Mobile apps, an essential technology in today’s world, should provide equal access to all, including 15% of the world population with disabilities. Assistive Technologies (AT), with the help of Accessibility APIs, provide alternative ways of interaction with apps for disabled users who cannot see or touch the screen. Prior studies have shown that mobile apps are prone to the \textit{under-access} problem, i.e., a condition in which functionalities in an app are not accessible to disabled users, even with the use of ATs. We study the dual of this problem, called the \textit{over-access} problem, and defined as a condition in which an AT can be used to gain access to functionalities in an app that are inaccessible otherwise. Over-access has severe security and privacy implications, allowing one to bypass protected functionalities using ATs, e.g., using VoiceOver to read notes on a locked phone. Over-access also degrades the accessibility of apps by presenting to disabled users information that is actually not intended to be available on a screen, thereby confusing and hindering their ability to effectively navigate. In this work, we first empirically study overly accessible elements in Android apps and define a set of conditions that can result in over-access problem. We then present OverSight, an automated framework that leverages these conditions to detect overly accessible elements and verifies their accessibility dynamically using an AT. Our empirical evaluation of OverSight on real-world apps demonstrates OverSight’s effectiveness in detecting previously unknown security threats, workflow violations, and accessibility issues.

Forough Mehralian

University of California at Irvine

United States

Navid Salehnamadi

University of California at Irvine

United States

Syed Fatiul Huq

University of California, Irvine

Sam Malek

University of California at Irvine, USA

United States