Introduction to AI Evasion and Model Security

As artificial intelligence becomes integrated into critical systems, understanding its vulnerabilities is essential for modern security professionals and developers. This text-based course introduces the core concepts of inference-time evasion, where inputs are subtly manipulated to mislead classifiers. You will gain a solid conceptual foundation in adversarial machine learning, enabling you to identify weaknesses in AI models and implement robust defensive strategies. What you'll learn: - Understand the fundamental principles of adversarial machine learning and model vulnerabilities. - Explore how inference-time evasion attacks manipulate input data to bypass classifiers. - Analyze common evasion techniques, including perturbation and adversarial examples. - Study the mathematical and logical concepts behind decision boundaries in machine learning. - Learn modern defensive techniques, such as adversarial training and input sanitization, to secure AI systems. - Practice evaluating model robustness through conceptual exercises and scenario analysis. This course begins with essential terminology and the foundational mechanics of machine learning classifiers before moving into evasion concepts and defensive alignment. It is designed for beginners, developers, and security enthusiasts with no prior experience in AI security. Start building more secure and resilient artificial intelligence systems today.