Input Fuzzing
Input Fuzzing is a dynamic application security testing technique that involves systematically supplying malformed, unexpected, or randomized input data to an application to discover input validation vulnerabilities by observing its behavior under invalid or edge-case conditions.
59
Publications
5.4K
Citations
206
Authors
87
Institutions
Publications per year
2017–2026
47
59
ContractFuzzer: fuzzing smart contracts for vulnerability detection
Bo Jiang, Ye Liu, W. K. Chan · 2018 · 798 citations · Full text
Learn&Fuzz: Machine learning for input fuzzing
Patrice Godefroid, Hila Peleg, Rishabh Singh · 2017 · 319 citations
REDQUEEN: Fuzzing with Input-to-State Correspondence
Cornelius Aschermann, Sergej Schumilo, Tim Blazytko et al. · 2019 · 314 citations · Full text
1–5 of 59
206
University of Illinois Urbana-Champaign, United States
4 publications · 447 citations
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3
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3
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3
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2
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1–5 of 206
87
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
6 publications
506 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
5 publications
1.1K citations
University of Virginia
Charlottesville, United States
2 h-index
6 publications
93 citations
Laboratoire d'Informatique de Grenoble
Grenoble, France
2 h-index
3 publications
242 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
166 citations

Peking University
Beijing, China
2 h-index
6 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
2 h-index
5 publications
433 citations
Stanford University
Stanford, United States
2 h-index
6 publications
540 citations
1–8 of 87
Repository · 6 publications · 134 citations
5
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TSE · 2 publications · 86 citations
2
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Conference · 1 publications · 14 citations
1
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1 publications · 21 citations
1
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SEN · 1 publications · 22 citations
1
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1–5