Android SmartTVs Vulnerability Discovery via Log-Guided Fuzzing

The recent rise of Smart IoT devices has opened new doors for cyber criminals to achieve damages unique to the ecosystem. SmartTVs, the most widely adopted home-based IoT devices, are no exception. Albeit their popularity, little has been done to evaluate their security and associated risks. To proactively address the problem, we propose a systematic evaluation of Android SmartTVs security. We overcome a number of prominent challenges such as most of the added TV related functionalities are (partially) implemented in the native layer and many security problems only manifest themselves on the physical aspect without causing any misbehaviors inside the OS. We develop a novel dynamic fuzzing approach, which features an on-the-fly log-based input specification derivation and feedback collection. Our solution further introduces a novel external observer that monitors the TV-related physical symptoms (i.e., visual and auditory) to detect potential physical anomalies. We leverage our technique to analyze 11 Android TV Boxes. Our analysis reveals 37 unique vulnerabilities, leading to high-impact cyber threats (e.g., corrupting critical boot environment settings and accessing highlysensitive data), memory corruptions, and even visual and auditory disturbances (e.g., persistent display content corruption and audio muting).