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 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 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…

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,…

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 SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier models can produce compilab…

The paper introduces SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier LLMs can generate compilabl…

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…

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 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 SseRex, a novel symbolic execution framework designed to detect unique and complex vulnerabilities in Solana smart contracts, significantly outperforming existing tools.

The paper introduces codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

This paper replicates and extends a study on Java security API misuse in LLMs, finding that while newer models improve performance, the misuse risk persists and is significantly mitigated by external…

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 evaluates code language model-based detection of vulnerability-fixing commits (VFCs) using a unified benchmark and concludes that code changes alone are insufficient for accurate detection,…

The paper introduces Semantic Compliance Hijacking (SCH), a novel payload-less attack that exploits LLM agent supply chains by manipulating compliance rules to force unauthorized code generation, achi…

The paper introduces KindHML, an automated formal verification approach that uses Hennessy-Milner Logic and the Kind 2 model checker to verify complex temporal properties of smart contracts, detecting…

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 Neuroforger, a system that combines a new formal specification language with LLMs and type checking to reliably generate and validate concrete violation witnesses (counterexamples…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…