The progress, challenges, and perspectives of directed greybox fuzzing

Author:

Wang Pengfei1ORCID,Zhou Xu1,Yue Tai1,Lin Peihong1,Liu Yingying1,Lu Kai1

Affiliation:

1. College of Computer National University of Defense Technology Changsha China

Abstract

SummaryGreybox fuzzing is a scalable and practical approach for software testing. Most greybox fuzzing tools are coverage‐guided as reaching high code coverage is more likely to find bugs. However, since most covered codes may not contain bugs, blindly extending code coverage is less efficient, especially for corner cases. Unlike coverage‐guided greybox fuzzing which increases code coverage in an undirected manner, directed greybox fuzzing (DGF) spends most of its time allocation on reaching specific targets (e.g. the bug‐prone zone) without wasting resources stressing unrelated parts. Thus, DGF is particularly suitable for scenarios such as patch testing, bug reproduction, and special bug detection. For now, DGF has become an active research area. However, DGF has general limitations and challenges that are worth further studying. Based on the investigation of 42 state‐of‐the‐art fuzzers that are closely related to DGF, we conducted the first in‐depth study to summarize the empirical evidence on the research progress of DGF. This paper studies DGF from a broader view, which takes into account not only the location‐directed type that targets specific code parts but also the behavior‐directed type that aims to expose abnormal program behaviors. By analyzing the benefits and limitations of DGF research, we try to identify gaps in current research, meanwhile, reveal new research opportunities and suggest areas for further investigation.

Funder

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

Wiley

Subject

Safety, Risk, Reliability and Quality,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

2. WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer;Electronics;2024-05-28

3. A Gradient-guided Fuzzing Approach to Recover More Complete Control Flow Graph of Binary;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21

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