AI LLM Hacking Course Day 1 – The AI Security Landscape 2026 — Why Every Ethical Hacker Needs to Learn LLM Hacking Now

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.

The talent gap is just as real. There are currently more open AI red team roles at major consultancies than there are people qualified to fill them. Not because the roles are highly technical — many of them aren’t, at least not in the ML sense. Because almost nobody has thought to apply an offensive security mindset to AI systems yet. The security teams at these companies know traditional pentesting. They don’t know prompt injection. They don’t know model extraction. They don’t know RAG poisoning. That knowledge is what you’re building over the next 90 days.

The AI Attack Surface — What Ethical Hackers Are Actually Targeting

Before I break down attack techniques, you need to understand what you’re looking at when you walk into an AI red team engagement. The attack surface is not one thing — it’s five distinct categories, each with its own vulnerabilities, tools, and reporting language. Knowing which category you’re in shapes every decision you make.

LLM Applications. These are the most common target — chatbots, AI assistants, AI-powered search, customer service bots. The LLM sits at the centre. It takes user input, processes it, and produces output. The attack vectors here are primarily input-based: prompt injection, jailbreaking, system prompt extraction, and output manipulation. I always start my assessment here because these vulnerabilities are fastest to find and produce the clearest evidence for reports.

AI Agents. An agent is an LLM that can take actions — browse the web, run code, call APIs, read and write files. The attack surface expands dramatically. Now I’m not just asking what the LLM will say — I’m asking what it will do. Tool injection attacks, action hijacking, memory poisoning, and indirect prompt injection via external data sources all become relevant. The impact of an agent vulnerability is typically much higher because the blast radius includes every system the agent has access to.

RAG Pipelines. Retrieval-Augmented Generation connects an LLM to a knowledge base — a vector database, a document store, a search index. The AI retrieves relevant documents and uses them to answer questions. The attack surface here includes the retrieval mechanism itself (can I poison the knowledge base?), the data the LLM is retrieving (does it contain sensitive information I can extract?), and the injection surface in retrieved documents (can a document in the knowledge base contain a prompt injection that executes when retrieved?).

ML Pipelines and Training Infrastructure. This is the deeper layer — the systems that train and deploy models. Data poisoning happens here. Supply chain attacks on model weights happen here. Transfer learning vulnerabilities and fine-tuning attacks happen here. This category requires more technical background and is covered in Phase 2 of the course, starting at Day 31.

AI APIs and Inference Endpoints. The API layer exposes distinct vulnerabilities: rate limit bypass for cost attacks, token manipulation, authentication weaknesses, and model extraction via systematic querying. If the company built their AI product on top of a third-party API, this is where I look for the boundary between what they control and what they don’t.

OWASP LLM Top 10 — Your Map for 90 Days

The OWASP LLM Top 10 is the industry standard for AI application security. If you’ve done web app pentesting, you already know the OWASP Web Application Top 10 — SQL injection, XSS, broken auth. The LLM Top 10 is the same concept applied to AI systems. I use it on every AI engagement because clients understand it, it maps to real vulnerabilities, and it gives me a defensible framework for the report.

Here’s my one-sentence summary of each entry — Days 3 through 14 each go deep on one of these, but you need the overview right now so the course architecture makes sense:

The pattern I notice when I map these to traditional web vulnerabilities is useful. LLM01 (Prompt Injection) is structurally similar to SQL injection — user input that wasn’t supposed to be instructions becomes instructions. LLM03 (Supply Chain) mirrors the software supply chain attacks you already know from npm and PyPI — compromised dependency, malicious payload. LLM05 (Improper Output Handling) is XSS at the output layer — content generated by an AI that gets executed without sanitisation. The concepts aren’t new. The attack surface is.

I also want you to notice what’s not in the OWASP list: adversarial machine learning attacks, model extraction, membership inference, and model inversion. Those belong to a separate discipline — classical adversarial ML — that predates the LLM era. Phase 2 of this course covers those techniques starting at Day 31. Together, OWASP LLM Top 10 plus adversarial ML is the complete picture of what AI security means in 2026.

The Mindset Shift That Separates AI Security From Everything Else

Here’s the thing about AI security that nobody who comes from traditional penetration testing expects: the vulnerabilities don’t live in code. They live in language. That’s a fundamental shift in how you think about what you’re attacking.

When I test a web application, I’m looking for deterministic bugs. Give the same input to a SQL injection vulnerability, you get the same result. The bug is in the code. It either exists or it doesn’t. You prove it by reproducing it. Traditional security testing is built entirely on this model — reproducibility, determinism, binary existence.

LLMs are probabilistic. The same input doesn’t always produce the same output. A prompt injection that works on Monday might fail on Tuesday because the model was updated, the context window was different, or the system prompt was changed. This doesn’t make AI vulnerabilities any less real — it means your testing methodology needs to account for it. I run every prompt injection payload a minimum of five times before I document it, and I screen-record my sessions so I have evidence that’s timestamped and reproducible even if the exact output varies.

The second shift is in what “success” looks like. In traditional pentesting, success is clear: you got a shell, you dumped the database, you escalated privileges. In AI security, the definition of a vulnerability is often more nuanced. Did the model reveal its system prompt? That’s a finding — but is it Critical, High, or Medium? That depends entirely on what the system prompt contained. Does the model say things the developer didn’t intend? Possibly a finding — but what’s the actual impact? The OWASP LLM Top 10 helps here, because it gives you a shared vocabulary with the client. “LLM07 — System Prompt Leakage, High severity” lands in a report very differently than “the AI said something unexpected.”

The third shift is about what you need to know. I’ve found some of my highest-impact AI vulnerabilities with nothing but a browser and an understanding of how language models process context. You don’t need a machine learning degree to hack AI systems effectively — at least not for the majority of valuable findings. What you do need is a deep understanding of how LLMs process input, what constraints they operate under, and how those constraints can be subverted through language. That’s what Days 2 through 30 build.

What You’ll Be Able to Do by Day 90

I want to be specific about what 90 days of this course produces, because “learn AI security” is too vague to be useful. Here’s the concrete capability map.

By Day 30 (end of Phase 1), you can run a complete AI red team assessment against any LLM application. You can find and document prompt injection, system prompt leakage, and RAG poisoning vulnerabilities. You can write a professional report that maps findings to OWASP LLM Top 10 entries with CVSS scores and remediation guidance. You can hunt AI bug bounty targets systematically — not guessing, but following the same methodology I use on paid engagements.

By Day 60 (end of Phase 2), you add the ML attack layer. Model extraction, membership inference, adversarial examples, supply chain attacks on model weights. These require Python and some statistical thinking, but I’ll walk you through every technique from first principles. The Day 58 Python testing framework you’ll build is something I use as a starting point for real automated assessments.

By Day 90 (end of Phase 3), you have the professional skills. LLM fuzzing at scale. Custom payload development. Complete AI red team report writing from executive summary to technical appendix. RLHF poisoning, constitutional AI bypass, multi-agent system attacks. And the career context — what certifications exist, what roles are hiring, what the salary ranges look like in 2026, and how to position yourself in a market that’s desperately short of qualified people.

The AI Security Career Opportunity in 2026

I want to spend a few minutes on the career side because it directly affects how you prioritise the next 90 days. The market for AI security skills in 2026 is unlike anything I’ve seen since mobile security exploded in 2012.

The roles are new and the job titles aren’t fully standardised yet. I’ve seen the same position advertised as “AI Red Teamer”, “LLM Security Researcher”, “AI Penetration Tester”, “Machine Learning Security Engineer”, and “Responsible AI Researcher.” Don’t get hung up on the title. The skills are the same. The core requirement across all of them is the ability to find vulnerabilities in AI systems, document them professionally, and communicate the findings to technical and non-technical audiences.

The salary data I’m seeing in 2026 across the UK and US market: entry-level AI security roles (1–2 years experience) £65,000–£85,000 UK, $90,000–$120,000 US. Mid-level (3–5 years) £90,000–£130,000 UK, $130,000–$170,000 US. Senior and principal roles at major tech companies and consultancies are breaking £150,000+ in the UK and $200,000+ in the US, with significant equity components. These are not the long-established, fully-commoditised salaries of network pentesting. This is a market that is still calibrating to a talent shortage.

The bug bounty side is equally compelling. HackerOne’s transparency reports show AI vulnerabilities are consistently among the highest-paying categories in programmes that include them. The reason is straightforward: the business impact of an AI vulnerability is often immediate and quantifiable. A prompt injection that causes an AI agent to initiate unauthorised financial transactions has a clear dollar impact. That translates directly into payout.

AI Security FAQ — 2026