Tracking GPTs Third Party Service: Automation, Analysis, and Insights
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915353524699136 |
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| author | Yan, Chuan Wan, Liuhuo Guan, Bowei Yu, Fengqi Bai, Guangdong Dong, Jin Song |
| author_facet | Yan, Chuan Wan, Liuhuo Guan, Bowei Yu, Fengqi Bai, Guangdong Dong, Jin Song |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracking GPTs Third Party Service: Automation, Analysis, and Insights Yan, Chuan Wan, Liuhuo Guan, Bowei Yu, Fengqi Bai, Guangdong Dong, Jin Song Cryptography and Security Software Engineering 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. |
| title | Tracking GPTs Third Party Service: Automation, Analysis, and Insights |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2506.17315 |