Clara: A Cross-Modal Learning Framework for Enhanced Vulnerability Detection

Clara is a new framework for detecting software vulnerabilities (weaknesses in code that attackers can exploit) by combining information from multiple sources: code written as text sequences and code represented as graphs (visual structures showing how different parts connect). The framework uses two techniques to better blend this information together: a local fusion module that uses learnable prompts (instructions that guide the AI to focus on relevant details) to help different data types interact, and a global fusion module that uses attention mechanisms (ways of deciding what information matters most) to adaptively weigh contributions from each data source.