Security and Privacy Testing Automation for LLM-Enhanced Applications in Mobile Devices

Аннотация

The integration of large language models (LLMs) into mobile applications introduces new vectors for security and privacy vulnerabilities. This study proposes an automated framework for systematically testing LLM-enabled mobile apps, focusing on identifying potential threats such as prompt injection, data leakage, unauthorized access, and adversarial manipulation. The approach combines dynamic analysis, static code inspection, and machine learning- based anomaly detection to evaluate app behaviors in real-time. Our method ensures scalability and efficiency across diverse mobile platforms and LLM configurations. Results demonstrate significant improvements in detection rates and response times compared to conventional manual testing. This work aims to bridge the gap between AI innovation and secure mobile deployment, promoting trust in AI-integrated ecosystems. KEYWORDS LLM, security testing, privacy, automation, mobile applications, prompt injection, anomaly detection. 1. INTRODUCTION Large language models have helped mobile applications improve user interactions by providing support that adjusts to users, fast language processing, and better personalized features. Yet, because LLM-enhanced apps are spreading rapidly, there are now major worries about safeguarding data and protecting people’s privacy on phones [1]. Because LLM-based applications process a wide range of confidential user data, such as private messages, locations, and identification, such apps require strong and flexible security features. Regardless of how far mobile app development has come, automated systems meant for privacy and security testing have not adapted to the problems with LLMs. Existing ways of testing supply insufficient coverage for vulnerabilities specific to LLM interaction, for example, malicious prompts, information leaks by the model, and unapproved understanding of a user’s intentions [2]. This means that we need quick and flexible automated methods that handle the dynamic actions of LLMs used on mobile phones. This research examines the growing role of security and privacy testing automation in the development of LLM-assisted mobile applications. It sets out to determine existing difficulties, introduce possible strategies for machines to discover threats, and remind us of the need to match testing approaches with both evolving AI and relevant regulations. Correcting these vulnerabilities allows developers and stakeholders to better protect users and use intelligent mobile technologies properly. As more mobile apps start to use LLMs and offer voice services, health advice, and help with money matters, the areas that can be attacked have increased too. Processing in the cloud and using third-party APIs by these applications means they are more likely to be targeted by man-in-the-middle attacks, where private data could be https://www.academicpublishers.org/journals/index.php/ijns 30 A MERICAN ACADEMIC PUBLISHER stolen or stolen models can be created [3]. Besides, because LLM processes are not clearly defined, it can be difficult to find out how a security or privacy issue began. In order to step beyond signature approaches, testing tools should integrate model-based strategies, discover unusual events, and include privacy risk measurements, all taking advantage of collaboration between software testing, cybersecurity, and machine learning. 2. Literature Review Their important 2018 work titled “Static and Dynamic Analysis in Mobile Security” served as the groundwork for the transition in mobile security. The literature assessment separated existing mobile security methods into those that scan application codes without operating them and those that observe application activities while the app is running. They found that traditionally applied methods that work fast and require less power can still miss concealed threats targeting the running application. But, in contrast, dynamic analysis takes more effort to run on a bigger scale. They pointed out that methods that combine AI are key to faster alerting and adapting against attacks from different malware strains. It designed an AI-based static code analysis system using both Natural Language Processing (NLP) and Deep Learning (DL) to search for security issues in source code [3]. Because their system was trained on large amounts of real mobile application data, it can find security issues that regular reviewers commonly miss. With the AI system, both time and accuracy for identifying threats improved, adding to evidence that AI increases both speed and accuracy. It takes away much of the manual work from security analysts and also helps avoid missing problems in extensive codebases. Performed anomaly detection using ML in real time on mobile applications. To catch dangerous actions, their study examined how the app interacted, how it accessed the internet, and which permissions it required [4]. The security team used old information on attacks as training material for their