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…

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

This paper evaluates modern reentrancy detection tools, finding that leading LLMs significantly outperform most existing static analyzers and ML models on both real-world and handcrafted benchmarks.

The paper introduces Sentinel, a novel proxy-based system that achieves comprehensive, type-agnostic reentrancy protection for smart contracts by intercepting all external calls.

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…

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

The paper introduces PSR extsuperscript{2}, a novel static analysis framework that significantly improves the detection of atomicity violations in smart contracts by combining structural path searchin…

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,…

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…

The paper proposes VulGNN, a lightweight Graph Neural Network (GNN) model, which achieves vulnerability detection performance comparable to large language models (LLMs) while being significantly small…

The paper proposes a graph-learning approach to predict multi-vulnerability attack chains within software supply chains, achieving high accuracy on both component classification and cascade prediction…

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…

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 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…

ZERO-APT introduces a novel closed-loop adversarial framework for automated penetration testing that simulates attacks against an intelligent, real-time defending system, achieving a high attack succe…

COBALT-TLA introduces a neuro-symbolic verification loop that successfully and autonomously discovers novel cross-chain bridge vulnerabilities by integrating an LLM with the TLA+ model checker.

The paper analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

The paper provides the first machine-checked, tridirectional correctness proof of the OpenZeppelin reentrancy-guard pattern against complex, production-deployed Solidity smart contract source.

The paper proposes PRAETORIAN, a novel defense mechanism for Graph Neural Networks (GNNs) that targets the intrinsic structural requirements of backdoor attacks, significantly reducing the attack succ…

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