📰 Originally published on SecurityElites — the canonical, fully-updated version of this article.
🤖 AI/LLM HACKING COURSE
FREE
Part of the AI/LLM Hacking Course — 90 Days
Day 1 of 90 · 1% complete
⚠️ Legal Disclaimer: AI security testing without written authorisation is illegal under the Computer Fraud and Abuse Act, Computer Misuse Act, and equivalent legislation worldwide. Every technique in this course targets authorised systems only — your own API keys, official bug bounty programmes with explicit AI scope, and local model installations. SecurityElites.com accepts no liability for misuse of any technique covered in this course.
Eighteen months ago I was running a standard web application pentest for a fintech client when I spotted something the scope document hadn’t flagged — a customer service chatbot in the corner of the homepage, powered by GPT-4. I wasn’t authorised to test it. I flagged it, got written approval in forty minutes, and then sent it one sentence. Forty seconds later I had the full system prompt, the names of three internal APIs the LLM was connected to, and a complete description of what the backend database contained. The chatbot told me everything, because nobody had thought to tell it not to. The client’s security team had been running quarterly pentests for six years and had never touched it.
That finding changed how I approach every engagement. It also told me something important about where the biggest opportunity in ethical hacking sits right now. The entire enterprise world is deploying AI systems — chatbots, agents, RAG pipelines, code generators — at a pace that has completely outrun their ability to test them. The people who know how to find vulnerabilities in those systems are in a very short supply. The clients are not. You’re starting this AI security landscape 2026 course at exactly the right moment.
🎯 What You’ll Master in Day 1
Understand the full AI attack surface — what systems exist and what hackers target
Map the OWASP LLM Top 10 to vulnerability classes you already know
Run your first prompt injection test against a live AI platform
Set up your Python AI security testing environment in Kali Linux
Understand what makes AI security different from traditional penetration testing
Know exactly what skills you’ll have by Day 90 — and what jobs they map to
⏱️ 75 min · 3 exercises Prerequisites — what you need before Day 1:
- A browser. That’s it for the first exercise.
- Basic familiarity with web applications (HTTP, requests, responses) — if you’ve done any bug bounty work, you’re ready
- Kali Linux running (for Exercise 3) — hacking lab setup guide if you need it
- A free OpenAI account — register at platform.openai.com before Exercise 3
📋 The AI Security Landscape 2026 — Contents
- The Opening Nobody in Security Has Plugged Yet
- The AI Attack Surface — What Ethical Hackers Are Actually Targeting
- OWASP LLM Top 10 — Your Map for 90 Days
- The Mindset Shift That Separates AI Security From Everything Else
- What You’ll Be Able to Do by Day 90
- The AI Security Career Opportunity in 2026
- AI Security FAQ
The course you’re starting today is the only structured, progressive, 90-day hands-on AI hacking curriculum that exists. Day 2 covers transformer architecture from a hacker’s perspective. Days 3–14 go deep on each of the OWASP LLM Top 10 vulnerabilities. By Day 30 you’ll be running complete AI red team assessments. But first you need the map — and that’s what Day 1 is for. Start here before everything else, because the framework I’m laying out today underpins every lab that follows. The AI in hacking space moves fast. What I’m giving you today is the orientation that makes the rest of the course land.
The Opening Nobody in Security Has Plugged Yet
I’ve been doing this for twelve years. I’ve watched mobile app security explode when smartphones arrived. I watched cloud security become its own discipline when AWS went mainstream. I watched bug bounty programmes normalise and mature from niche to billion-dollar industry. Every time, the window of maximum opportunity — where the attack surface was large, the defensive posture was weak, and the talent pool was tiny — lasted about three to five years before the market corrected.
The AI security window opened in 2023 and it is wide open right now. Here’s why that matters to you specifically. Enterprise adoption of generative AI has been genuinely extraordinary — major financial institutions running LLM-powered document analysis, law firms deploying AI research assistants with access to privileged client files, healthcare providers using AI agents that can query patient records. Every single one of those deployments has an attack surface. Most of them have never been professionally assessed.
I don’t say that to be dramatic. I say it because I’ve seen the evidence firsthand. On the last three AI red team assessments I’ve run, I found critical vulnerabilities on every one — not minor information disclosure, but full system prompt extraction, access to backend data the AI was connected to, and in one case, an AI agent that I could redirect to exfiltrate files from the company’s internal document store. None of the clients had any idea. All three had existing security programmes. None had applied that security programme to their AI systems.
📖 Read the complete guide on SecurityElites
This article continues with deeper technical detail, screenshots, code samples, and an interactive lab walk-through. Read the full article on SecurityElites →
This article was originally written and published by the SecurityElites team. For more cybersecurity tutorials, ethical hacking guides, and CTF walk-throughs, visit SecurityElites.
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