To reduce the attack surface from app source code, massive tools focus on detecting security 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 security vulnerabilities. Although they are effective in detecting some types of vulnerabilities, a large number of false positives would be introduced, which inevitably increases the patching overhead for app developers. (2) Similarly, 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 security vulnerability types. Meanwhile, a new benchmark dataset including all these 50 vulnerability types 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.

@inproceedings{paperbot7199,
  title = {AUSERA: Automated Security Vulnerability Detection for Android Apps},
  author = {Sen Chen and Yuxin Zhang and Lingling Fan and Jiaming Li and Yang Liu},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3559524}
}