Visible to the public Automated Combinatorial Testing for Detecting SQL Vulnerabilities in Web Applications

TitleAutomated Combinatorial Testing for Detecting SQL Vulnerabilities in Web Applications
Publication TypeConference Paper
Year of Publication2019
AuthorsSimos, Dimitris E., Zivanovic, Jovan, Leithner, Manuel
Conference Name2019 IEEE/ACM 14th International Workshop on Automation of Software Test (AST)
Date PublishedMay 2019
PublisherIEEE
ISBN Number978-1-7281-2237-3
Keywordsattack grammars, automated combinatorial testing, automated SQL injection vulnerability testing, Collaboration, combinatorial testing, combinatorial testing methodology, concrete attack vectors, Databases, Grammar, gray-box testing, gray-box testing method, Human Behavior, Internet, Metrics, policy-based governance, privacy, program testing, pubcrawl, resilience, Resiliency, security of data, security testing, SQL, SQL database backend, SQL detection, SQL Injection, SQL injection vulnerabilities, SQL vulnerabilities detection, Syntactics, Tools, Web applications
Abstract

In this paper, we present a combinatorial testing methodology for testing web applications in regards to SQL injection vulnerabilities. We describe three attack grammars that were developed and used to generate concrete attack vectors. Furthermore, we present and evaluate two different oracles used to observe the application's behavior when subjected to such attack vectors. We also present a prototype tool called SQLInjector capable of automated SQL injection vulnerability testing for web applications. The developed methodology can be applied to any web application that uses server side scripting and HTML for handling user input and has a SQL database backend. Our approach relies on the use of a database proxy, making this a gray-box testing method. We establish the effectiveness of the proposed tool with the WAVSEP verification framework and conduct a case study on real-world web applications, where we are able to discover both known vulnerabilities and additional previously undiscovered flaws.

URLhttps://ieeexplore.ieee.org/document/8821969
DOI10.1109/AST.2019.00014
Citation Keysimos_automated_2019