The advantages of large language models (LLMs) in content comprehension and question answering have led to the rapid emergence of LLM agent. Developers across diverse domains are actively building their own agent applications (apps), as these apps can streamline workflows, boost efficiency, or deliver innovative solutions, thereby enhancing the competitiveness of their products. Agent apps are playing an increasingly important role in our daily lives. However, numerous serious vulnerabilities and security issues have been identified in these apps. To effectively manage future security risks, it is essential to systematically understand the unique characteristics of agent app vulnerabilities and their mitigation. In this paper, we present the first comprehensive study on the vulnerabilities of agent apps, the mitigation practices of app developers, and the associated challenges and trade-offs. We identify 14 types of vulnerabilities and 16 root causes across 7 components, based on an analysis of 221 real-world vulnerabilities. Our study further investigates developer reactions, evaluates the effectiveness of various mitigation strategies, and explores the practical challenges and inevitable trade-offs in vulnerability mitigation. Finally, we distill 12 key findings, discuss their implications for agent app developers, maintainers, and security researchers, and offer suggestions for future research directions.

Fuzz driver generation (FDG) is a fundamental technique for fuzzing library software. Existing FDG approaches have been highly successful with open-source libraries. However, in practice, due to the complex nature of OEM Android frameworks (e.g., customized compilation toolchains, extensive codebases, diverse C/C++ language features), it is not straightforward to integrate existing fuzz driver generation tools with OEM Android libraries. To address this challenge, we first systematically summarize the obstacles to applying existing tools (e.g., FuzzGen) to libraries of an OEM Android (i.e., ColorOS), including compatibility, usability, and effectiveness issues. Following this, we developed a new fuzz driver generation tool, namely FuzzGen++, specifically designed to tackle these obstacles one by one. In our evaluation, we demonstrate the advantages of FuzzGen++ in real-world OEM Android frameworks. FuzzGen++ is compatible with OEM Android and can generate fuzz drivers for all its libraries which are not supported by existing works. The additional analysis of the OEM Android code also enhances its usability within the system. Overall, FuzzGen++ has helped automatically generate 21,457 fuzz drivers. Additionally, through fuzz driver ranking and selection solution, FuzzGen++ figured out cut off 95% fuzz drivers which are less useful. FuzzGen++ supports sophisticated C/C++ features in code analysis, ensuring effectiveness. Compared to hand-written fuzz drivers, FuzzGen++ could generate and select fuzz drivers providing a 107.92% coverage improvement. Furthermore, they discovered 6 bugs, showcasing the capability of FuzzGen++ to find real-world issues.