Fuzzing: Brute Force Vulnerability Discovery

By M. Sutton, Adam R. Greene, P. Amini. 2007

661 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4db2b87478c87c677135e802dbeccc8e5e384aed

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Citations in the corpus

RESTler: Stateful REST API Fuzzing (2019, methodology): “Fuzzing [39] means automatic ∗ The work of this author was mostly done at Microsoft Research. test generation and execution with the goal of ﬁnding security vulnerabilities.”
TaintScope: A Checksum-Aware Directed Fuzzing Tool for Automatic Software Vulnerability Detection (2010, background): “Most of these severe vulnerabilities have been confirmed by Secunia and oCERT, and assigned CVE identifiers (such as CVE-20091882, CVE-2009-2688).”
Prospex: Protocol Specification Extraction (2009, methodology): “To achieve better code coverage of the tested application, advanced fuzzers (such as Peach [19]) generate test data based on the grammar of the file formats or network messages understood by the target application (we refer the reader to [ 34 ] for a recent overview of fuzzing techniques).”
Grammar-based whitebox fuzzing (2008, background): “Blackbox fuzzing sometimes uses grammars to generate the well-formed inputs, as well as to encode application-specific knowledge and test heuristics for guiding the generation of input variants [1, 37].”
Fuzz4ALL: Universal Fuzzing with Large Language Models (2023, background): “Fuzz testing [69, 84], also known as fuzzing, is an automated testing approach for generating inputs designed to expose unexpected behaviors, e.g., crashes, of a system under test (SUT).”
Scenic: a language for scenario specification and scene generation (2018, methodology): “g only a simulator. Model-Based Test Generation. Techniques using a model to guide test generation have long existed [4]. A popular approach is to provide example tests, as in mutational fuzz testing [55] and example-based scene synthesis [12]. While these methods are easy to use, they do not provid”
Deep Text Classification Can be Fooled (2017, methodology): “In such cases, we borrow the idea of the fuzzing technique [Sutton et al., 2007] to implement a blind test to locate HTPs and HSPs.”
Robustness Testing of Autonomy Software (2017, background): “Fuzz testing efficiency can been improved by constraining the input space or by introducing statefulness [40].”
Learn&Fuzz: Machine learning for input fuzzing (2017, methodology): “, Peach1 and SPIKE2, among many others [30].”
The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “Due to the above reasons, we believe it is prime time to consolidate and distill the large amount of progress in fuzzing, many of which happened after the three trade-books on the subject were published in 2007–2008 [79], [203], [205].”
Automatic creation of SQL Injection and cross-site scripting attacks (2009, background): “Black-box test generation does not take advantage of the application’s internals [27], while previous white-box techniques have not been shown to discover unknown vulnerabilities [30].”