Automatic Uncovering of Hidden Behaviors from Input Validation in Mobile Apps

Zhao, Qingchuan and Zuo, Chaoshun and Brendan, Dolan-Gavitt and Pellegrino, Giancarlo and Lin, Zhiqiang
(2020) Automatic Uncovering of Hidden Behaviors from Input Validation in Mobile Apps.
In: IEEE Symposium on Security and Privacy.
Conference: S&P - IEEE Symposium on Security and Privacy

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Abstract

Mobile applications (apps) have exploded in popularity, with billions of smartphone users using millions of apps available through markets such as the Google Play Store or the Apple App Store. While these apps have rich and useful functionality that is publicly exposed to end users, they also contain hidden behaviors that are not disclosed, such as backdoors and blacklists designed to block unwanted content. In this paper, we show that the input validation behavior—the way the mobile apps process and respond to data entered by users—can serve as a powerful tool for uncovering such hidden functionality. We therefore have developed a tool, InputScope, that automatically detects both the execution context of user input validation and also the content involved in the validation, to automatically expose the secrets of interest. We have tested InputScope with over 150,000 mobile apps, including popular apps from major app stores and pre- installed apps shipped with the phone, and found 12,706 mobile apps with backdoor secrets and 4,028 mobile apps containing blacklist secrets.

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