Adversarial Attacks & Defenses

This lesson explores adversarial attacks and defensive strategies designed to protect AI systems from manipulation, deception, and abuse. Learners will examine how attackers exploit weaknesses in machine learning models through techniques such as adversarial examples, evasion attacks, poisoning, model extraction, and prompt injection. The lesson also covers defensive controls, resilience engineering, adversarial training, monitoring, validation, and governance practices that help organizations strengthen the security and trustworthiness of AI systems.