# GPT Store Mining and Analysis

arXiv: https://arxiv.org/abs/2405.10210 · HTML: https://mlsanity.com/paper/2405.10210/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 6 of 6)

- [Assessing Prompt Injection Risks in 200+ Custom GPTs](https://mlsanity.com/paper/2311.11538/) (2023)
- [A First Look at GPT Apps: Landscape and Vulnerability](https://mlsanity.com/paper/2402.15105/) (2024)
- [LLM App Store Analysis: A Vision and Roadmap](https://mlsanity.com/paper/2404.12737/) (2024)
- [Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs](https://mlsanity.com/paper/2401.00905/) (2023)
- [Beyond Google Play: A Large-Scale Comparative Study of Chinese Android App Markets](https://mlsanity.com/paper/1810.07780/) (2018)
- [GPT in Sheep's Clothing: The Risk of Customized GPTs](https://mlsanity.com/paper/2401.09075/) (2024)

## Built on by (most cited first, 5 of 5)

- [On the (In)Security of LLM App Stores](https://mlsanity.com/paper/2407.08422/) (2024)
- [LLM App Squatting and Cloning](https://mlsanity.com/paper/2411.07518/) (2024)
- [A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem](https://mlsanity.com/paper/2505.08148/) (2025)
- [When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs](https://mlsanity.com/paper/2506.00197/) (2025)
- [LaQual: An Automated Framework for LLM App Quality Evaluation](https://mlsanity.com/paper/2508.18636/) (2025)

## Abstract (arXiv)

As a pivotal extension of the renowned ChatGPT, the GPT Store serves as a dynamic marketplace for various Generative Pre-trained Transformer (GPT) models, shaping the frontier of conversational AI. This paper presents an in-depth measurement study of the GPT Store, with a focus on the categorization of GPTs by topic, factors influencing GPT popularity, and the potential security risks. Our investigation starts with assessing the categorization of GPTs in the GPT Store, analyzing how they are organized by topics, and evaluating the effectiveness of the classification system. We then examine the factors that affect the popularity of specific GPTs, looking into user preferences, algorithmic influences, and market trends. Finally, the study delves into the security risks of the GPT Store, identifying potential threats and evaluating the robustness of existing security measures. This study offers a detailed overview of the GPT Store's current state, shedding light on its operational dynamics and user interaction patterns. Our findings aim to enhance understanding of the GPT ecosystem, providing valuable insights for future research, development, and policy-making in generative AI.