebody’s API, the model is a black box — you send text, you get text, and everything in between is hidden. When you host it, you control the temperature, you can watch resource usage spike when a payload makes the model work harder, and later in the course you can inspect what the model actually received after the app assembled its prompt. That visibility turns guesswork into observation. A hosted API teaches you to attack blind; a local range teaches you to attack with the lights on, and the instincts you build with the lights on make you far sharper when you’re eventually back in the dark against a real target.
Decision two — everything is pinned
“Pinned” is the word you’ll hear me repeat more than any other. A lab that says “install the latest Kali” or “pull the model” is a lab that behaves differently for two students on the same day and differently for you next month. We pin the host version, the container tags, and — most importantly — the exact model digest, not the floating latest tag. When you build your own labs after this course, pinning is the single discipline that separates a range you can trust from a range that lies to you. A result you can’t reproduce is not a result.
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