Smart contracts are self-executing agreements with code-defined terms enabling trustless blockchain transactions. Their immutability and control over significant financial assets make them attractive attack targets, with vulnerabilities potentially causing catastrophic financial losses. Large Language Models (LLMs) have revolutionized numerous domains with remarkable capabilities in code understanding and problem-solving. Despite these advancements, recent research reveals that LLMs still face significant limitations in accurately detecting complex vulnerabilities in smart contracts. This disparity between the capabilities of LLMs and the stringent requirements of security analysis underscores the necessity for tailored methodologies to enhance LLM-based vulnerability detection strategies. In this paper, we propose Synapse, the first smart contract vulnerability detection framework leveraging thought-augmented LLM and fine-grained analysis under focal context. Specifically, Synapse emulates security researchers' vulnerability discovery workflow, including vulnerability pattern learning, thought instantiation, reasoning, and verification. We employ a Buffer of Vulnerability Reasoning Thoughts (BoVRT) approach for LLMs to learn and apply vulnerability-specific reasoning to concrete contracts, improving detection accuracy. We also leverage specialized reasoning and code models to optimize different stages of the vulnerability detection process. To evaluate Synapse, we collected real-world on-chain contract incidents from security company alerts not covered by existing datasets. Synapse identified 117 previously undiscovered vulnerabilities in on-chain smart contracts, including one critical vulnerability that safeguarded assets totaling $30 million from potential losses.
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