Brian Kondracki (Stony Brook University), Babak Amin Azad (Stony Brook University), Najmeh Miramirkhani (Stony Brook University), Nick Nikiforakis (Stony Brook University)
Malware sandboxes have long been a valuable tool for detecting and analyzing malicious software. The proliferation of mobile devices and, subsequently, mobile applications, has led to a surge in the development and use of mobile device sandboxes to ensure the integrity of application marketplaces. In turn, to evade these sandboxes, malware has evolved to suspend its malicious activity when it is executed in a sandbox environment. Sophisticated malware sandboxes attempt to prevent sandbox detection by patching runtime properties indicative of malware- analysis systems.
In this paper, we propose a set of novel mobile-sandbox- evasion techniques that we collectively refer to as “environment- aware” sandbox detection. We explore the distribution of artifacts extracted from readily available APIs in order to distinguish real user devices from sandboxes. To that end, we identify Android APIs that can be used to extract environment-related features, such as artifacts of user configurations (e.g. screen brightness), population of files on the device (e.g. number of photos and songs), and hardware sensors (e.g. presence of a step counter).
By collecting ground truth data from real users and Android sandboxes, we show that attackers can straightforwardly build a classifier capable of differentiating between real Android devices and well-known mobile sandboxes with 98.54% accuracy. More- over, to demonstrate the inefficacy of patching APIs in sandbox environments individually, we focus on feature inconsistencies between the claimed manufacturer of a sandbox (Samsung, LG, etc.) and real devices from these manufacturers. Our findings emphasize the difficulty of creating robust sandbox environments regardless of their underlying platform being an emulated en- vironment, or an actual mobile device. Most importantly, our work signifies the lack of protection against “environment-aware” sandbox detection in state-of-the-art mobile sandboxes which can be readily abused by mobile malware to evade detection and increase their lifespan.