Tracking GPTs Third Party Service: Automation, Analysis, and Insights

TL;DR

This paper introduces GPTs-ThirdSpy, an automated framework that extracts privacy settings of third-party GPTs, enabling systematic analysis of their integration, privacy, and security implications for academic research.

Contribution

The paper presents GPTs-ThirdSpy, a novel tool for real-time extraction of privacy metadata from third-party GPTs, supporting large-scale analysis of privacy and security issues.

Findings

GPTs-ThirdSpy effectively extracts privacy settings data.

The framework enables large-scale analysis of GPT third-party integrations.

It reveals transparency and security challenges in GPT app ecosystems.

Abstract

ChatGPT has quickly advanced from simple natural language processing to tackling more sophisticated and specialized tasks. Drawing inspiration from the success of mobile app ecosystems, OpenAI allows developers to create applications that interact with third-party services, known as GPTs. GPTs can choose to leverage third-party services to integrate with specialized APIs for domain-specific applications. However, the way these disclose privacy setting information limits accessibility and analysis, making it challenging to systematically evaluate the data privacy implications of third-party integrate to GPTs. In order to support academic research on the integration of third-party services in GPTs, we introduce GPTs-ThirdSpy, an automated framework designed to extract privacy settings of GPTs. GPTs-ThirdSpy provides academic researchers with real-time, reliable metadata on third-party services used by GPTs, enabling in-depth analysis of their integration, compliance, and potential security risks. By systematically collecting and structuring this data, GPTs-ThirdSpy facilitates large-scale research on the transparency and regulatory challenges associated with the GPT app ecosystem.