Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security
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Abstract (arXiv)
ChatGPT has enabled third-party developers to create plugins to expand ChatGPT's capabilities.These plugins are distributed through OpenAI's plugin store, making them easily accessible to users. With ChatGPT as the backbone, this app ecosystem has illustrated great business potential by offering users personalized services in a conversational manner. Nonetheless, many crucial aspects regarding app development, deployment, and security of this ecosystem have yet to be thoroughly studied in the research community, potentially hindering a broader adoption by both developers and users. In this work, we conduct the first comprehensive study of the ChatGPT app ecosystem, aiming to illuminate its landscape for our research community. Our study examines the distribution and deployment models in the integration of LLMs and third-party apps, and assesses their security and privacy implications. We uncover an uneven distribution of functionality among ChatGPT plugins, highlighting prevalent and emerging topics. We also identify severe flaws in the authentication and user data protection for third-party app APIs integrated within LLMs, revealing a concerning status quo of security and privacy in this app ecosystem. Our work provides insights for the secure and sustainable development of this rapidly evolving ecosystem.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Aug 2024.
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Citing papers per year: 2024: 1; 2025: 6; 2026: 3.
10 papers.
- 2025Catch-Only-One: Non-Transferable Examples for Model-Specific Authorizationcited by 7
- 2025Re-Key-Free, Risky-Free: Adaptable Model Usage Controlcited by 6
- 2025LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systemscited by 5
- 2026AI Model Modulation with Logits Redistributioncited by 3
- 2025Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuationcited by 2
- 2024LLM App Squatting and Cloningcited by 1
- 2025Slice or the Whole Pie? Utility Control for AI Modelscited by 0
- 2025LaQual: An Automated Framework for LLM App Quality Evaluationcited by 0
- 2026AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programscited by 0
- 2026On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplacescited by 0
- 2026On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces28 Aug 2026
- 2026AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs2 Jul 2026
- 2026AI Model Modulation with Logits Redistribution13 Mar 2026
- 2025LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems2 Dec 2025
- 2025Re-Key-Free, Risky-Free: Adaptable Model Usage Control24 Nov 2025
- 2025Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization13 Oct 2025
- 2025LaQual: An Automated Framework for LLM App Quality Evaluation26 Aug 2025
- 2025Slice or the Whole Pie? Utility Control for AI Models6 Aug 2025
- 2025Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation8 Jul 2025
- 2024LLM App Squatting and Cloning12 Nov 2024