  A large chunk of research on the security issues of neural networks is
focused on adversarial attacks. However, there exists a vast sea of simpler
attacks one can perform both against and with neural networks. In this article,
we give a quick introduction on how deep learning in security works and explore
the basic methods of exploitation, but also look at the offensive capabilities
deep learning enabled tools provide. All presented attacks, such as
backdooring, GPU-based buffer overflows or automated bug hunting, are
accompanied by short open-source exercises for anyone to try out.
