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Dynamic testing or fuzzing of embedded firmware is severely limited byhardware-dependence and poor scalability, partly contributing to the widespreadvulnerable IoT devices. We propose a software framework that continuouslyexecutes a given firmware binary while channeling inputs from an off-the-shelffuzzer, enabling hardware-independent and scalable firmware testing. Ourframework, using a novel technique called P$^2$IM, abstracts diverseperipherals and handles firmware I/O on the fly based on automaticallygenerated models. P$^2$IM is oblivious to peripheral designs and generic tofirmware implementations, and therefore, applicable to a wide range of embeddeddevices. We evaluated our framework using 70 sample firmware and 10 firmwarefrom real devices, including a drone, a robot, and a PLC. It successfullyexecuted 79% of the sample firmware without any manual assistance. We alsoperformed a limited fuzzing test on the real firmware, which unveiled 7 uniqueunknown bugs.

P$^2$IM: Scalable and Hardware-independent Firmware Testing via Automatic Peripheral Interface Modeling (extended version)