To reduce the attack surface from app source code, massive tools focus on detecting vulnerabilities in Android apps. However, some obvious weaknesses have been highlighted in the previous studies. For example, (1) most of the available tools such as AndroBugs, MobSF, Qark, and Super use pattern-based methods to detect vulnerabilities. Although they are effective in detecting some types, a large number of false positives would be introduced, which inevitably increases the patching overhead for app developers. (2) Similarly, the static taint analysis tools such as FlowDroid and IccTA present hundreds of vulnerability candidates of data leakage instead of confirmed vulnerabilities. (3) Last but not least, a relatively complete vulnerability taxonomy is missing, which would introduce a lot of false negatives. In this paper, based on our prior knowledge in this research domain, we empirically propose a vulnerability taxonomy as the baseline and then extend AUSERA by augmenting the detection capability to 50 vulnerability types. Meanwhile, a new benchmark dataset including all these 50 vulnerabilities is constructed to demonstrate the effectiveness of AUSERA. The tool and datasets are available at: https://github.com/tjusenchen/AUSERA and the demonstration video can be found at: https://youtu.be/UCiGwVaFPpY.

Sen Chen

Tianjin University

China

Yuxin Zhang

Tianjin University

Lingling Fan

Nankai University

Jiaming Li

Tianjin University

Yang Liu

Nanyang Technological University