A responsible AI–driven framework for robust and transparent software vulnerability detection

Software vulnerability detection (SVD) is increasingly challenged by the scale and complexity of modern software systems. Although deep learning–based approaches demonstrate strong detection performance, their adoption in security-critical settings is limited by insufficient interpretability, adversarial resilience, and systematic Responsible AI integration. This paper aims to design and evaluate a Responsible AI–driven framework for SVD that operationalizes fairness, interpretability, security, reliability, and transparency without compromising detection effectiveness. We propose a model-agnostic vulnerability detection framework that incorporates fairness-aware data preprocessing, multi-model evaluation, and structured verification mechanisms. Interpretability is achieved through multi-view explanations combining global and local SHAP, LIME, and attention-based attribution. Explanation consistency is examined using attention-guided token occlusion, while security is evaluated via multiple white-box adversarial attacks on correctly classified test samples. The framework is validated on three public datasets—CWE-119, CWE-399, and DiverseVul—using deep learning and pre-trained code models. Experimental results demonstrate competitive detection performance across datasets while providing structured explanation and adversarial evaluation evidence. Interpretability analyses reveal dataset-specific vulnerability cues aligned with domain knowledge, and perturbation studies show consistent confidence shifts under controlled token masking. Adversarial experiments further illustrate stable performance trends under attack conditions. The findings indicate that Responsible AI principles can be systematically operationalized within deep learning–based SVD pipelines. By integrating fairness, interpretability, security evaluation, reliability assessment, and transparency mechanisms, the proposed framework supports trustworthy and security-aware deployment of AI-driven vulnerability detection systems.