XAIHP:Xcademia AI Hacker Practitioner

The AI Hacking Certification for the Threat Nobody Has Trained For Yet 

The certified ethical hacker who does not understand prompt injection, adversarial examples, or model extraction is not a complete offensive security practitioner in 2026. AI systems are deployed everywhere: customer service, hiring, fraud detection, code review, healthcare decision support. Every one of them is a target. 

CEH does not cover these attack techniques. OSCP does not cover them. XEHP, XART, and the traditional offensive security certifications focus on network infrastructure, web applications, and operating system exploitation. None of them address the specific vulnerabilities of AI systems as targets. 

XAIHP is the certification that fills this gap. Eight instructor-led days covering the full adversarial AI attack surface and the defensive frameworks that protect against it.

The security professional who holds a traditional pen testing certification and an XAIHP is the professional who can assess the full attack surface of a modern organisation: the network, the applications, and the AI systems. The one who holds only traditional certs is missing an attack surface that is growing faster than any other. 

Why No Existing Certification Covers This 

The gap is structural, not accidental. AI security as a discipline is younger than the certifications that currently dominate the offensive security market. CEH was created in 2003. OSCP in 2006. The adversarial machine learning research that underpins AI attack techniques was primarily theoretical until 2017, and only became practically relevant to commercial AI deployments as large language models became widely deployed from 2022 onwards. 

The certification bodies that created traditional offensive security qualifications have been slow to update their curricula for AI-specific attack techniques. As of 2026, no major exam-based certification body has produced a comprehensive qualification specifically addressing adversarial AI, prompt injection at depth, model-level attacks, or agentic AI exploitation. 

Some certifications touch these topics as modules within broader AI courses. None of them assess whether candidates can actually execute these attack techniques in realistic conditions. 

The gap in the certification market is not a temporary oversight that will be filled next quarter. It reflects the speed at which AI deployment has outpaced both the security community's preparedness and the certification industry's ability to respond. XAIHP was built precisely because waiting for someone else to build it was not a viable option. 

What XAIHP Covers Across Eight Days 

The programme is structured around the actual adversarial AI attack surface as it exists in 2026, with specific attention to the techniques being actively used by threat actors rather than those that remain primarily theoretical. 

Days 1-2: Foundations of AI systems and their attack surfaces 

• How large language models, neural networks, and machine learning classifiers work: At a level sufficient to understand their specific vulnerabilities 

• The MITRE ATLAS framework: Adversarial AI taxonomy, technique mapping, and integration with traditional ATT&CK 

• AI deployment architectures and the attack surfaces they introduce: APIs, agentic systems, RAG pipelines, fine-tuned models 

• Threat modelling for AI systems: Applying STRIDE and AI-specific threat models to realistic deployment scenarios 

Days 3-4: Prompt injection and LLM exploitation 

• Direct prompt injection: Techniques for overriding system prompts, bypassing safety guardrails, and causing models to produce unintended outputs 

• Indirect prompt injection: Embedding malicious instructions in documents, web pages, emails, and other content that AI systems retrieve and process 

• Agentic AI exploitation: Targeting AI agents that can take real-world actions, causing them to execute attacker-controlled instructions 

• Jailbreaking techniques: Historical and current approaches to bypassing model safety training, why they work, and their limitations 

• Live lab: Prompt injection exercises against realistic AI application deployments 

Days 5-6: Adversarial examples and model attacks 

• Adversarial example generation: Creating inputs that cause misclassification in image, text, and multimodal models 

• Model extraction attacks: Systematic querying to reconstruct a target model's behaviour 

• Model inversion: Extracting training data from deployed models using output analysis 

• Training data poisoning: Understanding how attackers corrupt model training pipelines 

• Live lab: Adversarial example generation and model extraction exercises against controlled targets 

Days 7-8: Defensive AI security and assessment methodology 

• AI red team methodology: How to conduct a structured adversarial assessment of an AI deployment 

• Defensive controls: Input validation for AI, output filtering, model watermarking, differential privacy, adversarial training 

• AI security assessment reporting: Documenting AI-specific findings in a format that engineering and product teams can act on 

• Capstone: A full adversarial assessment of a realistic AI deployment, from threat modelling to finding documentation 

The capstone assessment 

Candidates conduct a structured adversarial assessment of a realistic AI application: an AI-powered hiring tool with a web interface, a connected database, and an agentic capability that can take actions. They must identify the attack surface, execute prompt injection and adversarial testing, attempt model extraction, and produce a professional AI security assessment report. The capstone is assessed by a senior Xcademia practitioner with real-world AI security experience. Verifiable at xcademia.com/verify. 

The XAIHP capstone is an actual AI security assessment, not a simulation. Candidates who pass it have successfully attacked an AI system under professional assessment conditions. That is the evidence that no multiple choice examination can produce. 

Who Needs XAIHP 

The professionals who need this certification are at the intersection of offensive security and AI deployment. 

• Penetration testers and red team operators who need to assess the full attack surface of organisations deploying AI systems 

• Security architects responsible for designing secure AI deployments who need to understand the attack techniques they are defending against 

• AI engineers and ML engineers who need to understand the security implications of the systems they build 

• SOC analysts and threat hunters who need to detect AI-specific attack patterns in their monitoring data 

• Security consultants advising on AI security posture who need an assessed qualification in the specific domain 

• CISOs and security leaders who need practitioners on their team capable of assessing AI-specific risk 

The market for this expertise is growing faster than the supply. Every organisation that deploys a significant AI system without assessing its AI-specific attack surface is a potential XAIHP client for the professional who can conduct that assessment. 

The AI hacking market is forming right now. The professionals who develop this specialism in 2026 will be in a position that mirrors where cloud security specialists were in 2012, ahead of the demand curve, in the space where the money and the complexity are both heading.