Analysis of Android hybrid applications and other fun with WALA

Julian Dolby

Hybrid apps help developers build multiple apps for different platforms with less duplicated effort, by providing platform-specific functionality via native code and user interactions via JavaScript code. However, most hybrid apps are developed in multiple programming languages with different semantics, complicating programming. Moreover, untrusted JavaScript code may access device-specific features via native code, exposing hybrid apps to attacks. Unfortunately, there are no existing tools to detect such vulnerabilities. In this paper, we present HybriDroid, the first static analysis framework for Android hybrid apps. First, we investigate the semantics of interoperation of Android Java and JavaScript. Then, we design and implement a static analysis framework that analyzes inter-communication between Android Java and JavaScript. We demonstrate HybriDroid with a bug detector that identifies programmer errors due to the hybrid semantics, and a taint analyzer that finds information leaks cross language boundaries. Our empirical evaluation shows that the tools are practically usable in that they found previously uncovered bugs in real-world Android hybrid apps and possible information leaks via a widely-used advertising platform.

The bulk of this presentation will focus on ASE 2016 work on analysis of hybrid apps (1), a blend of per-platform native code and portable JavaScript. I will also briefly discuss two other recent projects involving WALA: ASE 2015 work on a practically tunable static analysis framework for large-scale JavaScript applications (2), and ISSTA 2015 work on scalable and precise taint analysis for Android (3).


  1. Sungho Lee, Julian Dolby, Sukyoung Ryu: HybriDroid: static analysis framework for Android hybrid applications. ASE 2016: 250-261
  2. Yoonseok Ko, Hongki Lee, Julian Dolby, Sukyoung Ryu: Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T). ASE 2015: 541-551
  3. Wei Huang, Yao Dong, Ana Milanova, Julian Dolby: Scalable and precise taint analysis for Android. ISSTA 2015: 106-117