T-Fuzz: Fuzzing by Program Transformation
By Hui Peng, Yan Shoshitaishvili, Mathias Payer. IEEE Symposium on Security and Privacy, 2018
333 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/613552e5d9396eb0053f679ede711d5eecfe1730
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Petar Tsankov
- Taesoo Kim
- Meng Xu
- Seyit Ahmet Camtepe
- Thorsten Holz
- Michael W. Hicks
- Sheng Wen
- Martin T. Vechev
In Software
- Petar Tsankov
- Thorsten Holz
- Robert Gawlik
- Michael W. Hicks
- Sheng Wen
- Martin T. Vechev
- Seyit Ahmet Camtepe
- Xiaogang Zhu
Citations in the corpus
- Fuzzing: A Survey for Roadmap (2022, background): “Whitebox fuzzing usually utilizes concolic execution (i.e., dynamic symbolic execution) to analyze target programs [71, 81, 150, 177, 204].”
- AFL++ : Combining Incremental Steps of Fuzzing Research (2020, unknown): “Recently, REDQUEEN [5], based on KAFL [36], explored the possibility to bypass hard comparisons and checksum checks, like other previous works in literature [35] [12] [33] [44], but without the use of expensive techniques like taint tracking [46] or symbolic execution [6, 37].”
- The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “Angora [177] improves the fitness criteria of AFL by considering the calling context of each branch taken.”
- QSYM : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing (2018, background): “In addition, a recent study, T-Fuzz [40], transforms a program itself to cover more interesting code paths, which could be combined with QSYM to remove unsolvable constraints from the program.”
- Learning to Fuzz from Symbolic Execution with Application to Smart Contracts (2019, background): “Random fuzzers often get stuck with ineffective inputs that fail to exercise deep program paths and may thus miss bugs [22, 45, 48, 53].”
- REDQUEEN: Fuzzing with Input-to-State Correspondence (2019, methodology): “We started with targets commonly used in other papers, namely libtiff (tiff2ps) [31], [34], [40], imagemagick [34], [40] and jhead [16], [28], each in the most recent version of Ubuntu (16.”
- Evaluating Fuzz Testing (2018, methodology): “T-Fuzz [39] will transform the program to remove checks on the input that prevent new code from being reached.”