Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence (AI) and more specifically Large Language Models (LLMs) have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored. Prompting pre-trained LLMs in an effective way offers a computationally effective and scalable solution. Our contributions are (i)varied prompt designs for vulnerability detection and its corresponding reasoning in the wild. (ii)a real-world vector data store constructed from the National Vulnerability Database, that will provide real time context to vulnerability detection framework, and (iii)a scoring measure for combined measurement of accuracy and reasoning quality. Our contribution aims to examine whether LLMs are ready for wild deployment, thus enabling the reliable use of LLMs stronger for the development of secure software's.