AI Red Teaming: Practical Attacks on LLMs and AI Systems
Build a specialist skill set in AI security with Cybersteps’ AI Penetration Testing course. This hands-on program teaches real-world attack techniques against LLMs and AI systems.
How does our AI Red Teaming course work?
1. Prepare with Confidence
2. Practice on Real Systems
3. Add a Specialist Skill
A new attack surface, arriving faster than the rules to secure it
AI systems are moving into production faster than most organizations can test them — and the regulation that will require testing them is already written, even if enforcement is still phasing in.
annualized AI-related revenue running through Azure alone, up 123% year-over-year
Microsoft FY2026 Q3 earnings — a proxy for how fast AI systems are reaching production
new EU AI Act deadlines for mandatory risk management and testing of high-risk AI systems
EU AI Act, Digital Omnibus package (final Council approval, June 2026)
of organizations believe they currently have the AI security talent they need
Cited to World Economic Forum via secondary sources — verify against WEF’s original report before publishing
Framework-grounded, not a single vendor exam
This is a specialist skill layered onto existing security fundamentals, built around real, current industry frameworks — OWASP’s Top 10 for LLM Applications and MITRE ATLAS — rather than one vendor’s certification track.
Who this is for
AI penetration testing is an emerging specialization, not yet a standardized entry-level job title — it’s best approached as an addition to a security background, not a first job on its own.
Security professionals adding an AI specialization
For pentesters, SOC analysts, or security engineers extending existing skills into testing AI and LLM-based systems — one of the fastest-growing specialty tracks inside established security roles.
AI/LLM penetration testing and red-teaming roles
Titles like AI Red Teamer and AI Security Analyst are starting to appear at large tech companies, dedicated AI security vendors, and consultancies — focused on adversarial testing of deployed AI systems before attackers find the same weaknesses.
AI governance and compliance track
A less offensive, more governance-focused path: supporting organizations through EU AI Act risk assessments and ongoing audits of AI systems already in production.
Job titles in this field are still standardizing across the market. Treat it as a specialization you add to an existing security foundation, not a guaranteed job outcome on its own.
What the course covers
Structured around the frameworks security teams actually use to evaluate AI systems today.
Foundations: how LLMs and AI systems work
The architecture and data flows you need to understand before you can attack or defend them.
OWASP Top 10 for LLM Applications (2026)
Prompt injection, sensitive information disclosure, excessive agency, supply chain risk, and data/model poisoning — the current industry-standard risk list.
Adversarial ML techniques via MITRE ATLAS
Mapping attacker tactics and techniques against machine learning systems using the ATLAS knowledge base.
Hands-on penetration-testing labs
Practical exercises: prompt injection, jailbreaking, data exfiltration attempts against sandboxed AI systems — validated through the labs themselves, not a certification exam.
Governance, reporting, and EU AI Act compliance
Turning findings into the risk documentation organizations will need ahead of the 2027/2028 enforcement dates.
