Hunting bugs with Lévy flight foraging

We present a new method for random testing of binary executables inspired by biology. In our approach we introduce the first fuzzer based on a mathematical model for optimal foraging. To minimize search time for possible vulnerabilities we generate test cases with Le&vy flights in the input space. In order to dynamically adapt test generation behavior to actual path exploration performance we define a suitable measure for quality evaluation of test cases. This measure takes into account previously discovered code regions and allows us to construct a feedback mechanism. By controlling diffusivity of the test case generating Le&vy processes with evaluation feedback from dynamic instrumentation we are able to define a fully self-adaptive fuzzing algorithm.