Multiple Targets Directed Greybox Fuzzing

TL;DR

This paper introduces LeoFuzz, a novel directed greybox fuzzing approach that effectively targets multiple program locations simultaneously, improving bug discovery efficiency and outperforming existing tools.

Contribution

The paper presents a new adaptive coordination and energy scheduling strategy for multi-target directed greybox fuzzing, implemented in LeoFuzz, enhancing effectiveness and efficiency.

Findings

LeoFuzz outperforms six state-of-the-art fuzzers in effectiveness and efficiency.

Detected 23 new vulnerabilities, 11 with CVE IDs.

Improved bug reproduction and vulnerability exposure in real-world programs.

Abstract

Directed greybox fuzzing (DGF) can quickly discover or reproduce bugs in programs by seeking to reach a program location or explore some locations in order. However, due to their static stage division and coarse-grained energy scheduling, prior DGF tools perform poorly when facing multiple target locations (targets for short). In this paper, we present multiple targets directed greybox fuzzing which aims to reach multiple programs locations in a fuzzing campaign. Specifically, we propose a novel strategy to adaptively coordinate exploration and exploitation stages, and a novel energy scheduling strategy by considering more relations between seeds and target locations. We implement our approaches in a tool called LeoFuzz and evaluate it on crash reproduction, true positives verification, and vulnerability exposure in real-world programs. Experimental results show that LeoFuzz outperforms six state-of-the-art fuzzers, i.e., QYSM, AFLGo, Lolly, Berry, Beacon and WindRanger in terms of effectiveness and efficiency. Moreover, LeoFuzz has detected 23 new vulnerabilities in real-world programs, and 11 of them have been assigned CVE IDs.