# An MLLM-Assisted Web Crawler Approach for Web Application Fuzzing
## 6 Discussion  
**Advantages and Innovation. ** CrawlMLM significantly enhances test request coverage through page state mining, functionality analysis, and automated operation generation during the crawling phase, providing high-quality test cases for Web application fuzzing. Evaluation results demonstrate that CrawlMLM exhibits notable performance advantages compared to Burp, Black Widow, and Rnd BFS and URL path algorithms. **Adaptability. ** CrawlMLM adapts to various web applications beyond the initial dataset, not just PHP-written web applications, but equally adapts to Java, PHP, and Node.js applications. **Intelligent Interaction Capabilities. ** CrawlMLM is an intelligent crawler system capable of understanding webpage content and structure. The system not only generates appropriate access instructions for specific tasks but also simulates complex user interaction behaviors, including clicking and scrolling. Through this intelligent simulation of real user browsing patterns, CrawlMLM's automated test scripts are more difficult for anti-crawling mechanisms to detect compared to traditional random crawlers. **Current Limitations. ** CrawlMLM exhibits several constraints in webpage interaction scenarios. While demonstrating significant improvements in complex web applications, it shows a minimal advantage over traditional crawler methods when handling simple applications that require neither inter-page relationship analysis nor form content validation. The system faces challenges in effectively identifying and addressing vulnerabilities triggered by invisible parameters, primarily due to the MLLM's limited access to contextual information regarding hidden elements and dynamically generated content. The MLLM encounters difficulties in fully simulating and capturing the complete lifecycle of invisible parameters generated through user interactions. Furthermore, the model  
Figure 9: Success Rate of Operation Code Generation by MLLMs. Our experimental evaluation encompassed three categories of tasks: simple tasks (completable within five operations), medium-complexity tasks (requiring 5–10 operations), and complex tasks (requiring more than 10 operations). demonstrates low accuracy in simulating time-sensitive scenarios involving parameters dynamically generated under specific temporal or conditional constraints. Additionally, CrawlMLM presents limitations in state tracking and historical interaction data retention. Although computational overhead increases with application complexity, the framework maintains operational efficiency through its modular architecture and MLLM's capability to decompose complex pages into analyzable components. The increasing complexity of web applications presents challenges in generating comprehensive state machines for page state mining and complete task descriptions for functionality analysis. **Concerning computational resources and deployment. ** Our framework utilizes GPT-4o through API calls rather than requiring local MLLM deployment. This architectural choice significantly reduces computational resource requirements and simplifies deployment. The system can be effectively operated on standard development hardware, as the primary computational load is handled by the external API service. **Future Improvements. ** Despite CrawlMLM's robust performance in our evaluation, several avenues for optimization remain. The primary enhancement involves strengthening state tracking capabilities through the introduction of state recovery techniques to achieve precise system state management and implementing systematic page decomposition for complex web applications. Furthermore, the system requires improved capabilities for accessing contextual information of hidden elements and dynamically generated content, coupled with enhanced reasoning capabilities for request parameters and their values. Another crucial optimization direction involves incorporating a memory mechanism for modeling historical interactions. In terms of vulnerability detection, scanning tools have inherent limitations.