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

Mohammed Latif Siddiq, Md. Rezwanur Rahman Jahin, Mohammad Rafid Ul Islam, Rifat Shahriyar, Anindya Iqbal

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.