LLMs on support of privacy and security of mobile apps: state of the art and research directions
This addresses security and privacy threats for mobile app users, but it is incremental as it reviews existing research and discusses directions.
The chapter explores using Large Language Models (LLMs) to detect and mitigate security and privacy risks in mobile apps, highlighting their potential to replace traditional analysis methods like dynamic and hybrid analysis.
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.