Hawkeye: Towards a Desired Directed Grey-box Fuzzer

By Hongxu Chen, Yinxing Xue, Yuekang Li, Bihuan Chen, Xiaofei Xie, Xiuheng Wu, Yang Liu. Conference on Computer and Communications Security, 2018

343 citations recorded by the source corpus.

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Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks (2019, methodology): “Beside the classic approaches such as static analysis [1, 2], dynamic analysis [3–5] and symbolic execution, a number of advances have been made in applying machine learning as a complementary approach.”
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].”
The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “FairFuzz [141] H# ✓ ✓ ✓† H#† ✓ ✓ ✓ go-fuzz [225] H# ✓ ✓ ✓ ✓ ✓ ✓ H# ✓ Hawkeye [56] H# ✓ ✓ ✓ ✓ H# ✓ ✓ honggfuzz [213] H# ✓ ✓ ✓ kAFL [191] H# ✓ ✓ LibFuzzer [9] H# ✓ ✓ ✓ ✓ ✓ ✓ MagicFuzzer [50] H# ✓ ✓ ✓ Nautilus [25] H# ✓ ✓ ✓ H# ✓ ✓ RaceFuzzer [197] H# ✓ ✓ ✓ RedQueen [26] H# ✓ ✓ H# ✓”