Machine learning has made tremendous progress over the last decade. It’s thus tempting to believe that ML techniques are a “silver bullet”, capable of making progress on any real-world problem they are applied to. But is that really so? In this talk, I will discuss a major challenge in the real-world deployment of ML: making ML solutions robust, reliable and secure. In particular, I will focus on the phenomenon of widespread vulnerability of state-of-the-art ML models to various forms of adversarial noise.