Directed Greybox Fuzzing

By Marcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen, Abhik Roychoudhury. Conference on Computer and Communications Security, 2017

865 citations recorded by the source corpus.

Subfields: Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/824c05ae636ed07823b3a71ca1b7abf32676e437

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Citations in the corpus

Superion: Grammar-Aware Greybox Fuzzing (2018, positive): “Another thread of work focuses on improving the efﬁciency of fuzzing, e.g., seed selection [56], seed scheduling [9, 66], parameter tuning [10, 35], directed fuzzing [8, 12, 13] to reproduce crashes or assess potential bugs found by vulnerable code matching [11, 68], and operating primitives [67].”
CollAFL: Path Sensitive Fuzzing (2018, background): “AFLgo [10] prioritizes seeds closer to target locations, requiring an accurate path coverage information too.”
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (2018, background): “These have been expanded in various ways in order to make them faster or to increase the extent to which certain parts of the code can be targeted [5, 6].”
Evaluating Fuzz Testing (2018, methodology): “For example, AFLGo [5] does not aim to increase coverage globally, but rather aims to focus on particular, possibly error-prone points in the program.”
Fuzzing: A Survey for Roadmap (2022, methodology): “) block transition general ✓ 2017 AFLGo [22] SA(seed.”
T-Fuzz: Fuzzing by Program Transformation (2018, background): “[29] propose empirical seed selection and fuzzing scheduling algorithms to find more bugs in a given limited computation budget, in AFLFast [30] and AFLGo [31] Böhme et al.”
The Art, Science, and Engineering of Fuzzing: A Survey (2018, neutral): “…(cid:8) [94] AFLFast (cid:8) [37] classfuzz (cid:8) [59] GRR[211] Skyﬁre (cid:8) [217] GLADE (cid:8) [30] VUzzer (cid:8) [176] AFLGo (cid:8) [36] Hawkeye (cid:8) [53] Angora (cid:8) [56] CollAFL (cid:8) [83] FairFuzz (cid:8) [136] REDQUEEN (cid:8) [23] NAUTILUS (cid:8) [22] syzkaller[216]…”
AutoCodeRover: Autonomous Program Improvement (2024, neutral): “AI-based software engineering is currently a topic of study among researchers, innovators, and entrepreneurs.”
FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (2017, methodology): “The success of one particular fuzz tester, American Fuzzy Lop (or simply AFL) [43], has gained attention both in practice and in the research community [8, 9, 30, 39].”
AFLNET: A Greybox Fuzzer for Network Protocols (2020, background): “, towards dangerous [6] or uncovered program statements [5].”