Zenong Zhang (University of Texas at Dallas), George Klees (University of Maryland), Eric Wang (Poolesville High School), Michael Hicks (University of Maryland), Shiyi Wei (University of Texas at Dallas)

While many real-world programs are shipped with configurations to enable/disable functionalities, fuzzers have mostly been applied to test single configurations of these programs. In this work, we first conduct an empirical study to understand how program configurations affect fuzzing performance. We find that limiting a campaign to a single configuration can result in failing to cover a significant amount of code. We also observe that different program configurations contribute differing amounts of code coverage, challenging the idea that each one can be efficiently fuzzed individually. Motivated by these two observations we propose ConfigFuzz, which can fuzz configurations along with normal inputs. ConfigFuzz transforms the target program to encode its program options within part of the fuzzable input, so existing fuzzers’ mutation operators can be reused to fuzz program configurations. We instantiate ConfigFuzz on 3 configurable, common fuzzing targets, and integrate their executions in FuzzBench. In our preliminary evaluation, ConfigFuzz nearly always outperforms the baseline fuzzing of a single configuration, and in one target also outperforms the fuzzing of a sequence of sampled configurations. However, we find that sometimes fuzzing a sequence of sampled configurations, with shared seeds, improves on ConfigFuzz. We propose hypotheses and plan to use data visualization to further understand the behavior of ConfigFuzz, and refine it, in the full evaluation.

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Aditya Singh Rathore (University at Buffalo, SUNY), Yijie Shen (Zhejiang University), Chenhan Xu (University at Buffalo, SUNY), Jacob Snyderman (University at Buffalo, SUNY), Jinsong Han (Zhejiang University), Fan Zhang (Zhejiang University), Zhengxiong Li (University of Colorado Denver), Feng Lin (Zhejiang University), Wenyao Xu (University at Buffalo, SUNY), Kui Ren (Zhejiang University)

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Demo #15: Remote Adversarial Attack on Automated Lane Centering

Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

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Andreas Zeller (CISPA Helmholtz Center for Information Security)

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Adrian Herrera (Australian National University), Mathias Payer (EPFL), Antony Hosking (Australian National University)

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