VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection

We present VulStyle, a multi-modal software vulnerability detection model that jointly encodes function-level source code, non-terminal Abstract Syntax Tree AST structure, and code stylometry CStyle features. Prior work in code representation primarily leverages token-level models or full AST...

Today'S Cat Is Tomorrow'S Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches

Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing e.g. seen and assumed negatives. As vulnerabilities are discovered across calendar time, labels change and...