This paper investigates the capability of Large Language Models (LLMs) to detect obfuscation in Android apps through semantic reasoning.

The paper analyzes Android's permission system and finds that two legacy mechanisms—permission groups and normal-level custom permissions—allow apps to silently gain excessive permissions and expose s…

This study empirically analyzed 1,000 Android apps, finding that privacy policies are often vague and frequently fail to align with the actual sensitive data logged by the applications.

The paper empirically compares the security and privacy implementation characteristics of major Android messaging apps (Meta Messenger, Signal, and Telegram) using static and dynamic analysis, finding…

This paper conducts a longitudinal study on Android app signing credentials leakage, identifying over 5,600 compromised keystores and 278 affected apps.

PrivacyAssist is a multi-agent LLM framework that detects inconsistencies between user-granted app permissions and the app's actual data collection practices, finding that most apps are not fully tran…

The paper introduces a static analysis pipeline using graph kernels to automatically attribute unknown Android proxy malware to specific commercial proxy networks with high accuracy.

The paper proposes a time-aware self-supervised learning framework using BYOL to improve Android malware detection robustness by accurately accounting for app release times.

The paper analyzed 25 popular mental health apps and found significant privacy gaps, revealing that most apps fail to disclose embedded trackers and dangerous permissions, undermining informed user co…

The study surveyed Android developers to assess their willingness to adopt changes that mitigate device fingerprinting risks, finding that developers overwhelmingly support privacy protections even wi…

This study empirically demonstrates that privacy exposure in mobile gaming apps is primarily driven by complex, configuration-level SDK ecosystems rather than just the permissions the app explicitly r…

This study empirically analyzed 41 mobile gaming apps, finding that while device ID disclosures were relatively consistent, location and personal information disclosures showed significant mismatches…

The paper introduces Praxis, a behavior-oriented Android malware detection system that hypothesizes, confirms, and judges malicious behaviors.

The paper identifies and demonstrates a novel vulnerability, cross-app context poisoning, in the shared context architecture of ChatGPT Apps, allowing malicious apps to manipulate the LLM's behavior a…

AndroScanner is an automated pipeline that detects backend vulnerabilities in Android applications by combining static and dynamic analysis, successfully identifying a zero-day Excessive Data Exposure…

This paper proposes an approach to automate the development of app-specific voice assistants using GUI test code and large language models.

WOOTdroid is a novel, non-invasive system for comprehensive on-device tracing on stock Android that simultaneously addresses syscall data loss and the semantic gap in Binder IPC events.

PolicyGapper is an LLM-based tool that automatically detects inconsistencies and omissions between a mobile app's Google Play Data Safety Section and its official Privacy Policy, identifying thousands…

The paper proposes a privacy-by-design pipeline for Android malware detection that achieves strong performance by avoiding the collection of sensitive user data entirely.

This paper reviews existing process models for B2X mobile app development and conducts expert interviews to create a reference model for management decision support.