Modern OS kernels are bloated to support diverse hardware and features, yet most deployments use only a small subset. This paper advances configuration-based kernel debloating to reduce attack surface and code size in systems with known workloads. We present Tracie,a trace-based specialization tool that improves precision through better workload attribution and trace-to-config mapping, achieving a 21.67% reduction—outperforming prior work by 8%—and removing 1 additional CVEs. We also introduce Dice, a novel trace-free, dependency-aware debloating approach that iteratively prunes the configuration graph, achieving 35.59% reduction and eliminating 10 more CVEs. Finally, we empirically analyze the Linux kconfig system and show that structural limitations—such as coarse granularity and rigid dependencies—constrain the effectiveness of configuration-based debloating.

In this paper, we systematically evaluate the robustness of multi-exit language models against adversarial slowdown. To audit their robustness, we design a slowdown attack that generates natural adversarial text bypassing early-exit points. We use the resulting WAFFLE attack as a vehicle to conduct a comprehensive evaluation of three multi-exit mechanisms with the GLUE benchmark against adversarial slowdown. We then show our attack significantly reduces the computational savings provided by the three methods in both white-box and black-box settings. The more complex a mechanism is, the more vulnerable it is to adversarial slowdown. We also perform a linguistic analysis of the perturbed text inputs, identifying common perturbation patterns that our attack generates, and comparing them with standard adversarial text attacks. Moreover, we show that adversarial training is ineffective in defeating our slowdown attack, but input sanitization with a conversational model, e.g., ChatGPT, can remove perturbations effectively. This result suggests that future work is needed for developing efficient yet robust multi-exit models. Our code is available at: https://github.com/ztcoalson/WAFFLE