Multiple Targets Directed Greybox Fuzzing

Directed greybox fuzzing (DGF) can quickly discover or reproduce bugs in programs by seeking to reach a program location explore some locations order. However, due their static stage division and coarse-grained energy scheduling, prior DGF tools perform poorly when facing multiple target (targets for short). In this paper, we present targets directed which aims campaign. Specifically, propose novel strategy adaptively coordinate exploration exploitation stages, scheduling considering more relations between seeds locations. We implement our approaches tool called LeoFuzz evaluate it on crash reproduction, true positives verification, vulnerability exposure real-world programs. Experimental results show that outperforms six state-of-the-art fuzzers, i.e. QYSM, AFLGo, Lolly, Berry, Beacon WindRanger terms of effectiveness efficiency. Moreover, has detected 23 new vulnerabilities programs, 12 them have been assigned CVE IDs.