# LLM App Store Analysis: A Vision and Roadmap

arXiv: https://arxiv.org/abs/2404.12737 · HTML: https://mlsanity.com/paper/2404.12737/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2024.

## Built on by (most cited first, 13 of 13)

- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [Towards Automating Data Access Permissions in AI Agents](https://mlsanity.com/paper/2511.17959/) (2025)
- [On the (In)Security of LLM App Stores](https://mlsanity.com/paper/2407.08422/) (2024)
- [LLM Applications: Current Paradigms and the Next Frontier](https://mlsanity.com/paper/2503.04596/) (2025)
- [When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents](https://mlsanity.com/paper/2607.01641/) (2026)
- [GPT Store Mining and Analysis](https://mlsanity.com/paper/2405.10210/) (2024)
- [LLM App Squatting and Cloning](https://mlsanity.com/paper/2411.07518/) (2024)
- [AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs](https://mlsanity.com/paper/2607.01640/) (2026)
- [A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem](https://mlsanity.com/paper/2505.08148/) (2025)
- [IFHierBench: Hierarchical Instruction Following for Large Language Models](https://mlsanity.com/paper/2607.27912/) (2026)
- [SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling](https://mlsanity.com/paper/2502.14617/) (2025)
- [LaQual: An Automated Framework for LLM App Quality Evaluation](https://mlsanity.com/paper/2508.18636/) (2025)
- [Beyond Jailbreak: Unveiling Risks in LLM Applications Arising from Blurred Capability Boundaries](https://mlsanity.com/paper/2511.17874/) (2025)

## 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.