With the increasing adoption of large language models (LLMs), a growing number of applications leveraging these models have emerged. However, LLM-based applications face significant security challenges. The OWASP Top 10 for LLMs has identified critical vulnerabilities, including prompt injections, data leakage, and unauthorized code execution. While previous research has explored specific security issues, these studies often focus on a narrow range of problems and a small number of applications, leaving gaps in our understanding of security threats in real-world LLM-based applications. To facilitate research in this field, an automated tool was developed to systematically collect LLM applications and construct a dataset, particularly focusing on independent applications that leverage LLM APIs or frameworks. Our dataset includes 33,001 repositories, providing a broad and representative foundation for security analysis. Both our dataset and tool are publicly available to support further research and foster advancements in the security and development of LLM-based applications.