Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts
Smart contracts on blockchains are prone to diverse security vulnerabilities that can lead to significant financial losses due to their immutable nature. Existing detection approaches often lack flexibility across vulnerability types and rely heavily on manually crafted expert rules. In this paper, we present an LLM-based framework for practical smart contract vulnerability detection. We construct and release a large-scale dataset comprising 31,165 professionally annotated vulnerability instances collected from over 3,200 real-world projects across 15 major blockchain platforms. Our approach leverages precise AST-based context extraction and vulnerability-specific prompt design to instant...
| Authors | Xing Zhang, Keyu Zhang, Taohong Zhu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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