标题： 提示到入侵：智能合约的自动化漏洞生成 显示英文标题

标题： Prompt to Pwn: Automated Exploit Generation for Smart Contracts

摘要： 我们探索了使用大语言模型（LLM）用于针对易受攻击的智能合约的自动化漏洞利用生成（AEG）的可行性。我们提出了\textsc{ReX}，一个将基于LLM的漏洞利用合成与Foundry测试套件集成的框架，实现了概念验证（PoC）漏洞利用的自动生成和验证。我们在合成基准和受已知高影响漏洞影响的真实智能合约上评估了五种最先进的LLM（GPT-4.1，Gemini 2.5 Pro，Claude Opus 4，DeepSeek和Qwen3 Plus）。我们的结果表明，现代LLM可以可靠地为多种漏洞类型生成功能性的PoC漏洞利用，成功率高达92%。值得注意的是，Gemini 2.5 Pro和GPT-4.1在合成和真实场景中始终优于其他模型。我们进一步分析了影响AEG效果的因素，包括模型能力、合约结构和漏洞类型。我们还收集了首个经过整理的真实PoC漏洞利用数据集，以支持未来的研究。 显示英文摘要

摘要： We explore the feasibility of using LLMs for Automated Exploit Generation (AEG) against vulnerable smart contracts. We present \textsc{ReX}, a framework integrating LLM-based exploit synthesis with the Foundry testing suite, enabling the automated generation and validation of proof-of-concept (PoC) exploits. We evaluate five state-of-the-art LLMs (GPT-4.1, Gemini 2.5 Pro, Claude Opus 4, DeepSeek, and Qwen3 Plus) on both synthetic benchmarks and real-world smart contracts affected by known high-impact exploits. Our results show that modern LLMs can reliably generate functional PoC exploits for diverse vulnerability types, with success rates reaching up to 92\%. Notably, Gemini 2.5 Pro and GPT-4.1 consistently outperform others in both synthetic and real-world scenarios. We further analyze factors influencing AEG effectiveness, including model capabilities, contract structure, and vulnerability types. We also collect the first curated dataset of real-world PoC exploits to support future research.