𝜇AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware


Fuzzing is one of the most effective approaches to find software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus cannot be used to fuzz the corresponding driver code. In this work, we present 𝜇AFL, a hardware-assisted approach to fuzzing firmware on real devices. It leverages debugging tools in existing embedded system development to construct an AFL-compatible fuzzing framework. Specifically, we use the debug dongle to bridge the fuzzing environment on the PC and the target firmware on the microcontroller device. To collect code coverage information without costly code instrumentation, 𝜇AFL relies on the ARM ETM hardware debugging feature, which transparently collects the instruction trace and streams the results to the PC. However, the raw ETM data is obscure and needs enormous computing resources to recover the actual instruction flow. We therefore propose an alternative representation of code coverage, which retains the same path sensitivity as the original AFL algorithm, but can directly work on the raw ETM data without matching them with disassembled instructions. To further reduce the workload, we use the DWT hardware feature to selectively collect runtime information of interest. We evaluated 𝜇AFL on two real evaluation boards from two major vendors: NXP and STMicroelectronics. With our prototype, we discovered ten 0-day bugs in the driver code shipped with the SDK of STMicroelectronics and three 0-day bugs in the SDK of NXP. Considering the wide adoption of chip-vendor-provided SDK code in real products, our results are alarming.

2022 IEEE/ACM 44rd International Conference on Software Engineering (ICSE ‘22’), to appear