Keisuke Nishimura, Yuichi Sugiyama, Yuki Koike, Masaya Motoda, Tomoya Kitagawa, Toshiki Takatera, Yuma Kurogome (Ricerca Security, Inc.)

Fuzzing has contributed to automatically identifying bugs and vulnerabilities in the software testing field. Although it can efficiently generate crashing inputs, these inputs are usually analyzed manually. Several root cause analysis (RCA) techniques have been proposed to automatically analyze the root causes of crashes to mitigate this cost. However, outstanding challenges for realizing more elaborate RCA techniques remain unknown owing to the lack of extensive evaluation methods over existing techniques. With this problem in mind, we developed an end-to-end benchmarking platform, RCABench, that can evaluate RCA techniques for various targeted programs in a detailed and comprehensive manner. Our experiments with RCABench indicated that the evaluations in previous studies were not enough to fully support their claims. Moreover, this platform can be leveraged to evaluate emerging RCA techniques by comparing them with existing techniques.

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Ryo Ichikawa, Captain of CTF Team TokyoWesterns

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LOKI: State-Aware Fuzzing Framework for the Implementation of Blockchain...

Fuchen Ma (Tsinghua University), Yuanliang Chen (Tsinghua University), Meng Ren (Tsinghua University), Yuanhang Zhou (Tsinghua University), Yu Jiang (Tsinghua University), Ting Chen (University of Electronic Science and Technology of China), Huizhong Li (WeBank), Jiaguang Sun (School of Software, Tsinghua University)

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Enhancing Symbolic Execution by Machine Learning Based Solver Selection

Sheng-Han Wen (National Taiwan University), Wei-Loon Mow (National Taiwan University), Wei-Ning Chen (National Taiwan University), Chien-Yuan Wang (National Taiwan University), Hsu-Chun Hsiao (National Taiwan University)

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FitM: Binary-Only Coverage-GuidedFuzzing for Stateful Network Protocols

Dominik Maier, Otto Bittner, Marc Munier, Julian Beier (TU Berlin)

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