Large language model based hybrid framework for automatic vulnerability detection with explainable AI for cybersecurity enhancement

Organizations nowadays rely on intensive software systems to support their business operations but vulnerabilities within these systems can cause potential risks for major disruption. AI-based techniques are now widely considered for vulnera-bility identification; however effectiveness heavily relies on the dataset’s size and quality. These techniques often lack contextual information while processing data and pose challenges in resource-constrained environments. AI models are generally black box in nature which creates additional challenges to understand decision making processes. This work proposes a novel hybrid framework using LLM model based on CodeBERT with integration of fine-tuning and Model-Agnostic Meta-Learning for performing effective vulnerability detection. It includes few-shot learning technique for new vulnerability detection tasks while maintaining high performance on known cases. The approach adopts Explainable AI techniques from four dimensions including attention mechanisms, layer-wise analysis, feature contribution, and model confidence scores to explain model decision making. An experiment demonstrates the framework’s effectiveness, show-ing steady decrease in meta-loss from 0.45 to 0.14, accompanied by increase in support accuracy from 85.2% to 92.5%. These findings establish the proposed framework as a robust and interpretable solution for vulnerability detection and management.