LLM App Store Analysis: A Vision and Roadmap
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Abstract (arXiv)
The rapid growth and popularity of large language model (LLM) app stores have created new opportunities and challenges for researchers, developers, users, and app store managers. As the LLM app ecosystem continues to evolve, it is crucial to understand the current landscape and identify potential areas for future research and development. This paper presents a forward-looking analysis of LLM app stores, focusing on key aspects such as data mining, security risk identification, development assistance, and market dynamics. Our comprehensive examination extends to the intricate relationships between various stakeholders and the technological advancements driving the ecosystem's growth. We explore the ethical considerations and potential societal impacts of widespread LLM app adoption, highlighting the need for responsible innovation and governance frameworks. By examining these aspects, we aim to provide a vision for future research directions and highlight the importance of collaboration among stakeholders to address the challenges and opportunities within the LLM app ecosystem. The insights and recommendations provided in this paper serve as a foundation for driving innovation, ensuring responsible development, and creating a thriving, user-centric LLM app landscape.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2024.
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Citing papers per year: 2024: 3; 2025: 6; 2026: 3.
12 papers.
- 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
- 2025Towards Automating Data Access Permissions in AI Agentscited by 20
- 2024On the (In)Security of LLM App Storescited by 12
- 2025LLM Applications: Current Paradigms and the Next Frontiercited by 8
- 2026When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agentscited by 6
- 2024GPT Store Mining and Analysiscited by 5
- 2024LLM App Squatting and Cloningcited by 1
- 2025A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystemcited by 1
- 2026IFHierBench: Hierarchical Instruction Following for Large Language Modelscited by 1
- 2025SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scalingcited 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
- 2026IFHierBench: Hierarchical Instruction Following for Large Language Models30 Jul 2026
- 2026When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents2 Jul 2026
- 2026AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs2 Jul 2026
- 2025Towards Automating Data Access Permissions in AI Agents22 Nov 2025
- 2025LaQual: An Automated Framework for LLM App Quality Evaluation26 Aug 2025
- 2025A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem13 May 2025
- 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions30 Mar 2025
- 2025LLM Applications: Current Paradigms and the Next Frontier6 Mar 2025
- 2025SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling20 Feb 2025
- 2024LLM App Squatting and Cloning12 Nov 2024
- 2024On the (In)Security of LLM App Stores11 Jul 2024
- 2024GPT Store Mining and Analysis16 May 2024