Attention Is All You Need for LLM-Based Code Vulnerability Localization

This paper presents LOVA, a framework that improves how AI models find vulnerable code (code with security weaknesses) by using self-attention mechanisms (the components that help AI models figure out which parts of input text are most important). The key idea is that vulnerable lines of code will receive higher attention weights from the model, allowing LOVA to pinpoint security issues more accurately across different programming languages and achieve significantly better performance than existing AI-based approaches.