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Exponential growth in embedded systems is driving the research imperative todevelop fuzzers to automate firmware testing to uncover software bugs andsecurity vulnerabilities. But, employing fuzzing techniques in this contextpresent a uniquely challenging proposition; a key problem is the need to dealwith the diverse and large number of peripheral communications in an automatedtesting framework. Recent fuzzing approaches: i) employ re-hosting methods byexecuting code in an emulator because fuzzing on resource limited embeddedsystems is slow and unscalable; and ii) integrate models of hardware behaviourto overcome the challenges faced by the massive input-space to be exploredcreated by peripheral devices and to generate inputs that are effective inaiding a fuzzer to make progress. Our efforts expounds upon program executionbehaviours unique to firmware to address the resulting input-space searchproblem. The techniques we propose improve the fuzzer's ability to generatevalues likely to progress execution and avoids time consumed on mutating inputsthat are functionally equivalent to other test cases. We demonstrate themethods are highly efficient and effective at overcoming the input-space searchproblem. Our emulation-based implementation, Ember-IO, when compared to theexisting state-of-the-art fuzzing framework across 21 firmware binaries,demonstrates up to 255% improvement in blocks covered. Further Ember-IOdiscovered 6 new bugs in the real-world firmware, previously not identified bystate-of-the-art fuzzing frameworks. Importantly, Ember-IO integrated with thestate-of-the-art fuzzer, Fuzzware, demonstrates similar or improved coverageacross all firmware binaries whilst reproducing 3 of the 6 new bugs discoveredby Ember-IO.

Ember-IO: Effective Firmware Fuzzing with Model-Free Memory Mapped IO