Side channels in software are a class of information leaks where non-functional side effects of software systems (such as execution time, memory usage or power consumption) can leak information about sensitive data. In this talk, I present my research on a new class of side-channel vulnerabilities: JIT-induced side channels. In contrast to side channels introduced at the source code level, JIT-induced side channels arise at runtime due to the behavior of just-in-time (JIT) compilation. I show the existence of this class of side channels across multiple runtimes, and I demonstrate JIT-induced timing channels in large, open source projects large enough in magnitude to be detected over the public internet. I also present an automated approach to inducing this type of side channel in programs. In evaluating my automated technique, I show that programs classified as side-channel free by four state-of-the-art side channel analysis tools are, in fact, vulnerable to JIT-induced side channels. Finally, I discuss my contributions towards scalable quantification of side-channel vulnerabilities through a caching framework for model-counting queries.