Towards Generic Database Management System Fuzzing


Yupeng Yang and Yongheng Chen, Georgia Institute of Technology; Rui Zhong, Palo Alto Networks; Jizhou Chen and Wenke Lee, Georgia Institute of Technology


Database Management Systems play an indispensable role in modern cyberspace. While multiple fuzzing frameworks have been proposed in recent years to test relational (SQL) DBMSs to improve their security, non-relational (NoSQL) DBMSs have yet to experience the same scrutiny and lack an effective testing solution in general. In this work, we identify three limitations of existing approaches when extended to fuzz the DBMSs effectively in general: being non-generic, using static constraints, and generating loose data dependencies. Then, we propose effective solutions to address these limitations. We implement our solutions into an end-to-end fuzzing framework, BUZZBEE, which can effectively fuzz both relational and non-relational DBMSs. BUZZBEE successfully discovered 40 vulnerabilities in eight DBMSs of four different data models, of which 25 have been fixed with 4 new CVEs assigned. In our evaluation, BUZZBEE outperforms state-of-the-art generic fuzzers by up to 177% in terms of code coverage and discovers 30x more bugs than the second-best fuzzer for non-relational DBMSs, while achieving comparable results with specialized SQL fuzzers for the relational counterpart.

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