ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

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• ORACAL achieves state-of-the-art performance for smart contract vulnerability detection, with a peak Macro F1 of 91.28% on the primary benchmark.result 95%
• ORACAL maintains strong generalization on out-of-distribution datasets, achieving 91.8% Macro F1 on CGT Weakness and 77.1% on DAppScan.result 95%
• ORACAL is more robust against adversarial attacks than prior methods, limiting performance degradation to a 2.35% F1 decrease with an Attack Success Rate of only 3%.result 90%
• ORACAL integrates a heterogeneous multimodal graph (CFG, DFG, and Call Graph) and enriches critical subgraphs using an LLM-based RAG pipeline to inject expert-level security knowledge.method 90%
• ORACAL employs a causal attention mechanism to disentangle true vulnerability indicators from spurious correlations.method 85%
• The framework provides explainable evidence for its predictions by using PGExplainer to generate subgraph-level explanations identifying vulnerability triggering paths.method 90%
• In explainability evaluation, PGExplainer achieves 32.51% Mean Intersection over Union (MIoU) against manually annotated vulnerability triggering paths.result 90%
• Existing static analysis tools (like Slither and Oyente) are limited by handcrafted rules and struggle to generalize to unseen or evolving attack strategies.limitation 85%

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