Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

Intents are the primary message-passing mechanism on Android, used for both communication between intra-app and inter-app components. Intents go across the trust boundary of applications and can break the security isolation between them. Due to their shared API with intra-app communication, apps may unintentionally expose functionality leading to important security bugs. MALintent is an open-source fuzzing framework that uses novel coverage instrumentation techniques and customizable bug oracles to find security issues in Android Intent handlers. MALintent is the first Intent fuzzer that applies greybox fuzzing on compiled closed-source Android applications. We demonstrate techniques widely compatible with many versions of Android and our bug oracles were able to find several crashes, vulnerabilities with privacy implications, and memory-safety issues in the top-downloaded Android applications on the Google Play store.

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Qiyang Song (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Heqing Huang (Institute of Information Engineering, Chinese Academy of Sciences), Xiaoqi Jia (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Yuanbo Xie (Institute of Information…

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BARBIE: Robust Backdoor Detection Based on Latent Separability

Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University)

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Work-in-Progress: Detecting Browser-in-the-Browser Attacks from Their Behaviors and DOM...

Ryusei Ishikawa, Soramichi Akiyama, and Tetsutaro Uehara (Ritsumeikan University)

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BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS

Yinggang Guo (State Key Laboratory for Novel Software Technology, Nanjing University; University of Minnesota), Zicheng Wang (State Key Laboratory for Novel Software Technology, Nanjing University), Weiheng Bai (University of Minnesota), Qingkai Zeng (State Key Laboratory for Novel Software Technology, Nanjing University), Kangjie Lu (University of Minnesota)

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