The paper introduces Bastet, a novel, high-quality, expert-labeled dataset designed to overcome limitations in existing resources for detecting complex smart contract vulnerabilities in DeFi.

EvoPoC introduces a knowledge-driven agentic system that automates the synthesis of verifiable and economically viable exploits for DeFi smart contracts, achieving high recall and significant revenue…

The paper introduces FAUDITOR, a specialized, self-learning fuzzer that detects complex Monetarily Exploitable Vulnerabilities (MEVuls) in smart contracts by integrating NLP-processed auditor knowledg…

The paper introduces Phoenix, a training-free multi-agent framework that detects code vulnerabilities by synthesizing project-specific behavioral contracts, significantly outperforming existing method…

GoAT-X introduces a novel framework that structures cross-chain smart contract auditing as a Graph of Auditing Thoughts, significantly improving the detection of complex, semantic vulnerabilities in m…

The paper introduces an efficient, lightweight LLM framework for smart contract auditing that decouples the audit process into multiple components, achieving high accuracy while significantly reducing…

The paper introduces Sol2Vy, a framework that enables cross-language knowledge transfer from Solidity to Vyper, allowing effective vulnerability detection in low-resource smart contracts without needi…

This paper outlines a comprehensive research framework for smart contract security, moving beyond simple vulnerability detection to encompass advanced areas like semantic reasoning, automated repair,…

GenDetect introduces a novel framework to rapidly generalize detection rules from single observed DeFi exploits, significantly improving resilience against subsequent, similar 'Imitative Attack Cascad…

ContractShield is a robust multimodal framework that uses a novel three-level fusion mechanism to accurately detect multiple types of vulnerabilities in obfuscated smart contracts, significantly outpe…

This paper measures the prevalence of recurring vulnerability patterns (variants) across multiple AI infrastructure repositories and proposes INFRASCOPE, a framework to automatically detect these vari…

LibScan is an automated framework that detects eight categories of smart contract library misuse by combining LLM-based semantic reasoning with rule-based analysis, achieving 85.15% accuracy on real-w…

The paper introduces an LLM-based framework that uses vulnerability-specific prompting and a large-scale dataset to achieve high-precision, scalable detection of multiple smart contract vulnerabilitie…

The paper proposes a novel nine-dimension risk assessment framework for institutional DeFi adoption, significantly enhancing existing methodologies by incorporating novel dimensions like composability…

The paper introduces AgentFlow, a novel framework that uses a typed graph DSL and feedback-driven optimization to automatically synthesize and improve multi-agent harnesses for discovering security vu…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

The paper proposes a novel '3+1' heterogeneous multi-agent architecture using cloud LLMs and a local verifier to achieve high-accuracy, cost-effective code vulnerability detection, significantly outpe…

The paper introduces MEV non-interference, a formal security notion, to ensure that composing new smart contracts in DeFi does not increase the maximal extractable value, thereby providing a formal fo…

Semia is a novel static auditor that translates complex, prose-defined agent skills into a verifiable Datalog fact base, enabling the detection of critical security vulnerabilities in real-world LLM a…

The paper introduces Oracle Poisoning, an attack that corrupts knowledge graphs used by AI agents, demonstrating that all tested models blindly trust poisoned data at high sophistication levels.