## 1 Introduction  
SQLI Injection (SQLI) is a critical cybersecurity threat that exploits vulnerabilities in a system's database management by inserting malicious SQL code into queries. This type of attack can lead to severe consequences, including data breaches, system hijacking, and even complete system failure, resulting in substantial financial and reputational losses for both corporations and users. Despite extensive research on SQLI [1, 2, 3, 4, 5, 6, 7, 8, 9], recent studies, such as [10] and the OWASP Top Ten [11], indicate that SQLI continues to be one of the most prevalent attacks on web applications. SQLI scanners, typically used to identify potential SQLI vulnerabilities in web applications, are classified into white-box, gray-box, and black-box testing methods based on the level of visibility and access to the application being tested. White-box testing requires access to the source code, gray-box testing involves partial knowledge of the system's internal structure, and black-box testing is conducted without any knowledge of the application's internals. Consequently, black-box testing is more adept at simulating attacks from an external attacker's perspective. It is suitable for assessing web application vulnerabilities in real-world scenarios, especially when source code access is restricted for third-party testing. Common SQLI black-box detection methods include heuristic detection techniques based on predefined rules and intelligent detection techniques leveraging AI. Heuristic detection techniques rely heavily on the quality of predefined rules and often suffer from a low detection efficiency and accuracy. On the other hand, intelligent detection techniques require complex modeling for SQLI black-box detection and typically face challenges such as the lack of high-quality datasets and poor portability. Large Language Models (LLMs) [12; 13; 14; 15], such as those in the GPT series, have demonstrated substantial capabilities in contextual understanding and reasoning, quick adaptation to downstream tasks, and extensive knowledge storage. These attributes make LLMs a promising avenue for addressing SQLI black-box detection. Previous research, like Pentest-GPT [16], show that LLMs are already being used for penetration testing, indicating their potential in SQLI black-box detection. Yet, the full capabilities of LLMs in this specific area have not been exhaustively investigated, signaling a need for further exploration. To further explore the potential of LLMs in SQLI black-box detection, we designed a specialized micro benchmark called SqliMicroBenchmark, based on Sqli-labs, and conducted exploratory experiments. The findings reveal that LLMs hold considerable potential to guide SQLI black-box scanners. Building on this, we developed SqliGPT, an LLM-powered SQLI black-box scanner, which leverages the advanced contextual understanding and reasoning abilities of LLMs to increase the precision and efficiency of detecting SQLI in a black-box manner. By conducting validation tests of SqliGPT against six leading SQLI scanners from academia and industry, we proved our method's viability and effectiveness in enhancing detection efficiency and accuracy. The results indicate that SqliGPT successfully identified all 40 targets in the SqliMicroBenchmark, surpassing the performance of the other six advanced scanners, particularly against targets with insufficient defense mechanisms. Additionally, SqliGPT showed superior efficiency in its detection tasks. Although it was slightly less effective than Arachni and SQIRL on 22 targets that most scanners managed to detect, it exhibited the best performance on the remaining 18 targets. Moreover, our ablation studies verified the significant contributions of each module to the overall efficacy. In this paper, we begin by reviewing non-LLM- and LLM-based black-box detection methods for SQLI. Subsequently, we state the research motivation and report an exploratory study, which included the experimental setup and evaluation processes.