FuzzGuard: Filtering out Unreachable Inputs in Directed Grey-box Fuzzing through Deep Learning

Recently, directed grey-box fuzzing (DGF) becomes popular in the field of software testing. Different from coverage-based fuzzing whose goal is to increase code coverage for triggering more bugs, DGF is designed to check whether a piece of potentially buggy code (e.g., string operations) really contains a bug. Ideally, all the inputs generated by DGF should reach the target buggy code until triggering the bug. It is a waste of time when executing with unreachable inputs. Unfortunately, in real situations, large numbers of the generated inputs cannot let a program execute to the target, greatly impacting the efficiency of fuzzing, especially when the buggy code is embedded in the code guarded by various constraints. In this paper, we propose a deep-learning-based approach to predict the reachability of inputs (i.e., miss the target or not) before executing the target program, helping DGF filtering out the unreachable ones to boost the performance of fuzzing. To apply deep learning with DGF, we design a suite of new techniques (e.g., step-forwarding approach, representative data selection) to solve the problems of unbalanced labeled data and insufficient time in the training process. Further, we implement the proposed approach called FuzzGuard and equip it with the state-of-the-art DGF (e.g., AFLGo). Evaluations on 45 real vulnerabilities show that FuzzGuard boosts the fuzzing efficiency of the vanilla AFLGo up to 17.1×. Finally, to understand the key features learned by FuzzGuard, we illustrate their connection with the constraints in the programs.