20 citations · 54 across the 6 of their papers we have counts for
6 papers
FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
Yunlong Lyu, Peng Chen, Fengyi Wu +3
Library fuzzing is essential for hardening the software supply chain, but adopting it at scale remains expensive. Practitioners still spend substantial effort on environment setup,…
Prompt Fuzzing for Fuzz Driver Generation
Yunlong Lyu, Yuxuan Xie, Peng Chen +1
Crafting high-quality fuzz drivers not only is time-consuming but also requires a deep understanding of the library. However, the state-of-the-art automatic fuzz driver generation…
HOPPER: Interpretative Fuzzing for Libraries
Peng Chen, Yuxuan Xie, Yunlong Lyu +2
Despite the fact that the state-of-the-art fuzzers can generate inputs efficiently, existing fuzz drivers still cannot adequately cover entries in libraries. Most of these fuzz dri…
ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting
Yuliang Liu, Chunhua Shen, Lianwen Jin +4
End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentar…
Matryoshka: Fuzzing Deeply Nested Branches
Peng Chen, Jianzhong Liu, Hao Chen
Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to approaches for solving individual path constraints. However, they ha…
Angora: Efficient Fuzzing by Principled Search
Peng Chen, Hao Chen
Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution p…