1dFuzz: Reproduce 1-Day Vulnerabilities with Directed Differential Fuzzing

Yang, Songtao; He, Yubo; Chen, Kaixiang; Ma, Zheyu; Luo, Xiapu; Xie, Yaoqin; Chen, Jianjun; Zhang, Chao

doi:10.1145/3597926.3598102

Cited by 5 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, some inadequacies persist [13,[21][22][23][24][25]. Firstly, while some fuzzers provide more accurate definitions for distance metrics, they neglect the fact that distinct branches may have varying arrival probabilities in certain situations.…”

Section: Introductionmentioning

confidence: 99%

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

Zeng,

Xiong,

et al. 2024

Electronics

View full text Add to dashboard Cite

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.

show abstract