Abstract Testing Android apps effectively requires a systematic exploration of the app’s possible states by simulating user interactions and system events. While existing approaches have proposed several fuzzing techniques to generate various text inputs and trigger user and system events for GUI state exploration, achieving high code coverage remains a significant challenge in Android app testing. The main challenges are (1) reasoning about the complex and dynamic layout of GUI screens; (2) generating required inputs/events to deal with certain widgets like pop-ups; and (3) coordination between current test inputs and previous inputs to avoid getting stuck in the same GUI screen without improving test coverage. To address these problems, we propose VLM-Fuzz , a novel automated approach for Android GUI testing. At its foundation, VLM-Fuzz utilizes a heuristic-based, recursive depth-first search (DFS) strategy that is intelligently guided by a Vision Language Model (VLM) to effectively explore the app’s complex GUI states. The core innovation of VLM-Fuzz is not simply the use of a VLM, but its strategic, on-demand integration within a hybrid exploration framework. Our approach combines a fast, heuristic-based DFS for standard GUI interactions with targeted, VLM-assisted analysis for visually complex screens. We use static analysis to analyze the Android Manifest file and the runtime GUI hierarchy XML to extract the list of components, intent-filters and interactive GUI widgets. VLM is used to reason about complex GUI layout and widgets on an on-demand basis. Based on the inputs from static analysis, VLM, and the current GUI state, we use some heuristics to deal with the above-mentioned challenges. We evaluated VLM-Fuzz based on a benchmark containing 59 apps obtained from a recent work and compared it against two state-of-the-art approaches: APE and DeepGUI . VLM-Fuzz outperforms the best baseline by 9.0% , 3.7% , and 2.1% in terms of class coverage, method coverage, and line coverage, respectively. We also ran VLM-Fuzz on 80 recent Google Play apps (i.e., updated in 2024). VLM-Fuzz detected 52 unique crashes in 12 apps, which have been reported to respective developers.

Android banking applications have revolutionized financial management by allowing users to perform various financial activities through mobile devices. However, this convenience has attracted cybercriminals who exploit security vulnerabilities to access sensitive financial data. FjordPhantom, a malware identified by our industry collaborator, uses virtualization and hooking to bypass the detection of malicious accessibility services, allowing it to conduct keylogging, screen scraping, and unauthorized data access. This malware primarily affects banking and finance apps across East and Southeast Asia region where our industry partner’s clients are primarily based in. It requires users to be deceived into installing a secondary malicious component and activating a malicious accessibility service. In our study, we conducted an empirical study on the susceptibility of banking apps in the region to FjordPhantom, analyzed the effectiveness of protective measures currently implemented in those apps, and discussed ways to detect and prevent such attacks by identifying and mitigating the vulnerabilities exploited by this malware.

The emergence of mobile technology has significantly advanced the banking sector in terms of how consumers interact with their banks and manage their finances. The accessibility and ease of financial services have been improved by the switch from desktop banking to mobile banking. Mobile banking has a lot of advantages, but it also has security concerns. Illegal access to personal and financial information often occurs due to lapses in mobile security. In recent years, we have worked with banks from 10 countries and systematically analyzed 28 of their apps. We found several vulnerabilities in these apps by manual code reviews and by conducting 11 types of attacks. We then proposed and applied adequate security measures to protect these apps. Finally, we added these measures to our tool named AppProtect+ to effectively identify and thwart these threats. In this paper, we report our experience and practice of securing these Android apps.

Android is the most popular operating system for mobile devices nowadays. Permissions are a very important part of Android security architecture. Apps frequently need the users' permission, but many of them only ask for it once—when the user uses the app for the first time—and then they keep and abuse the given permissions. Longing to enhance Android permission security and users' private data protection is the driving factor behind our approach to explore fine-grained context-sensitive permission usage analysis and thereby identify misuses in Android apps. In this work, we propose an approach for classifying the fine-grained permission uses for each functionality of Android apps that a user interacts with. Our approach, named DroidGem, relies on mainly three technical components to provide an in-context classification for permission (mis)uses by Android apps for each functionality triggered by users: (1) static inter-procedural control-flow graphs and call graphs representing each functionality in an app that may be triggered by users' or systems' events through UI-linked event handlers, (2) graph embedding techniques converting graph structures into numerical encoding, and (3) supervised machine learning models classifying (mis)uses of permissions based on the embedding. We have implemented a prototype of DroidGem and evaluated it on 89 diverse apps. The results show that DroidGem can accurately classify whether permission used by the functionality of an app triggered by a UI-linked event handler is a misuse in relation to manually verified decisions, with up to 95% precision and recall. We believe that such a permission classification mechanism can be helpful in providing fine-grained permission notices in a context related to app users' actions, and improving their awareness of (mis)uses of permissions and private data in Android apps.

Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the complexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for configuring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a series of input transformations. We use Bayesian optimization guided by metamorphic-testing output to automatically identify the optimal configuration. The approach aims to streamline the configuration process and enhance the effectiveness of unsupervised learning systems. It has been evaluated through experiments on six datasets from three domains for anomaly detection. The evaluation results show that our approach can find configurations outperforming the baseline approaches as they achieved a recall of 0.89 and a precision of 0.84 (on average).

It is the basic right of a user to know how the permissions are used within the Android app’s scope and to refuse the app if granted permissions are used for the activities other than specified use which can amount to malicious behavior.