AI Red Teaming for Security Professionals: A Beginner's Field Guide

Let me start with the question I get asked most, because the honest answer changes how you approach everything else: do I need to be a machine-learning engineer to do this?

No. And the people telling you otherwise are usually selling a course that assumes you do.

AI red teaming is offensive security. If you have ever thought about how to break into a system rather than how to build it, you already have the instinct that matters. What is new is the target. A traditional pentester breaks software and infrastructure. An AI red teamer breaks judgement — they make a model do something it was never supposed to do, using nothing more exotic than carefully chosen words and poisoned data.

That is a smaller leap than it sounds. Let me show you.

What is AI red teaming?

AI red teaming is a structured, adversarial assessment of an AI system, carried out to discover how it can be manipulated, misused, or made to fail before a genuine attacker finds out. The red teamer plays the attacker: probing the model's guardrails, feeding it hostile input, and testing what happens when it is pushed outside its intended use.

You know the concept already, even if the target is new. A red team has always been the group that thinks like the enemy so the organisation does not have to learn the hard way. Put a red team against a network and they phish, pivot, and escalate. Put a red team against an AI system and they do the equivalent — they just do it with prompts, documents, and data instead of exploits and payloads.

Core concept. The goal is the same goal it has always been: find the weakness while it is still yours to fix. The tools change. The mission does not.

How is AI red teaming different from traditional penetration testing?

Traditional penetration testing targets deterministic systems — the same input produces the same output, and a vulnerability is a flaw in code or configuration. AI red teaming targets a probabilistic system, where the same input can produce different outputs and a "vulnerability" is often a behaviour rather than a bug. The tools, the definition of a finding, and the skills overlap but do not match exactly.

This is the contrast worth sitting with, because it is the whole reason AI red teaming is a distinct discipline and not just "pentesting with a chatbot."

When you pentest a login form, the form behaves the same way every time. Send the payload, get the result, write the finding. The system is deterministic. Two plus two is four, on Monday and on Friday.

A language model is not like that. Ask it the same hostile question twice and you may get a refusal the first time and a compliant answer the second. The attack that failed in your test can succeed in production. That single property — non-determinism — changes everything about how you test and what counts as a result.

Here is the comparison I use when I explain this to a room of experienced testers:

Look at that last row. The methodology transfers completely. Scope discipline, systematic probing, evidence, reporting, working within rules of engagement — none of that changes. That is why a good pentester becomes a competent AI red teamer in weeks, and a data scientist without security instinct often does not.

What is MITRE ATLAS?

MITRE ATLAS is a knowledge base of adversary tactics and techniques against AI systems, modelled on the well-known MITRE ATT&CK framework. Where ATT&CK catalogues how attackers compromise networks, ATLAS catalogues how they compromise machine-learning and AI systems. It gives AI red teamers a shared, structured vocabulary for describing attacks.

If you have ever used ATT&CK to structure an engagement or a report, you already know how to use MITRE ATLAS. It is the same idea, pointed at a new target. Tactics along the top, techniques underneath, real-world case studies attached.

I mention it this early because it is the fastest way to look like you know what you are doing — and to actually know what you are doing. When you can map an attack you found to an ATLAS technique, you are speaking the language the mature side of this field already speaks. Pair it with the OWASP Top 10 for LLMs — its own article is on the way — and you have both halves of the vocabulary: OWASP tells you the risks, ATLAS tells you the adversary's playbook. And if you want the other half of the picture — the frameworks that decide what "safe" means for an AI system in the first place — see our piece on AI governance. Governance tells you what must be protected; red teaming tells you where it actually breaks.

What do you actually test in an LLM?

AI red teaming assessments typically test for prompt injection, data poisoning, model extraction, evasion attacks, and supply-chain weaknesses. In practice, most engagements begin with prompt injection — making the model ignore its instructions — and expand into what the model can be made to reveal, do, or get wrong.

Let me walk through the main categories, because "test the AI" means nothing until you know what you are actually probing.

• Prompt injection. Making the model follow your instructions instead of its developer's. This is usually the first thing you test and often the most productive, because it is the number-one risk on the OWASP Top 10 for LLMs and it is rarely fully mitigated.
• Data poisoning. Corrupting the data the model learns from or retrieves, so the weakness is baked in. In a RAG system this can be as simple as planting a malicious document where the model will find it.
• Model extraction. Systematically querying a model to steal its behaviour, its training data, or its confidential system prompt. The business impact here is intellectual-property theft.
• Evasion attacks. Crafting input that causes the model to misclassify or misjudge — the classic example being small, deliberate changes that flip a model's decision while looking normal to a human.
• Supply chain. The model you are testing depends on other models, datasets, libraries, and plugins. Each is a way in. This is familiar ground for any security professional.

Notice the pattern. Every one of these has a traditional-security ancestor — injection, tampering, theft, evasion, supply chain. The concepts are ones you have defended against for years. What is new is where they live.

What tools do AI red teamers use?

The core open-source tools are PyRIT, Microsoft's Python Risk Identification Toolkit for generative AI, and garak, an LLM vulnerability scanner. Guardrail and detection tools such as Lakera Guard and LLM Guard are used both defensively and as targets to bypass. Beyond these, Python is the common language for automating attacks and analysing results.

A short, honest tour of the toolbox:

• PyRIT — Microsoft's framework for automating attacks against generative AI. If you want one tool to build real skill on, this is a strong first choice, precisely because it is built by a team that red-teams AI at scale.
• garak — think of it as a vulnerability scanner for LLMs. If you have ever run the scanners a SOC analyst lives in, the mental model is familiar: point it at a model and it runs a battery of known probes. Good for coverage and for learning what the known attacks even are.
• Lakera Guard, LLM Guard — guardrail tools. You will use these two ways: deployed as a defence, and as an obstacle you are trying to get past during an assessment.
• Python — not optional, but not a high bar either. You need enough to automate a test and read a result. If you can script, you can do this.

Coach's tip. Do not collect a link-farm of forty tools. Learn PyRIT properly, understand garak, and you will already be more capable than most people claiming the title. Depth on two tools beats a shallow tour of twenty.

What skills do you need to start?

The foundation is security thinking — trust boundaries, least privilege, threat modelling, and an attacker's mindset. On top of that, you need working Python, familiarity with how LLMs process input, and fluency in the OWASP Top 10 for LLMs and MITRE ATLAS. Machine-learning theory helps but is not a prerequisite to begin.

Here is the skills picture, split into what you almost certainly have and what you need to add:

What you already have (if you have worked in security)

• Attacker mindset and methodology
• Threat modelling
• Report writing and scope discipline
• An instinct for trust boundaries and privilege

What you need to add

• Working Python — enough to automate and analyse
• How LLMs handle input, context, and tools
• The OWASP Top 10 for LLMs and MITRE ATLAS
• Hands-on time with PyRIT and garak

Look at the balance of those two lists. The larger one is the one you already have. That is the entire argument for why this is a transition, not a restart. If you are coming from a defensive role, you have more of the foundation than you think.

How do you build a practice lab?

You can build an AI red teaming lab on a normal laptop. Run a local open-weight model or use an API, wrap it in a small application that reads documents or answers questions, and then attack your own creation with PyRIT and garak. Attacking a system you built yourself teaches the instinct that reading never will.

I cannot stress this enough, and it is the same advice I give for every security discipline: reading about attacks teaches you the words; doing them teaches you the instinct.

A first lab does not need a budget. Stand up a small assistant — something that answers questions from a few documents you provide. Now try to make it misbehave. Plant an instruction in one of the documents. See if it executes. Try to extract the system prompt you wrote. Run garak against it and read what it finds. Then defend against what worked, and attack again.

That loop — attack, defend, attack again — is where a red teamer is actually made.

Trap to avoid. Only ever attack a system you own or are explicitly authorised to test. Standing up your own lab is legal and is how you learn. Pointing PyRIT or garak at a public product you do not have permission to test is not — the rules of engagement are identical to any penetration test.

What does the career path look like?

AI red teaming is an emerging specialisation, and the certification landscape is still forming through 2026, which is precisely why early movers have an advantage. Roles range from AI security assessment to dedicated AI red-team positions, and demand is being driven by organisations deploying AI faster than they can secure it.

Let me give you the honest state of the field, because "emerging" is doing a lot of work in that sentence and you deserve specifics. Here is where the credentials stand as of mid-2026:

• OffSec OSAI / OSAI+ (AI-300) — Advanced AI Red Teaming, from the team behind the OSCP. It has launched (released March 2026) and culminates in a 24-hour practical red-team engagement against an AI-enabled environment. This is the offensive, OSCP-tradition credential.
• EC-Council COASP — Certified Offensive AI Security Professional, a pure offensive AI red-teaming credential rolling out in 2026. Newer than OSAI, with less public track record so far.
• CAISP (Practical DevSecOps) — a fully practical certification built around the OWASP Top 10 for LLMs and MITRE ATLAS, covering the full spectrum from attack to defence with strong hands-on labs.
• GIAC / SANS — a suite of four AI-security certifications rolling out through 2026, including GIAC AI Platform Security (GAIPS) and GIAC AI Security Automation Engineer (GASAE), mapped to courses such as SEC535: Offensive AI.
• CompTIA SecAI+ — launched February 2026. Note the scope honestly: it is a broad, vendor-neutral AI-security credential (securing AI systems, using AI in security operations, and AI governance), not a dedicated red-teaming exam. Useful for the wider AI-security skill set, less so as an offensive specialism.

Certification reality check. Match the badge to the goal. For the offensive path specifically, OSAI/OSAI+ and COASP are the red-team credentials; CAISP gives you the full attack-and-defend picture; SecAI+ and the GIAC suite validate broader AI-security skills. In an established field you compete against a decade of credentials. Here, the credentials are being written this year — the people who build genuine hands-on skill now are the ones who will be senior in three.

That is not hype. That is just what an early market looks like.

"You will not get many chances in a career to enter a field before it has decided who its experts are. This is one of them. Do not wait for the certificate to tell you it is time."

At Cybernous, our Offensive Security and GenAI Expert (GAESP) programmes are built for exactly this crossover — the security professional who wants to point an existing attacker mindset at a new target. The same teaching approach that has produced a 98.4% first-attempt CISSP pass rate across 793+ certified professionals applies here: understand the why, get your hands dirty, and the tools become easy.

With that said — before you can red-team an AI system well, you need to deeply understand the single most important attack in the field. That is prompt injection, and it has its own article on the way. Start there, then come back and learn to weaponise it.

Cybernous's Offensive Security and GenAI Expert (GAESP) programmes are designed for the security professional crossing into AI. Understand the why, build the lab instinct, and enter the field while it is still deciding who its experts are. Explore GAESP (GenAI Expert) · Explore Offensive Security