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

Abstract

Automated vulnerability detection in C source code is a long-standing challenge, as traditional static analysis tools often trade precision for scalability and struggle with complex vulnerability patterns. Recently, large language models (LLMs) have shown promising results; yet, most approaches rely on a single code representation, implicitly assuming uniform suitability across vulnerability types. In this work, we systematically study LLM-based vulnerability detection under multiple code representations, including raw source code, Abstract Syntax Trees (AST), and Control Flow Graphs (CFG). Through extensive empirical analysis, we show that different representations exhibit complementary strengths, with specific views being more effective for particular classes of vulnerabilities. Building on these findings, we propose a Representation-Aware Logical Ensemble (RALE) that integrates LLMs trained on different code representations through explicit logical rules and majority voting. While single-representation models achieve up to 73% F1-score, our integrated approach reaches 78% F1-score, with commensurate gains in accuracy, precision, and recall, consistently outperforming individual models. These results highlight the importance of explicitly leveraging complementary code representations when combining LLMs for effective vulnerability detection.