FGo: A Directed Grey-box Fuzzer with Probabilistic Exponential cut-the-loss Strategies

TL;DR

FGo is a novel directed grey-box fuzzer that uses probabilistic exponential cut-the-loss strategies to efficiently terminate unreachable test cases, significantly improving fuzzing speed without extra overhead.

Contribution

FGo introduces a probabilistic exponential cut-the-loss approach that enhances directed fuzzing by early termination of unreachable cases without additional static analysis.

Findings

FGo is 106% faster than AFLGo in crash reproduction.

The probabilistic strategy effectively balances termination and exploration.

Analysis of parameter effects on algorithm performance.

Abstract

Traditional coverage grey-box fuzzers perform a breadth-first search of the state space of Program Under Test (PUT). This aimlessness wastes a lot of computing resources. Directed grey-box fuzzing focuses on the target of PUT and becomes one of the most popular topics of software testing. The early termination of unreachable test cases is a method to improve directed grey-box fuzzing. However, existing solutions have two problems: firstly, reachability analysis needs to introduce extra technologies (e.g., static analysis); secondly, the performance of reachability analysis and auxiliary technologies lack versatility. We propose FGo, a probabilistic exponential cut-the-loss directed grey-box fuzzer. FGo terminates unreachable test cases early with exponentially increasing probability. Compared to other technologies, FGo makes full use of the unreachable information contained in iCFG and doesn't generate any additional overhead caused by reachability analysis. Moreover, it is easy to generalize to all PUT. This strategy based on probability is perfectly adapted to the randomness of fuzzing. The experiment results show that FGo is 106% faster than AFLGo in reproducing crashes. We compare multiple parameters of probabilistic exponential cut-the-loss algorithm and analyze them in detail. In addition, for enhancing the inerpretability of FGo, this paper discusses the difference between the theoretical performance and the practical performance of probabilistic exponential cut-the-loss algorithm.