Impersonation tactics, such as app squatting and app cloning, have posed longstanding challenges in the mobile app industry, where malicious actors exploit the names and reputations of popular apps to deceive users. With the rapid growth of large language model (LLM) stores like GPT Store and FlowGPT, these issues have similarly surfaced, highlighting the urgent need for robust industry standards and automated detection mechanisms to safeguard the LLM app ecosystem and protect users from fraudulent practices. In this study, we present the first large-scale analysis of LLM app squatting and cloning using our custom-built tool, LLMappCrazy. LLMappCrazy covers 14 squatting generation techniques and integrates Levenshtein distance and BERT-based semantic analysis to detect cloning by analyzing app functional similarities. Using this tool, we generated variations of the top 1000 app names and found over 5,000 squatting LLM apps in the dataset. Additionally, we observed 13,325 cloning cases across six major platforms. After sampling, we find that 4.7% of the squatting apps and 18.4% of the cloning apps exhibited malicious behavior, including phishing, malware distribution, fake content dissemination, and aggressive ad injection. Our work provides actionable insights for industry stakeholders to address these growing threats and foster a safer, more trustworthy LLM app ecosystem.