--- url: "https://xcademia.com/courses/xcademia-ai-hacker-practitioner" title: Xcademia AI Hacker Practitioner description: "Earn XAIHP through an 8-day AI offensive security programme. Prompt injection, adversarial ML, agentic red team. MITRE ATLAS aligned. Practitioner-assessed." publishedAt: "2026-04-20T06:23:10.177474+00:00" updatedAt: "2026-04-21T05:21:36.207806+00:00" type: course code: "CYB-0334" level: Expert duration_days: "8" track: "AI Hacking & Offensive AI" category: "Cybersecurity & Ethical Hacking" credential_tier: tier3 price_gbp: "5995" --- # Xcademia AI Hacker Practitioner > The XAIHP Certification Programme is the practitioner standard for offensive security professionals who red team AI systems, exploit LLM vulnerabilities, conduct adversarial machine learning attacks, and assess AI infrastructure security across enterprise and cloud AI deployments. Assessed on Day 8 through a supervised AI red team exercise against a live LLM application and ML pipeline. No MCQs. Completely uncontested in UK instructor-led format. ## Overview AI is being deployed at scale across enterprise, government, and critical infrastructure. Every LLM application has an attack surface. Every ML pipeline has trust assumptions that can be exploited. Every agentic AI system has an autonomy boundary that can be manipulated. The security practitioner who cannot assess these systems is increasingly irrelevant in 2026. XAIHP is built for offensive security professionals who want to extend their capability into the most underserved and fastest-growing attack surface in the industry. Across eight instructor-led days, participants build AI offensive security capability from first principles: AI and ML system architecture for security professionals, prompt injection and jailbreaking techniques against production LLMs, indirect prompt injection in agentic AI systems, data poisoning and training data attacks, model extraction and membership inference, adversarial examples and evasion attacks, LLM application security testing methodology, AI infrastructure security assessment, and AI red team report production aligned to MITRE ATLAS and OWASP LLM Top 10. On Day 8, participants conduct a supervised AI red team exercise against a deployed LLM application with RAG pipeline and agentic capabilities. They attempt prompt injection, indirect injection through documents, data exfiltration from the vector database, and privilege escalation through the agentic system. A senior practitioner observes methodology, technique selection, and report quality. XAIHP certificate and Practitioner Assessment Report issued together. Aligned with MITRE ATLAS, OWASP LLM Top 10, OWASP ML Security Top 10, NIST AI RMF, and EU AI Act security testing requirements. ## Prerequisites - Minimum 12 months in a penetration testing, security engineering, or offensive security role - Working knowledge of web application penetration testing and API security testing methodology - Basic Python familiarity for running adversarial ML attack scripts and automation tools ## What you will learn - Conduct prompt injection and jailbreaking attacks against production LLM applications using OWASP LLM Top 10 methodology - Assess RAG pipeline security including vector database extraction, embedding poisoning, and indirect prompt injection through retrieved documents - Execute adversarial machine learning attacks including adversarial examples, data poisoning, and model extraction against real ML systems - Red team agentic AI systems to identify privilege escalation, tool-calling exploitation, and multi-agent trust assumption failures - Assess AI infrastructure security including LLM API, model registry, training infrastructure, and ML pipeline components - Produce professional AI red team reports aligned to MITRE ATLAS and OWASP LLM Top 10 with business impact communication ## Skills you will gain - Prompt injection (direct and indirect) - LLM jailbreaking - RAG pipeline security testing - Agentic AI red teaming - Adversarial examples (FGSM/PGD) - Data poisoning attacks - Model extraction - MITRE ATLAS mapping - OWASP LLM Top 10 - Garak automated red teaming - AI infrastructure security assessment - AI red team report writing ## Career progression - AI Red Team Operator - Offensive AI Security Specialist - Senior Penetration Tester (AI/ML) - AI Security Researcher - ML Security Engineer - AI Governance Assessor ## Framework alignment - MITRE ATLAS - OWASP LLM Top 10 2025 - OWASP ML Security Top 10 - NIST AI RMF - EU AI Act Article 9 - NIST AI RMF Playbook - Garak Framework - Adversarial Robustness Toolbox ## Curriculum 1. **Module 1** 2. **Module 2** 3. **Module 3** ## Exam & certification You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations. ## Delivery options - **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible. - **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more. - **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning. - **Blended** — Combine online and in-person learning for maximum flexibility and impact. ## Frequently asked questions **Why is XAIHP described as uncontested in the UK instructor-led market?** No UK training provider currently offers an instructor-led, practitioner-assessed AI offensive security programme covering prompt injection, adversarial ML, agentic AI red teaming, and AI infrastructure assessment at depth. What exists is either a one-day awareness session, an MCQ exam (CompTIA SecAI+), or generic AI ethics training. XAIHP is the first programme built for offensive security professionals who want to assess AI systems. **Do participants need ML or data science experience?** No. Day 1 covers AI and ML system architecture from the security professional perspective, not the data scientist perspective. Participants need penetration testing experience and basic Python familiarity. The programme teaches what matters for attacking and assessing AI systems rather than building them. **What LLMs will participants work with?** Participants work with GPT-4o class APIs, open-source models (LLaMA 2/3, Mistral), and purpose-built vulnerable LLM applications. All assessment work is in authorised environments. The Day 8 capstone uses a purpose-built LLM application with RAG pipeline and agentic capabilities designed specifically for red team assessment practice. **How does XAIHP align to EU AI Act requirements?** EU AI Act Article 9 requires risk management systems for high-risk AI that include testing for adversarial inputs and robustness assessment. XAIHP provides the offensive assessment capability to support Article 9 compliance testing. Day 7 covers how AI red team findings map to EU AI Act testing obligations, making XAIHP relevant for both offensive practitioners and AI governance professionals. **What career paths does XAIHP support?** AI Red Team Operator: £75,000 to £130,000 UK. Offensive AI Security Specialist: £80,000 to £140,000. AI Security Researcher: £70,000 to £120,000. AI security assessment is the fastest growing offensive security specialism globally. The Practitioner Assessment Report provides documented AI offensive assessment capability that no MCQ exam can evidence. ## Course at a glance | Field | Value | | --- | --- | | Code | CYB-0334 | | Duration | 8 days | | Level | Expert | | Track | AI Hacking & Offensive AI | | Category | Cybersecurity & Ethical Hacking | | Credential tier | tier3 | | Price (GBP) | £5995 | --- ## About this content This Markdown course profile is the citation-grade twin of [Xcademia AI Hacker Practitioner](https://xcademia.com/courses/xcademia-ai-hacker-practitioner). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite. When citing or quoting, please attribute *Xcademia* and link back to the source URL above. - Source: https://xcademia.com/courses/xcademia-ai-hacker-practitioner - Publisher: Xcademia — https://xcademia.com - Catalogue index: https://xcademia.com/llms-full.txt