SQLIFIX: Learning Based Approach to Fix SQL Injection Vulnerabilities in Source Code

Abstract

SQL Injection attack is one of the oldest yet effective attacks for web applications. Even in 2020, applications are vulnerable to SQL Injection attacks. The developers are sup-posed to take precautions such as parameterizing SQL queries, escaping special characters, etc. However, developers, especially inexperienced ones, often fail to comply with such guidelines. There are quite a few SQL Injection detection tools to expose any unattended SQL Injection vulnerability in source code. However, to the best of our knowledge, very few works have been done to suggest a fix of these vulnerabilities in the source code. We have developed a learning-based approach that prepares abstraction of SQL Injection vulnerable codes from training dataset and clusters them using hierarchical clustering. The test samples are matched with a cluster of similar samples and a fix suggestion is generated. We have developed a manually validated training and test dataset from real-world projects of Java and PHP to evaluate our language-agnostic approach. The results establish the superiority of our technique over comparable techniques. The code and dataset are released publicly to encourage reproduction.