Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning
This addresses smart contract security vulnerabilities for blockchain developers, though it appears incremental as it builds on existing partitioning concepts with LLM enhancement.
The paper tackles smart contract manipulation vulnerabilities by presenting PartitionGPT, an LLM-driven approach that partitions contracts into privileged and normal codebases to prevent sensitive information leakage. The results show it generates compilable partitions for 78% of sensitive functions, reduces code by 30% compared to baseline, and prevents 8 out of 9 real-world attacks that caused $25 million in losses.
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.