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.

With many trigger-action platforms that integrate Internet of Things (IoT) systems and online services, rich functionalities transparently connecting digital and physical worlds become easily accessible for the end users. On the other hand, such facilities incorporate multiple parties whose data control policies may radically differ and even contradict each other, and thus privacy violations may arise throughout the lifecycle (e.g., generation and transmission) of triggers and actions. In this work, we conduct an in-depth study on the privacy issues in multi-party trigger-action integration platforms (TAIPs). We first characterize privacy violations that may arise with the integration of heterogeneous systems and services. Based on this knowledge, we propose Taifu, a dynamic testing approach to identify privacy weaknesses from the TAIP. The key insight of Taifu is that the applets which actually program the trigger-action rules can be used as test cases to explore the behavior of the TAIP. We evaluate the effectiveness of our approach by applying it on the TAIPs that are built around the IFTTT platform. To our great surprise, we find that privacy violations are prevalent among them. Using the automatically generated 407 applets, each from a different TAIP, Taifu detects 194 cases with access policy breaches, 218 access control missing, 90 access revocation missing, 15 unintended flows, and 73 over-privilege access.

A key feature of the booming smart home is the integration of a wide assortment of technologies, including various standards, proprietary communication protocols and heterogeneous platforms. Due to customization, unsatisfied assumptions and incompatibility in the integration, critical security vulnerabilities are likely to be introduced by the integration. Hence, this work addresses the security problems in smart home systems from anintegrationperspective, as a complement to numerous studies that focus on the analysis of individual techniques. We propose HomeScan, an approach that examines the security of the implementations of smart home systems. It extracts the abstract specification of application-layer protocols and internal behaviors of entities, so that it is able to conduct an end-to-end security analysis against various attack models. Applying HomeScanon three extensively-used smart home systems, we have found twelve non-trivial security issues, which may lead to unauthorized remote control and credential leakage.

A key feature of the booming smart home is the integration of a wide assortment of technologies, including various standards, proprietary communication protocols and heterogeneous platforms. Due to customization, unsatisfied assumptions and incompatibility in the integration, critical security vulnerabilities are likely to be introduced by the integration. Hence, this work addresses the security problems in smart home systems from an integration perspective, as a complement to numerous studies that focus on the analysis of individual techniques. We propose HOMESCAN, an approach that examines the security of the implementations of smart home systems. It extracts the abstract specification of application-layer protocols and internal behaviors of participants, so that it is able to conduct an end-to-end security analysis against various attack models. Applying HOMESCAN on three extensively-used smart home systems, we have found twelve non-trivial security vulnerabilities, which may lead to unauthorized remote control and credential leakage.