Don't Bite Off More than You Can Chew: Investigating Excessive Permission Requests in Trigger-Action Integrations
Liuhuo Wan, Kailong Wang, Kulani Mahadewa, Haoyu Wang, Guangdong Bai · 2024
Web-based trigger-action platforms (TAP) allow users to integrate Internet of Things (IoT) systems and online services into trigger-action integrations (TAIs), facilitating rich automation tasks known as applets. Despite their benefits, these integrations~(typically involving the TAP, trigger, and action service providers) pose significant security and privacy challenges, such as mis-triggering and data leakage. This work investigates cross-entity permission management within TAIs to address the underlying causes of these security and privacy issues, emphasizing permission-functionality consistency to ensure fairness in permission requests. We introduce PFCon, a system that leverages GPT-based language models for analyzing required and requested permissions, revealing excessive permission requests in a large-scale study of IFTTT TAP. Our findings highlight the need for service providers to enforce permission-functionality consistency, raising awareness of the importance of security and privacy in TAI.