Lei Zhao (Wuhan University), Yue Duan (University of California, Riverside), Heng Yin (University of California, Riverside), Jifeng Xuan (Wuhan University)

Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-the-art hybrid fuzzing systems deploy ``demand launch'' and ``optimal switch'' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel ``discriminative dispatch'' strategy to better utilize the capability of concolic execution. We design a novel Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats fuzzing as a random sampling process. It calculates each path's probability based on the sampling information. Finally, our model prioritizes and assigns the most difficult paths to concolic execution. We implement a prototype system DigFuzz and evaluate our system with two representative datasets. Results show that the concolic execution in DigFuzz outperforms than that in a state-of-the-art hybrid fuzzing system Driller in every major aspect. In particular, the concolic execution in DigFuzz contributes to discovering more vulnerabilities (12 vs. 5) and producing more code coverage (18.9% vs. 3.8%) on the CQE dataset than the concolic execution in Driller.

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Shiqi Shen (National University of Singapore), Shweta Shinde (National University of Singapore), Soundarya Ramesh (National University of Singapore), Abhik Roychoudhury (National University of Singapore), Prateek Saxena (National University of Singapore)

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Ke Coby Wang (UNC Chapel Hill), Michael K. Reiter (UNC Chapel Hill)

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Daimeng Wang (University of California Riverside), Ajaya Neupane (University of California Riverside), Zhiyun Qian (University of California Riverside), Nael Abu-Ghazaleh (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Edward J. M. Colbert (Virginia Tech), Paul Yu (U.S. Army Research Lab (ARL))

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Wajih Ul Hassan (NEC Laboratories America, Inc.; University of Illinois at Urbana–Champaign), Shengjian Guo (Virginia Tech), Ding Li (NEC Laboratories America, Inc.), Zhengzhang Chen (NEC Laboratories America, Inc.), Kangkook Jee (NEC Laboratories America, Inc.), Zhichun Li (NEC Laboratories America, Inc.), Adam Bates (University of Illinois at Urbana–Champaign)

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