Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security

• URL: http://arxiv.org/abs/2408.14357v1
• Date: Mon, 26 Aug 2024 15:31:58 GMT
• Title: Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security
• Authors: Chuan Yan, Ruomai Ren, Mark Huasong Meng, Liuhuo Wan, Tian Yang Ooi, Guangdong Bai,
• Abstract summary: ChatGPT has enabled third-party developers to create plugins to expand ChatGPT's capabilities. We conduct the first comprehensive study of the ChatGPT app ecosystem, aiming to illuminate its landscape for our research community. We uncover an uneven distribution of functionality among ChatGPT plugins, highlighting prevalent and emerging topics.
• Score: 3.0924093890016904
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality [1.7624347338410744]
  ChatGPT is a Large Language Model (LLM) that can perform a variety of tasks with remarkable semantic understanding and accuracy. This study aims to gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security. It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing.
  arXiv  Detail & Related papers  (2024-08-01T10:14:05Z)
• Securing 3rd Party App Integration in Docker-based Cloud Software Ecosystems [0.0]
  We present a new concept for significant security improvements for cloud-based software ecosystems using Docker for 3rd party app integration. Based on the security features of Docker we describe a secure integration of applications in the cloud environment securely.
  arXiv  Detail & Related papers  (2024-05-18T15:26:38Z)
• Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? [83.19032025950986]
  We study the use of large language model-based agents for interacting with software via web browsers. WorkArena is a benchmark of 33 tasks based on the widely-used ServiceNow platform. BrowserGym is an environment for the design and evaluation of such agents.
  arXiv  Detail & Related papers  (2024-03-12T14:58:45Z)
• SecGPT: An Execution Isolation Architecture for LLM-Based Systems [37.47068167748932]
  SecGPT aims to mitigate the security and privacy issues that arise with the execution of third-party apps. We evaluate SecGPT against a number of case study attacks and demonstrate that it protects against many security, privacy, and safety issues.
  arXiv  Detail & Related papers  (2024-03-08T00:02:30Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
  Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing. As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework. This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
  arXiv  Detail & Related papers  (2023-11-14T21:46:27Z)
• Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
  We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
  arXiv  Detail & Related papers  (2023-11-11T11:01:13Z)
• LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins [31.678328189420483]
  Large language model (LLM) platforms have recently begun offering an app ecosystem to interface with third-party services on the internet. While these apps extend the capabilities of LLM platforms, they are developed by arbitrary third parties and thus cannot be implicitly trusted. We propose a framework that lays a foundation for LLM platform designers to analyze and improve the security, privacy, and safety of current and future third-party integrated LLM platforms.
  arXiv  Detail & Related papers  (2023-09-19T02:20:10Z)
• Unveiling Security, Privacy, and Ethical Concerns of ChatGPT [6.588022305382666]
  ChatGPT uses topic modeling and reinforcement learning to generate natural responses. ChatGPT holds immense promise across various industries, such as customer service, education, mental health treatment, personal productivity, and content creation. This paper focuses on security, privacy, and ethics issues, calling for concerted efforts to ensure the development of secure and ethically sound large language models.
  arXiv  Detail & Related papers  (2023-07-26T13:45:18Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.