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

arXiv: https://arxiv.org/abs/2502.14215 · HTML: https://mlsanity.com/paper/2502.14215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 90 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023)
- [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018)
- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
- [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](https://mlsanity.com/paper/1807.03932/) (2018)
- [SoK: Decentralized Finance (DeFi) Attacks](https://mlsanity.com/paper/2208.13035/) (2022)
- [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts](https://mlsanity.com/paper/2004.08563/) (2020)
- [Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks](https://mlsanity.com/paper/1812.05934/) (2018)
- [ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts](https://mlsanity.com/paper/2403.06838/) (2024)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution](https://mlsanity.com/paper/1804.05141/) (2018)
- [SmartInv: Multimodal Learning for Smart Contract Invariant Inference](https://mlsanity.com/paper/2411.09217/) (2024)
- [DeFiRanger: Detecting Price Manipulation Attacks on DeFi Applications](https://mlsanity.com/paper/2104.15068/) (2021)
- [Demystifying Invariant Effectiveness for Securing Smart Contracts](https://mlsanity.com/paper/2404.14580/) (2024)
- [sGUARD: Towards Fixing Vulnerable Smart Contracts Automatically](https://mlsanity.com/paper/2101.01917/) (2021)
- [Precise Attack Synthesis for Smart Contracts](https://mlsanity.com/paper/1902.06067/) (2019)
- [Smart Contract Repair](https://mlsanity.com/paper/1912.05823/) (2019)

## Built on by (most cited first, 2 of 2)

- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [Design-Specification Tiling for ICL-based CAD Code Generation](https://mlsanity.com/paper/2603.12712/) (2026)

## Abstract (arXiv)

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.