Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PartitionGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into privileged and normal codebases, guided by a few annotated sensitive data variables. We evaluated PartitionGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PartitionGPT successfully generates compilable, and verified partitions for 78% of the sensitive functions while reducing approximately 30% code compared to function-level partitioning approach. Furthermore, we evaluated PartitionGPT on nine real-world manipulation attacks that lead to a total loss of 25 million dollars, PartitionGPT effectively prevents eight cases, highlighting its potential for broad applicability and the necessity for secure program partitioning during smart contract development to diminish manipulation vulnerabilities.

本研究针对智能合约中由于敏感信息泄露导致的操作漏洞，提出了PartitionGPT，这是首个结合静态分析与大语言模型（LLM）上下文学习能力的智能合约分区方法。研究表明，PartitionGPT能成功生成78%的敏感功能的可编译和经过验证的代码分区，并有效防止了多起真实世界的操控攻击，显示了其在智能合约开发中的广泛应用潜力。

面向智能合约安全程序分区的LLM上下文学习方法