Machine Learning Security: Threats, Attacks, and Evaluation

Machine learning systems fail in ways that traditional security training doesn't prepare you for. No packet is intercepted, no perimeter is breached, and no credential is stolen. An attacker sends a legitimate request through a public interface, and a model trusted to make the right decision makes the wrong one. This course teaches you to recognize, model, and evaluate that entire class of failure. I built this course from my background as a published researcher in intelligent computing, with years spent working with neural networks and adaptive AI systems. As VP of Technical Content at Educative, I've seen that teams rarely struggle to build a model that performs well on a benchmark. They struggle to define what an attacker can and can't touch, and what evidence would actually prove a security claim true. Vague labels like "strong attacker" and unexamined robustness claims fill that gap, and a researcher's habit of reading every claim skeptically is the fix. You'll build a working mental model for ML security from the ground up: what "attack" means when nothing is intercepted, how to construct a rigorous threat model, and how to read a robustness claim, a defense proposal, or a published paper with an evidence-first eye. The scope ranges from a single misclassified image to an LLM agent tricked into taking a real-world action it was never asked for. By the end, you'll be able to look at any ML-powered system, whether a classifier, a RAG pipeline, or an LLM agent with tool access, and identify exactly where its trust boundaries are and what evidence would be needed to prove it secure.