# A First Look at GPT Apps: Landscape and Vulnerability

**Authors:** Zejun Zhang, Li Zhang, Xin Yuan, Anlan Zhang, Mengwei Xu, Feng Qian  
**arXiv:** [2402.15105](https://arxiv.org/abs/2402.15105)  
**Date:** 2024-02-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.15105v1/

## Key points

- Large Language Models (LLMs) such as Generative Pre-trained Transformers (GPTs) have gained popularity in various applications but harbor unexplored vulnerabilities.
- Concerns over safety and plagiarism arise due to the susceptibility of LLMs to attacks.
- Researchers conducted a pioneering exploration of GPT stores to uncover vulnerabilities and instances of plagiarism in GPT applications.
- A novel TriLevel GPT Reversing (T-GR) strategy was introduced to extract internal components of GPTs for analysis.
- Automated tools for web scraping and programmatically interacting with GPTs were developed to facilitate the investigation efficiently.
- Nearly 90% of system prompts within GPTs are easily accessible, leading to widespread instances of plagiarism and duplication across different models.

## Summary

In their research titled "A First Look at GPT Apps: Landscape and Vulnerability," authors Zejun Zhang, Li Zhang, Xin Yuan, Anlan Zhang, Mengwei Xu, and Feng Qian delve into the realm of Large Language Models (LLMs) with a focus on Generative Pre-trained Transformers (GPTs). These advanced language models have gained popularity in various applications but still harbor unexplored vulnerabilities within the LLM ecosystem. Concerns over safety and plagiarism arise due to the susceptibility of LLMs to attacks. To address these issues, the researchers embark on a pioneering exploration of GPT stores, aiming to uncover vulnerabilities and instances of plagiarism in GPT applications. The study begins with a comprehensive analysis of two prominent stores: an unofficial platform known as GPTStore.AI and the official OpenAI GPT Store. This large-scale monitoring effort marks a significant milestone in understanding the landscape of GPT interactions. The researchers introduce a novel TriLevel GPT Reversing (T-GR) strategy designed to extract internal components of GPTs for further analysis. To facilitate their investigation efficiently, they develop automated tools for web scraping and programmatically interacting with GPTs. Through their findings, the team observes a remarkable level of enthusiasm among users and developers engaging with GPT technology. The rapid proliferation of new GPT variants and creators underscores the growing interest in leveraging these powerful language models. However, amidst this fervor lies a concerning trend – nearly 90% of system prompts within GPTs are easily accessible, leading to widespread instances of plagiarism and duplication across different models. Overall, this research sheds light on both the promising potential and inherent risks associated with GPT applications. By identifying vulnerabilities and addressing issues related to intellectual property protection, the study contributes valuable insights to enhance the security and integrity of future developments in the field of Large Language Models.