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Emulating firmware of microcontrollers is challenging due to the lack ofperipheral models. Existing work finds out how to respond to peripheral readoperations by analyzing the target firmware. This is problematic because thefirmware sometimes does not contain enough clues to support the emulation oreven contains misleading information (e.g. buggy firmware). In this work, wepropose a new approach that builds peripheral models from the peripheralspecification. Using NLP, we translate peripheral behaviors in human language(documented in chip manuals) into a set of structured condition-action rules.By checking, executing, and chaining them at runtime, we can dynamicallysynthesize a peripheral model for each firmware execution. The extractedcondition-action rules might not be complete or even be wrong. We, therefore,propose incorporating symbolic execution to quickly pinpoint the root cause.This assists us in the manual correction of the problematic rules. We haveimplemented our idea for five popular MCU boards spanning three different chipvendors. Using a new edit-distance-based algorithm to calculate tracedifferences, our evaluation against a large firmware corpus confirmed that ourprototype achieves much higher fidelity compared with state-of-the-artsolutions. Benefiting from the accurate emulation, our emulator effectivelyavoids false positives observed in existing fuzzing work. We also designed anew dynamic analysis method to perform driver code compliance checks againstthe specification. We found some non-compliance which we later confirmed to bebugs caused by race conditions.

What Your Firmware Tells You Is Not How You Should Emulate It: A Specification-Guided Approach for Firmware Emulation (Extended Version)