Clipboard usage is prevalent in mobile applications nowadays. However, insufficient access control on the clipboard in mobile operating systems exposes its contained data to high risks where one application can read the data, store it locally, or even send it to remote servers. Unfortunately, the literature only has ad-hoc studies in this respect and lacks a comprehensive and systematic study of the entire mobile application ecosystem. Therefore, this paper proposes an automated tool, ClipboardScope+, that leverages the principled static program analysis to uncover the clipboard data usage in mobile applications at scale by defining a usage as a combination of two aspects, i.e., how the clipboard data is validated and where does it go. It defines four primary categories of clipboard data operation, namely spot-on, grand-slam, selective, and cherry-pick, based on the clipboard usage in an application. ClipboardScope+ is evaluated on over1.2 millionmobile applications available on Google Play, spanning the years 2022 and 2023. It uncovered an increase of 5.9% in behaviors of storing and transferring clipboard data over the one-year time, most of which occur automatically in background services. We also conducted a comprehensive case study to characterize different clipboard usages and reveal their privacy issues. Moreover, we uncovered a prevalent programming habit of using theSharedPreferencesobject to store historical data, which can become an unnoticeable privacy leakage channel.

Privacy labels (e.g., Data Safety section on Google Play) aim to replace lengthy privacy policies with concise and standardized summaries of in-app privacy practices. However, studies reveal widespread inaccuracies in these self-reported labels, with developers omitting or misrepresenting privacy practices, undermining user trust and regulatory compliance. Existing methods for detecting such discrepancies lack coverage or scalability and fail to address the semantic ambiguity inherent in privacy label auditing. We present Iterative Context Reconstruction (ICR), an evidence-driven workflow that reconstructs context from decompiled code to resolve the ambiguity. Based on ICR, PriLabel: Towards Comprehensively Uncovering Omitted Disclosures in Privacy Labels of Android Apps on a Large Scale is a context-aware static auditor that comprehensively uncoversomitted disclosuresin Android privacy labels, mapping transmitted data to Google’s label taxonomy in asource-freeandontology-freemanner. Our evaluation demonstrates PriLabel: Towards Comprehensively Uncovering Omitted Disclosures in Privacy Labels of Android Apps on a Large Scale’s high precision (91.5%) in detecting omitted disclosures in privacy labels. Applied to 4,851 top-installed Google Play apps, it revealed that 2,374 apps omitted at least one disclosure, with 210 transmitting sensitive financial data (e.g., credit card numbers) without proper labeling, exposing systemic risks of non-compliance.

Recently, clipboard usage has become prevalent in mobile apps allowing users to copy and paste text within the same app or across different apps. However, insufficient access control on the clipboard in the mobile operating systems exposes its contained data to high risks where one app can read the data copied in other apps and store it locally or even send it to remote servers. Unfortunately, the literature only has ad-hoc studies in this respect and lacks a comprehensive and systematic study of the entire mobile app ecosystem. To establish the missing links, this paper proposes an automated tool, ClipboardScope, that leverages the principled static program analysis to uncover the clipboard data usage in mobile apps at scale by defining a usage as a combination of two aspects, i.e., how the clipboard data is validated and where does it go. It defines four primary categories of clipboard data operation, namely spot-on, grand-slam, selective, and cherry-pick, based on the clipboard usage in an app. ClipboardScope is evaluated on 26,201 out of a total of 2.2 million mobile apps available on Google Play as of June 2022 that access and process the clipboard text. It identifies 23,948, 848, 1,075, and 330 apps that are recognized as the four designated categories, respectively. In addition, we uncovered a prevalent programming habit of using the SharedPreferences object to store historical data, which can become an unnoticeable privacy leakage channel.