Looking from the Mirror: Evaluating IoT Device Security through Mobile Companion Apps

Xueqiang Wang, Indiana University Bloomington

Smart home IoT devices have increasingly become a favorite target for the cybercriminals due to their weak security designs. To identify these vulnerable devices, existing approaches rely on the analysis of either real devices or their firmware images. These approaches, unfortunately, are difficult to scale in the highly fragmented IoT market due to the unavailability of firmware images and the high cost involved in acquiring real-world devices for security analysis.

In this paper, we present a platform that accelerates vulnerable device discovery and analysis, without requiring the presence of actual devices or firmware. Our approach is based on two key observations: First, IoT devices tend to reuse and customize others’ components (e.g., software, hardware, protocol, and services), so vulnerabilities found in one device are often present in others. Second, reused components can be indirectly inferred from the mobile companion apps of the devices; so a cross analysis of mobile companion apps may allow us to approximate the similarity between devices. Using a suite of program analysis techniques, our platform analyzes mobile companion apps of smart home IoT devices on market and automatically discovers potentially vulnerable ones, allowing us to perform a large-scale analysis involving over 4,700 devices. Our study brings to light the sharing of vulnerable components across the smart home IoT devices (e.g., shared vulnerable protocol, backend services, device rebranding), and leads to the discovery of 324 devices from 73 different vendors that are likely to be vulnerable to a set of security issues.

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