StFuzzer: Contribution-Aware Coverage-Guided Fuzzing for Smart Devices

被引:2
|
作者
Yang, Jiageng [1 ]
Zhang, Xinguo [1 ]
Lu, Hui [1 ]
Shafiq, Muhammad [1 ]
Tian, Zhihong [1 ]
机构
[1] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
35;
D O I
10.1155/2021/1987844
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The root cause of the insecurity for smart devices is the potential vulnerabilities in smart devices. There are many approaches to find the potential bugs in smart devices. Fuzzing is the most effective vulnerability finding technique, especially the coverage-guided fuzzing. The coverage-guided fuzzing identifies the high-quality seeds according to the corresponding code coverage triggered by these seeds. Existing coverage-guided fuzzers consider that the higher the code coverage of seeds, the greater the probability of triggering potential bugs. However, in real-world applications running on smart devices or the operation system of the smart device, the logic of these programs is very complex. Basic blocks of these programs play a different role in the process of application exploration. This observation is ignored by existing seed selection strategies, which reduces the efficiency of bug discovery on smart devices. In this paper, we propose a contribution-aware coverage-guided fuzzing, which estimates the contributions of basic blocks for the process of smart device exploration. According to the control flow of the target on any smart device and the runtime information during the fuzzing process, we propose the static contribution of a basic block and the dynamic contribution built on the execution frequency of each block. The contribution-aware optimization approach does not require any prior knowledge of the target device, which ensures our optimization adapting gray-box fuzzing and white-box fuzzing. We designed and implemented a contribution-aware coverage-guided fuzzer for smart devices, called StFuzzer. We evaluated StFuzzer on four real-world applications that are often applied on smart devices to demonstrate the efficiency of our contribution-aware optimization. The result of our trials shows that the contribution-aware approach significantly improves the capability of bug discovery and obtains better execution speed than state-of-the-art fuzzers.
引用
收藏
页数:15
相关论文
共 45 条
  • [1] CoLaFUZE: Coverage-Guided and Layout-Aware Fuzzing for Android Drivers
    Mu, Tianshi
    Zhang, Huabing
    Wang, Jian
    Li, Huijuan
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2021, E104D (11): : 1902 - 1912
  • [2] CoLaFUZE: Coverage-guided and layout-aware fuzzing for android drivers
    Tianshi M.U.
    Zhang H.
    Wang J.
    Huijuan L.I.
    [J]. IEICE Transactions on Information and Systems, 2021, 104 (11) : 1902 - 1912
  • [3] EPF: An Evolutionary, Protocol-Aware, and Coverage-Guided Network Fuzzing Framework
    Helmke, Rene
    Winter, Eugen
    Rademacher, Michael
    [J]. 2021 18TH INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST), 2021,
  • [4] SPINALFUZZ: Coverage-Guided Fuzzing for SpinalHDL Designs
    Ruep, Katharina
    Grosse, Daniel
    [J]. 2022 IEEE EUROPEAN TEST SYMPOSIUM (ETS 2022), 2022,
  • [5] REFuzz: A Remedy for Saturation in Coverage-Guided Fuzzing
    Lyu, Qian
    Zhang, Dalin
    Da, Rihan
    Zhang, Hailong
    [J]. ELECTRONICS, 2021, 10 (16)
  • [6] Coverage-guided Fuzzing for Feedforward Neural Networks
    Xie, Xiaofei
    Chen, Hongxu
    Li, Yi
    Ma, Lei
    Liu, Yang
    Zhao, Jianjun
    [J]. 34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2019), 2019, : 1162 - 1165
  • [7] NDFuzz: a non-intrusive coverage-guided fuzzing framework for virtualized network devices
    Zhang, Yu
    Zhong, Nanyu
    You, Wei
    Zou, Yanyan
    Jian, Kunpeng
    Xu, Jiahuan
    Sun, Jian
    Liu, Baoxu
    Huo, Wei
    [J]. CYBERSECURITY, 2022, 5 (01)
  • [8] RIFF: Reduced Instruction Footprint for Coverage-Guided Fuzzing
    Wang, Mingzhe
    Liang, Jie
    Zhou, Chijin
    Jiang, Yu
    Wang, Rui
    Sun, Chengnian
    Sun, Jiaguang
    [J]. PROCEEDINGS OF THE 2021 USENIX ANNUAL TECHNICAL CONFERENCE, 2021, : 147 - 159
  • [9] Enhancing Coverage-Guided Fuzzing via Phantom Program
    Wu, Mingyuan
    Chen, Kunqiu
    Luo, Qi
    Xiang, Jiahong
    Qi, Ji
    Chen, Junjie
    Cui, Heming
    Zhang, Yuqun
    [J]. PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, 2023, : 1037 - 1049
  • [10] TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
    Odena, Augustus
    Olsson, Catherine
    Andersen, David G.
    Goodfellow, Ian
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97