The field of access control and mobile app security is witnessing significant advancements with the integration of AI and large language models (LLMs). Researchers are exploring the potential of LLMs in making access control decisions, identifying AI capabilities in mobile apps, and detecting deceptive design patterns. The use of LLMs is showing promise in improving the accuracy and efficiency of access control decisions, as well as in identifying AI-driven features in mobile apps. However, challenges such as scalability, reliability, and transparency remain to be addressed. Noteworthy papers in this area include: Towards Harnessing the Power of LLMs for ABAC Policy Mining, which evaluates the performance of LLMs in policy mining, and OpenApps, which develops a lightweight ecosystem for simulating environment variations to measure UI-agent reliability. Additionally, LLMAID is proposed as a framework for identifying AI capabilities in Android apps, and Exploring Hidden Geographic Disparities in Android Apps reveals regional differences in app behavior and security.