USENIX Security '21 - Automatic Firmware Emulation through Invalidity-guided Knowledge Inference

USENIX Security '21 - Automatic Firmware Emulation through Invalidity-guided Knowledge Inference

Wei Zhou, National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences; Le Guan, Department of Computer Science, University of Georgia; Peng Liu, College of Information Sciences and Technology, The Pennsylvania State University; Yuqing Zhang, National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences; School of Cyber Engineering, Xidian University; School of Computer Science and Cyberspace Security, Hainan University

Emulating firmware for microcontrollers is challenging due to the tight coupling between the hardware and firmware. This has greatly impeded the application of dynamic analysis tools to firmware analysis. The state-of-the-art work automatically models unknown peripherals by observing their access patterns, and then leverages heuristics to calculate the appropriate responses when unknown peripheral registers are accessed. However, we empirically found that this approach and the corresponding heuristics are frequently insufficient to emulate firmware. In this work, we propose a new approach called µEmu to emulate firmware with unknown peripherals. Unlike existing work that attempts to build a general model for each peripheral, our approach learns how to correctly emulate firmware execution at individual peripheral access points. It takes the image as input and symbolically executes it by representing unknown peripheral registers as symbols. During symbolic execution, it infers the rules to respond to unknown peripheral accesses. These rules are stored in a knowledge base, which is referred to during the dynamic firmware analysis. µEmu achieved a passing rate of 93% in a set of unit tests for peripheral drivers without any manual assistance. We also evaluated µEmu with real-world firmware samples and new bugs were discovered.