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Binary firmware fuzzing has garnered attention in recent years. Compared to source-code-based approaches, binary approaches require less semantic information and are there- fore more applicable. This is particularly relevant in firmware analysis, as most firmware vendors distribute only binaries, withholding source code due to proprietary concerns. Pivoting away from the traditional hardware-in-the-loop (HiL) methodology, researchers are exploring more efficient ways to engage real hardware for fuzzing. However, existing approaches have inherent drawbacks, such as reliance on high-end hardware features, inability to recover complete coverage, and slow execu- tion speeds. We propose DRIFT, a novel approach for on-device binary firmware testing that follows the semihosting methodology. DRIFT addresses all the aforementioned drawbacks. The core insight of DRIFT is to use the Debug Monitor (DM) for firmware fuzzing. DM is a Arm Cortex-M CPU feature that allows triggering interrupt when a breakpoint is hit. Through chaining the DM interrupts, DRIFT is able let firmware to trace itself. This self-tracing approach minimizes interference from the workstation, significantly boosting fuzzing performance. We designed DRIFT to be highly flexible, accommodating a number of hardware resource limitations. When applied to new firmware, DRIFT discovered three previously unknown bugs that were not identified by existing binary fuzzing techniques. Furthermore, DRIFT outperforms all state-of-the-art binary firmware fuzzers in terms of speed and fidelity, trailing only SHiFT, an approach that requires source code. 

Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimization tasks or active search under constraints. The existing constrained multi-objective algorithms address the issue with heuristics and approximations, posing challenges to the analysis of the sample efficiency. We propose a novel constrained multi-objective Bayesian optimization algorithm COMBOO that balances active learning of the level-set defined on multiple unknowns with multi-objective optimization within the feasible region. We provide both theoretical analysis and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.