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Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security

  • Chuan Yan
  • , Ruomai Ren
  • , Mark Huasong Meng
  • , Liuhuo Wan
  • , Tian Yang Ooi
  • , Guangdong Bai*
  • *Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

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. © 2024 the owner/author(s). Publication rights licensed to ACM.
Original languageEnglish
Title of host publicationProceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages1370-1382
Number of pages13
ISBN (Print)9798400712487
DOIs
Publication statusPublished - Oct 2024
Externally publishedYes
Event39th ACM/IEEE International Conference on Automated Software Engineering (ASE 2024) - Sacramento, United States
Duration: 27 Oct 20241 Nov 2024
https://conf.researchr.org/home/ase-2024

Publication series

NameProceedings - ACM/IEEE International Conference on Automated Software Engineering, ASE

Conference

Conference39th ACM/IEEE International Conference on Automated Software Engineering (ASE 2024)
Abbreviated titleASE ’24
PlaceUnited States
CitySacramento
Period27/10/241/11/24
Internet address

Funding

We thank anonymous reviewers for their insightful comments. This research has been partially supported by Australian Research Council Discovery Projects (DP230101196, DP240103068).

Research Keywords

  • deployment
  • large language model
  • security
  • testing

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