ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment
arXiv:2603.28128v2 Announce Type: replace Abstract: Although Graph Neural Networks (GNNs) have shown promise for smart contract vulnerability detection, they still face significant limitations. Homogeneous graph models fail to capture the interplay between control flow and data dependencies, while heterogeneous graph approaches often lack deep semantic understanding, leaving them susceptible to adversarial attacks. Moreover, most black-box models fail to provide explainable evidence, hindering trust in professional audits. To address these challenges, we propose ORACAL (Observable RAG-enhanced
The increasing complexity and value held in smart contracts necessitate more robust and transparent security measures, especially with the continuous evolution of AI techniques like GNNs.
Improved smart contract vulnerability detection reduces financial risk, fosters greater trust in blockchain technologies, and enhances the security posture of decentralized applications.
The proposed ORACAL framework offers a more explainable and robust method for identifying vulnerabilities, moving beyond black-box models and improving auditability.
- · Blockchain developers
- · Cybersecurity firms
- · Decentralized finance (DeFi) platforms
- · Auditing firms
- · Malicious actors targeting smart contracts
- · Less sophisticated auditing tools
Reduced incidents of smart contract exploits due to more advanced detection.
Increased institutional adoption of blockchain and DeFi due to enhanced security and trust.
Potential for new regulatory frameworks to mandate transparent and explainable AI-driven auditing for smart contracts.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG