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Multiple Targets Directed Greybox Fuzzing
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AI
DOI：10.1109/TDSC.2023.3253120.png)
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Abstract
En 中文
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 12 of them have been assigned CVE IDs.
Keywords:
Computer bugs
Fuzzing
Codes
Optimal scheduling
Harmonic analysis
Testing
Switches
Crash reproduction
directed greybox fuzzing
true positives verification
vulnerability exposure
Journal
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7.5
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2.4K
Citations:
9.6K