# Prompt to Pwn: Automated Exploit Generation for Smart Contracts

arXiv: https://arxiv.org/abs/2508.01371 · HTML: https://mlsanity.com/paper/2508.01371/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 30 of 31)

- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [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)
- [Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback](https://mlsanity.com/paper/2403.16792/) (2024)
- [When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?](https://mlsanity.com/paper/2309.05520/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Do you still need a manual smart contract audit?](https://mlsanity.com/paper/2306.12338/) (2023)
- [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](https://mlsanity.com/paper/1807.03932/) (2018)
- [Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking](https://mlsanity.com/paper/2407.02395/) (2024)
- [Finding The Greedy, Prodigal, and Suicidal Contracts at Scale](https://mlsanity.com/paper/1802.06038/) (2018)
- [SmartBugs: A Framework to Analyze Solidity Smart Contracts](https://mlsanity.com/paper/2007.04771/) (2020)
- [Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)](https://mlsanity.com/paper/2206.08202/) (2022)
- [A Survey of Smart Contract Formal Specification and Verification](https://mlsanity.com/paper/2008.02712/) (2020)
- [The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts](https://mlsanity.com/paper/1902.06976/) (2019)
- [Large Language Models for Blockchain Security: A Systematic Literature Review](https://mlsanity.com/paper/2403.14280/) (2024)
- [Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach](https://mlsanity.com/paper/2505.23953/) (2025)
- [SoK: Transparent Dishonesty: front-running attacks on Blockchain](https://mlsanity.com/paper/1902.05164/) (2019)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [SoK: Semantic Privacy in Large Language Models](https://mlsanity.com/paper/2506.23603/) (2025)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning](https://mlsanity.com/paper/2502.14215/) (2025)
- [A Comprehensive Survey of Upgradeable Smart Contract Patterns](https://mlsanity.com/paper/2304.03405/) (2023)

## Built on by (most cited first, 4 of 4)

- [PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2511.02780/) (2025)
- [Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization](https://mlsanity.com/paper/2603.26270/) (2026)
- [LLMs as verification oracles for Solidity](https://mlsanity.com/paper/2509.19153/) (2025)
- [Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts](https://mlsanity.com/paper/2607.15673/) (2026)

## Abstract (arXiv)

Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart contract vulnerability detection, leaving end-to-end automated exploit generation (AEG) much less understood. We study that gap with textscReX, an execution-grounded framework that links LLM-based exploit synthesis to the Foundry stack for end-to-end generation, compilation, execution, and validation. Five recent LLMs are evaluated across eight common vulnerability classes, supported by a curated dataset of 38+ real incident PoCs and three automation aids: prompt refactoring, a compiler feedback loop, and templated test harnesses. Results indicate that current frontier LLMs can often produce deterministic PoCs for single-contract vulnerabilities, but remain weak on cross-contract attacks; outcomes depend mainly on the model and bug type, while code structure and prompt tuning contribute less in our setting. The study also surfaces important boundary conditions of LLM-driven AEG, including gaps between oracle-validated exploitability and real-world economic attacks, pointing to the need for stronger defenses and more realistic evaluation.