The Hare, the Tortoise and the Fox : Extending Anti-Fuzzing

Abstract: Background. The goal of our master's thesis is to reduce the effectiveness of fuzzers using coverage accounting. The method we chose to carry out our goal is based on how the coverage accounting in TortoiseFuzz rates code paths to find memory corruption bugs. It simply looks for functions that tend to cause vulnerabilities and considers more to be better. Our approach is to insert extra function calls to these memory functions inside fake code paths generated by anti-fuzzing. Objectives. Our thesis researches the current anti-fuzzing techniques to figure out which tool to extend with our counter to coverage accounting. We conduct an experiment where we run several fuzzers on different benchmark programs to evaluate our tool. Methods. The foundation for the anti-fuzzing tool will be obtained by conducting a literature review, to evaluate current anti-fuzzing techniques, and how coverage accounting prioritizes code paths. Afterward, an experiment will be conducted to evaluate the created tool. To evaluate fuzzers the FuzzBench platform will be used, a homogeneous test environment that allows future research to easier compare to old research using a standard platform. Benchmarks representative of real-world applications will be chosen from within this platform. Each benchmark will be executed in three versions, the original, one protected by a prior anti-fuzzing tool, and one protected by our new anti-fuzzing tool. Results. This experiment showed that our anti-fuzzing tool successfully lowered the number of unique found bugs by TortoiseFuzz, even when the benchmark is protected by a prior developed anti-fuzzing tool. Conclusions. We can conclude, based on our results, that our tool shows promise against a fuzzer using coverage accounting. Further study will push fuzzers to become even better to overcome new anti-fuzzing methods.