Knowdit is a knowledge-driven, agentic framework that significantly improves smart contract vulnerability detection by modeling shared DeFi semantics and leveraging historical audit knowledge.

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…

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

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 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,…

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…

ORACAL, a novel multimodal framework, achieves state-of-the-art smart contract vulnerability detection by integrating control, data, and call graphs with causal reasoning and LLM-enhanced explainabili…

This systematic mapping survey reviews label-efficient approaches for code vulnerability detection, synthesizing five paradigm families and providing a decision guide to navigate trade-offs.

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…

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…

This paper benchmarks LLMs for smart contract security analysis, concluding that while LLMs show potential, their reliability is limited by lexical bias and requires integration with traditional stati…

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

FixV2W introduces a knowledge graph embedding approach to significantly improve the accuracy of inconsistent CVE-CWE mappings in public vulnerability databases, achieving high prediction rates for exp…

The paper introduces Deniable Covert Asset Transfer (DCAT), a method that stages asset transfers to appear as ordinary, loss-producing DeFi activities, achieving empirical unobservability on major blo…

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

The paper proposes FinSec, a novel four-tier security detection framework, to robustly identify complex financial risks and suspicious dialogue patterns in LLM-powered financial agents, achieving stat…

The paper analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments are heterogeneous, lack clear autonomous execution, and exhibit poor risk…

The paper empirically analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments lack robust autonomous execution and exhibit poor risk-adjust…