Towards Secure Program Partitioning for Smart Contracts with LLM’s In-Context Learning

Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, and David Lo

IEEE Transactions on Software Engineering, 2026

Abstract: 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, achieving a precision of 80% while reducing more than 26% code compared to function-level partitioning approach. Furthermore, we evaluated PARTITIONGPT on nine real-world manipulation attacks that led 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.

Cite:

@article{Liu2026TSP,
  author = {Liu, Ye and Niu, Yuqing and Ma, Chengyan and Han, Ruidong and Ma, Wei and Li, Yi and Gao, Debin and Lo, David},
  journal = {IEEE Transactions on Software Engineering},
  month = feb,
  title = {Towards Secure Program Partitioning for Smart Contracts with {LLM}'s In-Context Learning},
  year = {2026}
}