A First Look at GPT Apps: Landscape and Vulnerability

Abstract: With the advancement of Large Language Models (LLMs), increasingly sophisticated and powerful GPTs are entering the market. Despite their popularity, the LLM ecosystem still remains unexplored. Additionally, LLMs' susceptibility to attacks raises concerns over safety and plagiarism. Thus, in this work, we conduct a pioneering exploration of GPT stores, aiming to study vulnerabilities and plagiarism within GPT applications. To begin with, we conduct, to our knowledge, the first large-scale monitoring and analysis of two stores, an unofficial GPTStore.AI, and an official OpenAI GPT Store. Then, we propose a TriLevel GPT Reversing (T-GR) strategy for extracting GPT internals. To complete these two tasks efficiently, we develop two automated tools: one for web scraping and another designed for programmatically interacting with GPTs. Our findings reveal a significant enthusiasm among users and developers for GPT interaction and creation, as evidenced by the rapid increase in GPTs and their creators. However, we also uncover a widespread failure to protect GPT internals, with nearly 90% of system prompts easily accessible, leading to considerable plagiarism and duplication among GPTs.