DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection

arXiv:2405.01202v1 Announce Type: cross
Abstract: Software vulnerability detection is generally supported by automated static analysis tools, which have recently been reinforced by deep learning (DL) models. However, despite the superior performance of DL-based approaches over rule-based ones in research, applying DL approaches to software vulnerability detection in practice remains a challenge due to the complex structure of source code, the black-box nature of DL, and the domain knowledge required to understand and validate the black-box results for addressing tasks after …

analysis arxiv automated cs.cr cs.se deep learning detection framework language large large language model performance research software software vulnerability static analysis tools vulnerability vulnerability detection