AlphaFuzz: Evolutionary Mutation-based Fuzzing as Monte Carlo Tree Search
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Fuzzing is becoming more and more popular in the field of vulnerability detection. In the process of fuzzing, seed selection strategy plays an important role in guiding the evolution direction of fuzzing. However, the SOTA fuzzers only focus on individual uncertainty, neglecting the multi-factor uncertainty caused by both randomization and evolution. In this paper, we consider seed selection in fuzzing as a large-scale online planning problem under uncertainty. We propose which is a new intelligent seed selection strategy. In Alpha-Fuzz, we leverage the MCTS algorithm to deal with the effects of the uncertainty of randomization and evolution of fuzzing. Especially, we analyze the role of the evolutionary relationship between seeds in the process of fuzzing, and propose a new tree policy and a new default policy to make the MCTS algorithm better adapt to the fuzzing. We compared with four state-of-the-art fuzzers in 12 real-world applications and LAVA-M data set. The experimental results show that could find more bugs on lava-M and outperforms other tools in terms of code coverage and number of bugs discovered in the real-world applications. In addition, we tested the compatibility of , and the results showed that could improve the performance of existing tools such as MOPT and QSYM.
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