Internet of Things (IoT) deployments are becoming increasingly automated and vastly more complex. Facilitated by programming abstractions such as trigger-action rules, end-users can now easily create new functionalities by interconnecting their devices and other online services. However, when multiple rules are simultaneously enabled, complex system behaviors arise that are difficult to understand or diagnose. While history tells us that such conditions are ripe for exploitation, at present the security states of trigger-action IoT deployments are largely unknown. In this work, we conduct a comprehensive analysis of the interactions between trigger-action rules in order to identify their security risks. Using IFTTT as an exemplar platform, we first enumerate the space of inter-rule vulnerabilities that exist within trigger-action platforms. To aid users in the identification of these dangers, we go on to present iRuler, a system that performs Satisfiability Modulo Theories (SMT) solving and model checking to discover inter-rule vulnerabilities within IoT deployments. iRuler operates over an abstracted information flow model that represents the attack surface of an IoT deployment, but we discover in practice that such models are difficult to obtain given the closed nature of IoT platforms. To address this, we develop methods that assist in inferring trigger-action information flows based on Natural Language Processing. We develop a novel evaluative methodology for approximating plausible real-world IoT deployments based on the installation counts of 315,393 IFTTT applets, determining that 66% of the synthetic deployments in the IFTTT ecosystem exhibit the potential for inter-rule vulnerabilities. Combined, these efforts provide the insight into the real-world dangers of IoT deployment misconfigurations.
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If This Context Then That Concern : Exploring users’ concerns with IFTTT applets
Abstract End users are increasingly using trigger-action platforms like If-This-Then-That (IFTTT) to create applets to connect smart-home devices and services. However, there are inherent implicit risks in using such applets—even non-malicious ones—as sensitive information may leak through their use in certain contexts ( e.g., where the device is located, who can observe the resultant action). This work aims to understand to what extent end users can assess this implicit risk. More importantly we explore whether usage context makes a difference in end-users’ perception of such risks. Our work complements prior work that has identified the impact of usage context on expert evaluation of risks in IFTTT by focusing the impact of usage context on end-users’ risk perception. Through a Mechanical Turk survey of 386 participants on 49 smart-home IFTTT applets, we found that participants have a nuanced view of contextual factors and that different values for contextual factors impact end-users’ risk perception differently. Further, our findings show that nudging the participants to think about different usage contexts led them to think deeper about the associated risks and raise their concern scores.
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- Award ID(s):
- 2008089
- PAR ID:
- 10357413
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2022
- Issue:
- 1
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 166 to 186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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