PosFuzz: augmenting greybox fuzzing with effective position distribution

Abstract

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness. Mutation-based greybox fuzzing generates input cases by mutating the input seed, i.e., applying a sequence of mutation operators to randomly selected mutation positions of the seed. However, existing fruitful research work focuses on scheduling mutation operators, leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency. This paper proposes a novel greybox fuzzing method, PosFuzz, that statistically schedules mutation positions based on their historical performance. PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations. PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator. It then leverages two sampling methods in different mutating stages to select the positions from the distribution. We have implemented PosFuzz on top of AFL, AFLFast and MOPT, called Pos-AFL, -AFLFast and -MOPT respectively, and evaluated them on the UNIFUZZ benchmark (20 widely used open source programs) and LAVA-M dataset. The result shows that, under the same testing time budget, the Pos-AFL, -AFLFast and -MOPT outperform their counterparts in code coverage and vulnerability discovery ability. Compared with AFL, AFLFast, and MOPT, PosFuzz gets 21% more edge coverage and finds 133% more paths on average. It also triggers 275% more unique bugs on average.