The field of software vulnerability detection is witnessing significant advancements with the integration of AI-based systems. Recent developments focus on improving the generalizability and robustness of these systems, enabling them to detect vulnerabilities across diverse software projects and codebases. A key direction is the emphasis on data quality and model architecture, with studies demonstrating that enhancements in dataset diversity and quality substantially improve detection performance. Additionally, the use of counterfactual augmentation and graph neural networks is being explored to mitigate spurious correlations and improve the interpretability of vulnerability detection models. Noteworthy papers include:

• Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection, which highlights the importance of data quality and model selection in developing robust vulnerability detection systems.
• VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation, which proposes a unified framework for robust and interpretable vulnerability detection using counterfactual augmentation and graph neural networks.