skip to main content


Title: A Study of Data Store-based Home Automation
Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store. Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines. This analysis results in ten key findings with serious security implications. For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home. Our findings draw attention to the unique security challenges of platforms that execute routines via centralized data stores, and highlight the importance of enforcing security by design in emerging home automation platforms.  more » « less
Award ID(s):
1815336
NSF-PAR ID:
10173156
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy
Page Range / eLocation ID:
73 to 84
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Home automation platforms enable consumers to conveniently automate various physical aspects of their homes. However, the security flaws in the platforms or integrated third-party products can have serious security and safety implications for the user’s physical environment. This article describes our systematic security evaluation of two popular smart home platforms, Google’s Nest platform and Philips Hue, which implement home automation “routines” (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store . Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines, and it leads to 11 key findings with serious security implications. We combine several of the vulnerabilities we find to demonstrate the first end-to-end instance of lateral privilege escalation in the smart home, wherein we remotely disable the Nest Security Camera via a compromised light switch app. Finally, we discuss potential defenses, and the impact of the continuous evolution of smart home platforms on the practicality of security analysis. Our findings draw attention to the unique security challenges of smart home platforms and highlight the importance of enforcing security by design. 
    more » « less
  2. This paper focuses on developing a security mechanism geared towards appified smart-home platforms. Such platforms often expose programming interfaces for developing automation apps that mechanize different tasks among smart sensors and actuators (e.g., automatically turning on the AC when the room temperature is above 80 F). Due to the lack of effective access control mechanisms, these automation apps can not only have unrestricted access to the user's sensitive information (e.g., the user is not at home) but also violate user expectations by performing undesired actions. As users often obtain these apps from unvetted sources, a malicious app can wreak havoc on a smart-home system by either violating the user's security and privacy, or creating safety hazards (e.g., turning on the oven when no one is at home). To mitigate such threats, we propose Expat which ensures that user expectations are never violated by the installed automation apps at runtime. To achieve this goal, Expat provides a platform-agnostic, formal specification language UEI for capturing user expectations of the installed automation apps' behavior. For effective authoring of these expectations (as policies) in UEI, Expat also allows a user to check the desired properties (e.g., consistency, entailment) of them; which due to their formal semantics can be easily discharged by an SMT solver. Expat then enforces UEI policies in situ with an inline reference monitor which can be realized using the same app programming interface exposed by the underlying platform. We instantiate Expat for one of the representative platforms, OpenHAB, and demonstrate it can effectively mitigate a wide array of threats by enforcing user expectations while incurring only modest performance overhead. 
    more » « less
  3. Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices. 
    more » « less
  4. Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smartphone operating systems—the permission to access operations are separated by the device which performs them instead of their functionality. Unfortunately, this leads to two issues: (1) apps that do not require access to all of the granted device operations have overprivileged access to them, (2) apps might pose a higher risk to users than needed because physical device operations are fundamentally risk-asymmetric — “door.unlock” provides access to burglars, and “door.lock” can potentially lead to getting locked out. Overprivileged apps with access to mixed-risk operations only increase the potential for damage. We present Tyche, a secure development methodology that leverages the risk-asymmetry in physical device operations to limit the risk that apps pose to smart home users, without increasing the user’s decision overhead. Tyche introduces the notion of risk-based permissions for IoT systems. When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity. Starting from a set of permissions derived from the popular Samsung SmartThings platform, we conduct a user study involving domain-experts and Mechanical Turk users to compute a relative ranking of risks associated with device operations. We find that user assessment of risk closely matches that of domain experts. Using this insight, we define risk-based groupings of device operations, and apply it to existing SmartThings apps. We show that existing apps can reduce access to high-risk operations by 60% while remaining operable. 
    more » « less
  5. A smart home involves a variety of entities, such as IoT devices, automation applications, humans, voice assistants, and companion apps. These entities interact in the same physical environment, which can yield undesirable and even hazardous results, called IoT interaction threats. Existing work on interaction threats is limited to considering automation apps, ignoring other IoT control channels, such as voice commands, companion apps, and physical operations. Second, it becomes increasingly common that a smart home utilizes multiple IoT platforms, each of which has a partial view of device states and may issue conflicting commands. Third, compared to detecting interaction threats, their handling is much less studied. Prior work uses generic handling policies, which are unlikely to fit all homes. We present IoTMediator, which provides accurate threat detection and threat-tailored handling in multi-platform multi-control-channel homes. Our evaluation in two real-world homes demonstrates that IoTMediator significantly outperforms prior state-of-the-art work. 
    more » « less