Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage

Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verificationbased approaches, they require specialist knowledge, and are timeconsuming. Recently, we introduced HyperGI, a dynamic, hypertestbased approach that can detect and produce potential fixes for hyperproperty violations. In particular, we focused on violations of the noninterference property, as it results in information flow leakage. Our instantiation of HyperGI was able to detect and reduce leakage in three small programs. Its fitness function tried to balance information leakage and program correctness but, as we pointed out, there may be tradeoffs between keeping program semantics and reducing information leakage that require developer decisions. In this work we ask if it is possible to automatically detect and repair information leakage in more realistic programs without requiring specialist knowledge. We instantiate a multi-objective version of HyperGI in a tool, called LeakReducer, which explicitly encodes the tradeoff between program correctness and information leakage. We apply LeakReducer to six leaky programs, including the wellknown Heartbleed bug. LeakReducer is able to detect leakage in all, in contrast to state-of-the-art fuzzers, detecting leakage in only two programs. Moreover, LeakReducer is able to reduce leakage in all subjects, with comparable results to previous work, while scaling to much larger software.