Fuzzing: a survey
Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements i...
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| Vydáno v: | Cybersecurity (Singapore) Ročník 1; číslo 1; s. 1 - 13 |
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| Jazyk: | angličtina |
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Singapore
Springer Singapore
05.06.2018
Springer Nature B.V SpringerOpen |
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| ISSN: | 2523-3246, 2523-3246 |
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| Abstract | Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions. |
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| AbstractList | Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions. Abstract Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions. |
| ArticleNumber | 6 |
| Author | Li, Jun Zhang, Chao Zhao, Bodong |
| Author_xml | – sequence: 1 givenname: Jun surname: Li fullname: Li, Jun organization: Tsinghua University – sequence: 2 givenname: Bodong surname: Zhao fullname: Zhao, Bodong organization: Tsinghua University – sequence: 3 givenname: Chao surname: Zhang fullname: Zhang, Chao email: chaoz@tsinghua.edu.cn organization: Tsinghua University |
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| Cites_doi | 10.1145/3133956.3134073 10.14722/ndss.2017.23404 10.1145/3133956.3134103 10.1049/sej.1995.0010 10.1145/2090147.2094081 10.1109/SP.2010.37 10.1016/S1353-4858(08)70095-3 10.1109/SP.2012.31 10.1145/3133956.3134046 10.1093/comjnl/bxm021 10.1145/360248.360252 10.1109/SP.2017.23 10.1145/3133956.3134020 10.1109/MINES.2012.202 10.1145/1791194.1791203 10.1145/3106237.3106258 10.1145/2976749.2978428 10.1007/11836810_25 10.14722/ndss.2016.23368 10.17487/rfc2828 |
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| Keywords | Fuzzing Vulnerability discovery Coverage-based fuzzing Software security |
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| References_xml | – reference: Nichols, N, Raugas M, Jasper R, Hilliard N (2017) Faster fuzzing: Reinitialization with deep neural models. arXiv preprint arXiv:1711.02807. – reference: The Clang Team (2017) Leaksanitizer. https://clang.llvm.org/docs/LeakSanitizer.html. Accessed 25 Dec 2017. – reference: Petsios, T, Zhao J, Keromytis AD, Jana S (2017) Slowfuzz: Automated domain-independent detection of algorithmic complexity vulnerabilities In: Proceeding CCS ’17 Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2155–2168.. ACM, New York. https://doi.org/10.1145/3133956.3134073. – reference: Stephenfewer (2016) Grinder. https://github.com/stephenfewer/grinder. Accessed 25 Dec 2017. – reference: Rajpal, M, Blum W, Singh R (2017) Not all bytes are equal: Neural byte sieve for fuzzing. arXiv preprint arXiv:1711.04596. – reference: The Clang Team (2017) Dataflowsanitizer. https://clang.llvm.org/docs/DataFlowSanitizerDesign.html. 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