Enhancing LLM-Based Vulnerability Detection via Representation-Aware Logical Ensemble

Summary

This research studies LLM-based vulnerability detection in C source code across three code representations: raw source, Abstract Syntax Trees (AST), and Control Flow Graphs (CFG). The authors propose a Representation-Aware Logical Ensemble (RALE) that combines LLMs trained on different representations using explicit logical rules and majority voting. RALE reaches a 78% F1-score, compared with up to 73% for single-representation models.