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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VICEROY: GDPR-/CCPA-compliant Enforcement of Verifiable Accountless Consumer Requests

Scott Jordan (University of California, Irvine), Yoshimichi Nakatsuka (University of California, Irvine), Ercan Ozturk (University of California, Irvine), Andrew Paverd (Microsoft Research), Gene Tsudik (University of California, Irvine)

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dewolf: Improving Decompilation by leveraging User Surveys

Steffen Enders, Eva-Maria C. Behner, Niklas Bergmann, Mariia Rybalka, Elmar Padilla (Fraunhofer FKIE, Germany), Er Xue Hui, Henry Low, Nicholas Sim (DSO National Laboratories, Singapore)

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Trellis: Robust and Scalable Metadata-private Anonymous Broadcast

Simon Langowski (Massachusetts Institute of Technology), Sacha Servan-Schreiber (Massachusetts Institute of Technology), Srinivas Devadas (Massachusetts Institute of Technology)

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