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Unveiling AI-Driven Web Applications: Insights into Characteristics, Functionality, and Compliance

Liuhuo Wan, Zicong Liu, Chuan Yan, Liujia Wan, Naipeng Dong, Zi Huang, Guangdong Bai

2026Year

Abstract

Collaborative platforms such as Google Workspace, Microsoft Teams, and Zoom increasingly rely on third-party applications (referred to as plugins) to extend their core functionalities, with AI-assisted plugins emerging as a key driver of productivity. Despite their popularity and rapid adoption, little is known about the characteristics of the marketplace, the potential security and privacy risks that concern users, and the compliance of plugins with AI ethics guidelines. In this paper, we present the first large-scale, cross-platform study of plugins from five major web application marketplaces, covering domains from office productivity to software development. We systematically examine the distribution characteristics of current plugins, analyze users’ concerns, and assess their compliance with emerging AI regulations. Our findings indicate that (i) the current marketplaces exhibit an uneven distribution of functionality and installations, (ii) AI-assisted plugins face a range of emerging issues that negatively impact user experience, and (iii) a significant proportion of plugins fail to comply with established AI ethics principles. Our work highlights the need for strigent policies and security auditing to maintain quality of AI-assisted plugins.

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