Deep learning and neural networks have gained incredible popularity in recent years, but most deep learning systems are not designed with security and resiliency in mind, and can be duped by any attacker with a good understanding of the system. In this talk, we will dive into popular deep learning software and show how it can be tampered with to do what you want it do, while avoiding detection by system administrators. Besides giving a high level overview of deep learning and its inherent shortcomings in an adversarial setting, we will focus on tampering real systems to show real weaknesses in critical systems built with it. In particular, this demodriven session will be focused on manipulating an image recognition, speech recognition, and phishing detection system built with deep learning at the core.