An Empirical Study of Automatic Program Repair Techniques for Injection Vulnerabilities

Injection vulnerabilities are among the most serious and dangerous security defects, as they can be exploited by attackers to inject malicious inputs and carry out cybercrimes. Timely fixing of injection vulnerabilities is crucial. However, manual repairs of injection vulnerabilities often require specialized knowledge and are prone to errors, posing a challenge and a heavy burden on developers. In recent years, Automated Program Repair (APR) techniques have shown promising momentum in automatically fixing general defects. Yet, there has been no research on how APR techniques perform in repairing injection vulnerabilities. Therefore, in this paper, we conduct an empirical study. We first construct a benchmark for injection vulnerability repair and evaluate several representative state-of-the-art APR approaches on this benchmark. The results show that existing APR tools do not adequately support the repair of injection vulnerabilities. To investigate the underlying reasons, we compare the characteristics of patches for injection vulnerabilities and general defects, and explore whether the plastic surgery hypothesis widely used in APR still holds for injection vulnerabilities. The results reveal that fixing injection vulnerabilities is more complex than fixing general defects due to significant differences in the characteristics of their patches. Additionally, the support for the plastic surgery hypothesis is much lower in the context of injection vulnerability repair. We also analyzed developers' intentions when fixing injection vulnerabilities. Finally, we summarize the implications and point out potential research directions for injection vulnerability repair.