Augmenting Multi-technique Static Analysis with Large Language Models: A Neuro-symbolic Approach to Smart Contract Vulnerability Detection

Abstract

Smart contracts facilitate and enforce agreements between untrusted parties without trusted intermediaries, but vulnerabilities within contracts can cause severe damage once exploited. Various analysis techniques have been proposed for vulnerability detection, but they are typically limited to specific vulnerability types. While recent frameworks employ aggregation to broaden detection capacity, they often accumulate false positives due to loose integration that fails to resolve underlying conflicts. Meanwhile, the semantic reasoning capacity of large language models (LLMs) has shown promise in detecting vulnerabilities, despite inherent reasoning bottlenecks and hallucinations. Recognizing these challenges, in this work, we propose a novel neuro-symbolic approach, named Ensemble LLM-Assisted Static Analysis (ELSA). ELSA comprises two key modules, namely, LLM-assisted static analysis and analyzer ensemble (the ensemble of multiple LLM-assisted static analyzers), each incorporating two distinct strategies. The LLM-assisted static analysis augments individual analysis techniques with constraint-guided neural semantic reasoning, while the analyzer ensemble resolves conflicting outputs to distill a robust consensus. We evaluate ELSA on a comprehensive benchmark, including three open-source datasets and additional self-constructed Zero-Knowledge Proof-based smart contracts whose complexity poses unique challenges to static analysis. Experimental results demonstrate that our approach achieves an overall improvement of at least 17% over baselines and advanced mainstream approaches, effectively bridging semantic gaps and synergizing the complementary advantages of different analyzers. Furthermore, an ablation study and fine-grained analysis are conducted to investigate the key factors contributing to overall performance gains.