Recommendation engines are quiet, high-value infrastructure — they decide what customers see first, which products get momentum, and which sellers win the algorithm. That also makes them a target. A coordinated group of fake accounts can fabricate demand for a mediocre product, bury a competitor’s listing, or slowly poison a model’s understanding of what “popular” means — and by the time anyone notices, the damage is baked into weeks of training data. NIST’s 2025 adversarial ML taxonomy, MITRE ATLAS, and the OWASP ML Top 10 all now treat this as a formal risk category. Most ML courses never mention it at all.
This course closes that gap directly: you build a real recommender system, then you attack it yourself — safely, on synthetic data, against your own lab infrastructure — and then you defend it with the same rigor a production ML security team would use. This is not a course about breaking real platforms. Every offensive technique here runs under a documented Red-Team Rules of Engagement, against synthetic data and owned local services only.
