Wed 6 Dec 2023 17:00 - 17:15 at Golden Gate C1 - Fuzzing Chair(s): Shaukat Ali

Like many software applications, data manipulation functionalities (DMFs) are prevalent in Android apps, which perform the common CRUD operations (create, read, update, delete) to handle app-specific data. Thus, ensuring the correctness of these DMFs is fundamentally important for many core app functionalities. However, the bugs related to DMFs (named as data manipulation errors, DMEs), especially those non-crashing logic ones, are prevalent but difficult to find. To this end, inspired by property-based testing, we introduce a property-based fuzzing approach to effectively finding DMEs in Android apps. Our key idea is that, given some type of app data of interest, we randomly interleave its relevant DMFs and other possible events to explore diverse app states for thorough validation. Specifically, our approach characterizes DMFs in (data) model-based properties and leverage the consistency between the data model and the UI layouts as the handler to do property checking. The properties of DMFs are specified by human according to specific app features. To support the application of our approach, we implemented an automated GUI testing tool, PBFDroid. We evaluated PBFDroid on 20 real-world Android apps, and successfully found 30 unique and previously unknown bugs in 18 apps. Out of the 30 bugs, 29 of which are DMEs (22 are non-crashing logic bugs, and 7 are crash ones). To date, 19 have been confirmed and 9 have already been fixed. Many of these bugs are non-trivial and lead to different types of app failures. Our further evaluation confirms that none of the 22 non-crashing DMEs can be found by the state-of-the-art techniques. In addition, a user study shows that the manual cost of specifying the DMF properties with the assistance of our tool is acceptable. Overall, given accurate DMF properties, our approach can automatically find DMEs without any false positives.

Wed 6 Dec

Displayed time zone: Pacific Time (US & Canada) change

16:00 - 18:00
FuzzingResearch Papers at Golden Gate C1
Chair(s): Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University
16:00
15m
Talk
Enhancing Coverage-guided Fuzzing via Phantom Program
Research Papers
Mingyuan Wu Southern University of Science and Technology and the University of Hong Kong, Kunqiu Chen Southern University of Science and Technology, Qi Luo Southern University of Science and Technology, Jiahong Xiang Southern University of Science and Technology, Ji Qi The University of Hong Kong, Junjie Chen Tianjin University, Heming Cui University of Hong Kong, Yuqun Zhang Southern University of Science and Technology
Media Attached
16:15
15m
Talk
Co-Dependence Aware Fuzzing for Dataflow-based Big Data Analytics
Research Papers
Ahmad Humayun Virginia Tech, Miryung Kim University of California at Los Angeles, USA, Muhammad Ali Gulzar Virginia Tech
Pre-print Media Attached
16:30
15m
Talk
SJFuzz: Seed & Mutator Scheduling for JVM Fuzzing
Research Papers
Mingyuan Wu Southern University of Science and Technology and the University of Hong Kong, Yicheng Ouyang University of Illinois at Urbana-Champaign, Minghai Lu Southern University of Science and Technology, Junjie Chen Tianjin University, Yingquan Zhao College of Intelligence and Computing, Tianjin University, Heming Cui University of Hong Kong, Guowei Yang University of Queensland, Yuqun Zhang Southern University of Science and Technology
Media Attached
16:45
15m
Talk
Metamong: Detecting Render-update Bugs in Web Browsers through Fuzzing
Research Papers
Suhwan Song Seoul National University, South Korea, Byoungyoung Lee Seoul National University, South Korea
Media Attached
17:00
15m
Talk
Property-based Fuzzing for Finding Data Manipulation Errors in Android Apps
Research Papers
Jingling Sun East China Normal University, Ting Su East China Normal University, Jiayi Jiang East China Normal University, Jue Wang Nanjing University, Geguang Pu East China Normal University, Zhendong Su ETH Zurich
Media Attached
17:15
15m
Talk
Leveraging Hardware Probes and Optimizations for Accelerating Fuzz Testing of Heterogeneous Applications
Research Papers
Jiyuan Wang University of California at Los Angeles, Qian Zhang University of California, Riverside, Hongbo Rong Intel Labs, Guoqing Harry Xu University of California at Los Angeles, Miryung Kim University of California at Los Angeles, USA
Pre-print Media Attached
17:30
15m
Talk
NaNofuzz: A Usable Tool for Automatic Test Generation
Research Papers
Matthew C. Davis Carnegie Mellon University, Sangheon Choi Rose-Hulman Institute of Technology, Sam Estep Carnegie Mellon University, Brad A. Myers Carnegie Mellon University, Joshua Sunshine Carnegie Mellon University
Link to publication DOI Media Attached
17:45
15m
Talk
[Remote] A Generative and Mutational Approach for Synthesizing Bug-exposing Test Cases to Guide Compiler Fuzzing
Research Papers
Guixin Ye Northwest University, Tianmin Hu Northwest University, Zhanyong Tang Northwest University, Zhenye Fan Northwest University, Shin Hwei Tan Concordia University, Bo Zhang Tencent Security Platform Department, Wenxiang Qian Tencent Security Platform Department, Zheng Wang University of Leeds, UK
Media Attached