SQIRL: Grey-Box Detection of SQL Injection Vulnerabilities Using Reinforcement Learning

Web security scanners are used to discover SQL injection vulnerabilities in deployed web applications. Scanners tend to use static rules to cover the most common injection cases, missing diversity in their payloads, leading to a high volume of requests and false negatives. Moreover, scanners often rely on the presence of error messages or other significant feedback on the target web pages, as a result of additional insecure programming practices by web developers.

In this paper we develop SQIRL, a novel approach to detecting SQL injection vulnerabilities based on deep reinforcement learning, using multiple worker agents and grey-box feedback. Each worker intelligently fuzzes the input fields discovered by an automated crawling component. This approach generates a more varied set of payloads than existing scanners, leading to the discovery of more vulnerabilities. Moreover, SQIRL attempts fewer payloads, because they are generated in a targeted fashion.

SQIRL finds all vulnerabilities in our microbenchmark for SQL injection, with substantially fewer requests than most of the state-of-the-art scanners compared with. It also significantly outperforms other scanners on a set of 14 production grade web applications, discovering 33 vulnerabilities, with zero false positives. We have responsibly disclosed 22 novel vulnerabilities found by SQIRL, grouped in 6 CVEs.