AI Penetration Testing Course: Hands-On Training for Security Practitioners

This AI penetration testing course is designed for security professionals who need to assess and harden machine learning systems against real-world attacks. It includes six core modules: adversarial input generation, model inversion techniques, data poisoning detection, API-level exploitation, red teaming workflows, and audit reporting for compliance frameworks like NIST AI RMF.

Upon completion, practitioners can conduct end-to-end penetration tests on AI-powered applications, identify model-specific vulnerabilities, and produce audit-ready findings aligned with enterprise risk policies. The course is ideal for security analysts, red team operators, and compliance officers responsible for AI governance.

SEC2904 Mastering CSA STAR; A Step-by-Step Guide to Cloud Security Assurance provides complementary cloud security context, but this course focuses specifically on offensive techniques against AI models.

What the Course Covers

The curriculum is structured around practical attack simulations. Module one introduces threat modelling for AI systems using the MITRE ATLAS framework, mapping known tactics such as model stealing and prompt injection to detection and mitigation strategies.

Module two dives into adversarial machine learning, teaching how to craft inputs that cause misclassification, critical for image recognition or NLP systems. Labs include generating perturbed inputs that fool computer vision models while remaining imperceptible to humans.

Module three covers model inversion and membership inference attacks, where attackers reverse-engineer training data from model outputs. This has direct implications for data privacy compliance under regulations like GDPR, particularly when models are trained on sensitive information.

Module four explores data poisoning, how malicious actors can corrupt training datasets to introduce backdoors or biases. Participants learn to detect anomalies in model behaviour and trace them to specific data sources.

Module five focuses on API exploitation, simulating attacks on RESTful interfaces that serve AI models. Common issues include insufficient authentication, rate limiting bypasses, and prompt manipulation in generative AI systems.

Module six is dedicated to reporting and remediation. Participants learn how to document findings in a way that supports audit readiness under standards like and communicate technical risks to non-technical stakeholders.