Guiding a colony of black-box fuzzers with chemotaxis

We present a bio-inspired method for large-scale fuzzing of binary executables to detect vulnerabilities. In our approach we deploy a small group of feedback-driven explorers that guide a colony of black-box fuzzers to promising regions in input space. We achieve this by applying the biological concept of chemotaxis: The explorer fuzzers mark test case regions that drive the target binary to previously undiscovered execution paths with an attractant. This allows us to construct a force of attraction that draws the black-box fuzzers to high-quality test cases. We implement a prototype and evaluate our presented algorithm to show the feasibility of our approach.