Abstract-HTML5-based mobile apps become more and more popular, mostly because they are much easier to be ported across different mobile platforms than native apps. HTML5-based apps are implemented using the standard web technologies, including HTML5, JavaScript and CSS; they depend on some middlewares, such as PhoneGap, to interact with the underlying OS.Knowing that JavaScript is subject to code injection attacks, we have conducted a systematic study on HTML5-based mobile apps, trying to evaluate whether it is safe to rely on the web technologies for mobile app development. Our discoveries are quite surprising. We found out that if HTML5-based mobile apps become popular-it seems to go that direction based on the current projection-many of the things that we normally do today may become dangerous, including reading from 2D barcodes, scanning Wi-Fi access points, playing MP4 videos, pairing with Bluetooth devices, etc. This paper describes how HTML5-based apps can become vulnerable, how attackers can exploit their vulnerabilities through a variety of channels, and what damage can be achieved by the attackers. In addition to demonstrating the attacks through example apps, we have studied 186 PhoneGap plugins, used by apps to achieve a variety of functionalities, and we found that 11 are vulnerable. We also found two real HTML5-based apps that are vulnerable to the attacks.

When OS and hypervisor are compromised, mobile devices currently provide a hardware protected mode called Trusted Execution Environment (TEE) to guarantee the confidentiality and integrity of the User Interface (UI). The present TEE UI solutions adopt a self-contained design model, which provides a fully functional UI stack in the TEE, but they fail to manage one critical design principle of TEE: a small Trusted Computing Base (TCB), which should be more easily verified in comparison to a rich OS. The TCB size of the self-contained model is large as a result of the size of an individual UI stack. To reduce the TCB size of the TEE UI solution, we proposed a novel TEE UI design model called delegation model. To be specific, our design reuses the majority of the rich OS UI stack. Unlike the existing UI solutions protecting 3-dimensional UI processing in the TEE, our design protects the UI solely as a 2-dimensional surface and thus reduces the TCB size. Our system, called TruZ-View, allows application developers to use the rich OS UI development environment to develop TEE UI with consistent UI looks across the TEE and the rich OS. We successfully implemented our design on HiKey board. Moreover, we developed several TEE UI use cases to protect the confidentiality and integrity of UI. We performed a thorough security analysis to prove the security of the delegation UI model. Our real-world application evaluation shows that developers can leverage our TEE UI with few changes to the existing app's UI logic.

Uninstalling apps from mobile devices is among the most common user practices on smartphones. It may sound trivial, but the entire process involves multiple system components coordinating to remove the data belonging to the uninstalled app. Despite its frequency and complexity, little has been done to understand the security risks in the app's uninstallation process. In this project, we have conducted the first systematic analysis of Android's data cleanup mechanism during the app's uninstallation process. Our analysis reveals that data residues are pervasive in the system after apps are uninstalled. For each identified data residue instance, we have formulated hypotheses and designed experiments to see whether it can be exploited to compromise the system security. The results are surprising: we have found 12 instances of vulnerabilities caused by data residues. By exploiting them, adversaries can steal user's online-account credentials, access other app's private data, escalate privileges, eavesdrop on user's keystrokes, etc. We call these attacks the data residue attacks.To evaluate the real-world impact of the attacks, we have conducted an analysis on the top 100 apps in each of the 27 categories from GooglePlay. The result shows that a large portion of the apps can be the target of the data residue attacks. We have further evaluated the effectiveness of popular app markets (GooglePlay, Amazon appstore and Samsung appstore) in preventing our attacking apps from reaching their markets. Moreover, we have studied the data residue attacks on 10 devices from different vendors to see how vendor customization can affect our attacks. Google has acknowledged all our findings, and is working with us to get the problems fixed.Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

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