An Chen (University of Georgia), Jiho Lee (University of Virginia), Basanta Chaulagain (University of Georgia), Yonghwi Kwon (University of Virginia), Kyu Hyung Lee (University of Georgia)

Testing database-backed web applications is challenging because their behaviors (e.g., control flow) are highly dependent on data returned from SQL queries. Without a database containing sufficient and realistic data, it is challenging to reach potentially vulnerable code snippets, limiting various existing dynamic-based security testing approaches. However, obtaining such a database for testing is difficult in practice as it often contains sensitive information. Sharing it can lead to data leaks and privacy issues.

In this paper, we present SYNTHDB, a program analysis-based database generation technique for database-backed PHP applications. SYNTHDB leverages a concolic execution engine to identify interactions between PHP codebase and the SQL queries. It then collects and solves various constraints to reconstruct a database that can enable exploring uncovered program paths without violating database integrity. Our evaluation results show that the database generated by SYNTHDB outperforms state-of-the-arts database generation techniques in terms of code and query coverage in 17 real-world PHP applications. Specifically, SYNTHDB generated databases achieve 62.9% code and 77.1% query coverages, which are 14.0% and 24.2% more in code and query coverages than the state-of-the-art techniques. Furthermore, our security analysis results show that SYNTHDB effectively aids existing security testing tools: Burp Suite, Wfuzz, and webFuzz. Burp Suite aided by SYNTHDB detects 76.8% of vulnerabilities while other existing techniques cover 55.7% or fewer. Impressively, with SYNTHDB, Burp Suite discovers 33 previously unknown vulnerabilities from 5 real-world applications.

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Xiang Li (Tsinghua University), Baojun Liu (Tsinghua University), Xuesong Bai (University of California, Irvine), Mingming Zhang (Tsinghua University), Qifan Zhang (University of California, Irvine), Zhou Li (University of California, Irvine), Haixin Duan (Tsinghua University; QI-ANXIN Technology Research Institute; Zhongguancun Laboratory), Qi Li (Tsinghua University; Zhongguancun Laboratory)

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Tian Dong (Shanghai Jiao Tong University), Shaofeng Li (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Haojin Zhu (Shanghai Jiao Tong University), Zhen Liu (Shanghai Jiao Tong University)

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Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

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