Ethereum NFT 智能合约漏洞检测：结合代码切片和 DeepSeek 的知识引导方法

在 450 个 NFT 合约样本上，完整配置的检测器给出了 437 个正标签，正标签率达到 97.1%。

去除外部知识库后，正标签率降至 87.11%；

进一步去除代码切片，正标签率降至 73.78%。

结果表明，聚焦代码上下文和领域约束显著影响检测效果。

Ethereum NFT Smart Contracts: Knowledge-Guided Vulnerability Detection with LLM and Code Slicing

Ethereum non-fungible tokens (NFTs) implement ownership, transfer, authorization, and metadata operations through smart contracts, making contract vulnerabilities a direct risk to digital assets. Existing static analyzers provide efficient rule-based screening but can struggle with application-specific logic, whereas unconstrained large language model analysis may be distracted by irrelevant code or produce inconsistent outputs. We present a vulnerability-detection method that combines vulnerability-focused code slicing, an ERC-721-oriented knowledge base, and constrained DeepSeek analysis. Regular-expression patterns locate candidate statements for reentrancy, integer overflow or underflow, and timestamp dependence. A structure-aware context-window algorithm then extracts line-numbered code slices. DeepSeek analyzes each slice using explicit decision rules and a fixed output schema, and the resulting records support automated batch processing. On 450 NFT contract samples, the full configuration produced 437 positive labels, corresponding to a reported positive-label rate of 97.1%. Removing the external knowledge base reduced this rate to 87.11%, while analyzing complete contracts without the knowledge base reduced it to 73.78%. These results indicate that focused code context and domain constraints materially affect the detector's reported output.

This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Key entities and technical topics under continuous tracking include vulnerability detection, DeepSeek, NFT.

Primary source verification: published directly by arXiv: DeepSeek with first-party authority.