As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android OS which enables JPF to dynamically explore the concrete state spaces of the tested apps; we construct programs to generate user interaction and environmental input so as to drive the dynamic execution of the apps; and we introduce Android specific reduction techniques to help alleviate the state space explosion. DroidPF focuses on common security vulnerabilities in Android apps including sensitive data leakage involving a non-trivial flow- and context-sensitive taint-style analysis. DroidPF has been evaluated with 131 apps, which include real-world apps, third-party libraries, malware samples and benchmarks for evaluating app analysis techniques like ours. DroidPF precisely identifies nearly all of the previously known security issues and nine previously unreported vulnerabilities/bugs.

Current dynamic analysis tools for Android applications do not get good code coverage since they can only explore a subset of the behaviors of the applications and do not have full control over the environment in which they execute. In this work we use model checking to systematically explore application paths while reducing the analysis size using state matching and backtracking. In particular, we extend the Java PathFinder (JPF) model checking environment for Android. We describe the difficulties one needs to overcome to make this a reality as well as our current approaches to handling these issues. We obtain significantly higher coverage using shorter event sequences on a representative sample of Android apps, when compared to Dynodroid and Sapienz, the current state-of-the-art dynamic analysis tools for Android applications.