LLMs on support of privacy and security of mobile apps: state of the art and research directions

Abstract

Modern life has witnessed the explosion of mobile devices. However, besides the valuable features that bring convenience to end users, security and privacy risks still threaten users of mobile apps. The increasing sophistication of these threats in recent years has underscored the need for more advanced and efficient detection approaches. In this chapter, we explore the application of Large Language Models (LLMs) to identify security risks and privacy violations and mitigate them for the mobile application ecosystem. By introducing state-of-the-art research that applied LLMs to mitigate the top 10 common security risks of smartphone platforms, we highlight the feasibility and potential of LLMs to replace traditional analysis methods, such as dynamic and hybrid analysis of mobile apps. As a representative example of LLM-based solutions, we present an approach to detect sensitive data leakage when users share images online, a common behavior of smartphone users nowadays. Finally, we discuss open research challenges.

I am writing to respectfully request the withdrawal of my recent submission to arXiv due to an authorship issue. The paper was submitted without the explicit consent of two co-authors. After internal discussion, they have expressed clear disagreement with the submission and raised concerns about unresolved academic inaccuracies in the current version