## 7 Discussion  
**Hallucination in LLMs**: Hallucination [51] in LLMs refers to errors or inaccuracies in the text generated by LLMs, stemming from flaws in the training data, the training process, and the inference process. This phenomenon can affect the reliability of our automated SQLI black-box scanner. Our experiments used closed-source commercial models such as GPT-3.5, GPT-4, and Claude-3. Since we could not directly access or modify the training data and model parameters, we employed CoT and Self-Correcting [52] techniques at the prompts to minimize hallucinations as much as possible. At the same time, we are constantly exploring other methods to reduce hallucination and further optimize the tool's performance. **High Computational and Financial Costs of advanced LLMs**: The high computational and financial costs associated with advanced LLMs such as GPT and Claude constitute one of the limitations in practical applications. These models require considerable computational resources for reasoning, and the financial expenditures can be noticeable, particularly in large-scale concurrent processing scenarios. For large-scale applications, these costs may need to be revised. To reduce costs and increase feasibility, we plan to actively explore the use of open-source LLMs in our subsequent work. These open-source models can be tuned and optimized to meet specific needs and significantly reduce the costs associated with their use, thus enhancing the utility of our SQLI black-box detection tool. **Challenges for LLMs in Bypassing Defense Mechanisms**: LLMs face several significant challenges when attempting to bypass defense mechanisms like WAFs or keyword filters. These mechanisms might process detected malicious payloads in unique ways, such as removing, replacing keywords, or redirecting requests to an error page. Consequently, LLMs struggle to accurately determine if a keyword has been manipulated, leading to a high incidence of false positives. This issue primarily stems from two factors: firstly, SqliGPT's inadequacy in providing enough information related to targeted web applications to LLMs; secondly, LLMs in use may lack the deep logical reasoning required for precise judgment. Addressing these challenges is a pivotal focus of our future research. **Integrating LLMs with other Automated Scanners**: SqliGPT merges LLMs and Sqlmap to create an efficient, intelligent, and robust automated SQLI black-box scanner. Sqlmap was selected due to its extensive feature set, ease of integration, and broad community acceptance. However, Sqlmap does have some limitations, including inadequate support for certain database types and potential issues with missed detections or false positives in specific scenarios. Thus, our framework plans to incorporate additional automated SQLI scanners to address diverse scenarios better and meet particular requirements. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scanner**} & \multicolumn{3}{c}{**Avg Requests per Successful Target**} & \multirow{2}{*}{**Basic**} & \multirow{2}{*}{**Advanced**} & \multirow{2}{*}{**Total (45)**} \\  & & **Class-1** & & & & & **Targets (28)** \\ \hline \multirow{4}{*}{LLM4 Sqli-Sqlmap} & GPT-3.5 & 609 & 6712 & – & 26 (93\%) & 4 (24\%) & 30 (67\%) \\  & GPT-4 & 517 & 8451 & – & 27 (96\%) & 4 (24\%) & 31 (69\%) \\  & Claude-3 & 504 & 11436 & – & 26 (93\%) & 4 (24\%) & 30 (67\%) \\  & GPT-3.5 & 142 & 788 & – & **28 (100\%)** & 4 (24\%) & 32 (71\%) \\ SqliGPT-Strategy & GPT-4 & 119 & 773 & – & **28 (100\%)** & 4 (24\%) & 32 (71\%) \\  & Claude-3 & **112** & **731** & – & **28 (100\%)** & 4 (24\%) & 32 (71\%) \\  & GPT-3.5 & 127 & 761 & 1684 & **28 (100\%)** & **17 (100\%)** & **45 (100\%)** \\ SqliGPT & GPT-4 & 119 & 822 & 1681 & **28 (100\%)** & **17 (100\%)** & **45 (100\%)** \\  & Claude-3 & 113 & **749** & **1669** & 28 (100\%)** & **17 (100\%)** & **45 (100\%)** \\ \hline \hline \end{tabular}  
* The best performance for each metric is highlighted in bold font.  
\end{table}