Leveraging Historical Versions of Android Apps for Efficient and Precise Taint Analysis

Today, computing on various Android devices is pervasive. However, growing security vulnerabilities and attacks in the Android ecosystem constitute various threats through user apps. Taint analysis is a common technique for defending against these threats, yet it su?ers from challenges in attaining practical simultaneous scalability and e?ectiveness. This paper presents a novel approach to fast and precise taint checking, called incremental taint analysis, by exploiting the evolving nature of Android apps. The analysis narrows down the search space of taint checking from an entire app, as conventionally addressed, to the parts of the program that are di?erent from its previous versions. This technique improves the overall efciency of checking multiple versions of the app as it evolves. We have implemented the techniques as a tool prototype, EvoTaint, and evaluated our analysis by applying it to real-world evolving Android apps. Our preliminary results show that the incremental approach largely reduced the cost of taint analysis, by 78.6% on average, yet without sacrifcing the analysis e?ectiveness, relative to a representative precise taint analysis as the baseline.