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WearFlow: Expanding Information Flow Analysis To Companion Apps in Wear OS

Tileria, Marcos and Blasco, Jorge and Suarez-Tangil, Guillermo (2020) WearFlow: Expanding Information Flow Analysis To Companion Apps in Wear OS. In: The 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), 14-16 October 2020, Virtual event.

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Smartwatches and wearable technology have proliferated in the recent years featured by a seamless integration with a paired smartphone. Many mobile applications now come with a companion app that the mobile OS deploys on the wearable. These execution environments expand the context of mobile applications across more than one device, introducing new security and privacy issues. One such issue is that current information flow analysis techniques can not capture communication between devices. This can lead to undetected privacy leaks when developers use these channels. In this paper, we present WearFlow, a framework that uses static analysis to detect sensitive data flows across mobile and wearable companion apps in Android. WearFlow augments taint analysis capabilities to enable inter-device analysis of apps. WearFlow models proprietary libraries embedded in Google Play Services and instruments the mobile and wearable app to allow for a precise information flow analysis between them. We evaluate WearFlow on a test suite purposely designed to cover different scenarios for the communication Mobile-Wear, which we release as Wear-Bench. We also run WearFlow on 3K+ real-world apps and discover privacy violations in popular apps (10M+ downloads).

Item Type: Conference or Workshop Papers (Paper)
Depositing User: Guillermo Suarez-Tangil
Date Deposited: 10 Nov 2020 10:10
Last Modified: 10 Nov 2020 10:10

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