YURASCANNER: Leveraging LLMs for Task-driven Web App Scanning This study addresses this limitation by introducing YURASCANNER, a task-driven web application scanner that leverages large-language models (LLMs) to autonomously execute tasks and workflows
YuraScanner: Leveraging LLMs for Task-driven Web App Scanning This study addresses this limitation by introducing Y URA S CANNER , a task-driven web application scanner that leverages large-language models (LLMs) to autonomously execute tasks and workflows
GitHub - pixelindigo yurascanner: YuraScanner · GitHub This repository contains the code of YuraScanner --- an LLM-powered web application scanner, first presented at NDSS 2025: YuraScanner: Leveraging LLMs for Task-driven Web App Scanning
YuraScanner: Leveraging LLMs for Task-driven Web App Scanning This study addresses this limitation by introducing YuraScanner, a task-driven web application scanner that leverages large-language models (LLMs) to autonomously execute tasks and workflows
NDSS 2025 – YuraScanner: Leveraging LLMs For Task-driven Web App Scanning4+ This LLM-based approach allows YuraScanner to bridge the semantic gap, making it versatile across different web applications It utilizes the XSS engine of Black Widow to test discovered input points for vulnerabilities, thereby enhancing the thoroughness and precision of the scanning process
YuraScanner - god. owasp. de Instead of training a model, we opted to use large language models (LLMs) Non-academic approaches have proposed LLM-based browsing agents to assist users with tasks [2, 3] E g , “Book a hotel in Düsseldorf” Instead, we want to complete workflows and reach deeper states in web applications without user interaction