Prompt to Pwn: Automated Exploit Generation for Smart Contracts

arXiv:2508.01371v3 Announce Type: replace-cross Abstract: 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 extsc{ReX}, 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.

Related

• Domain-Specific Data Generation Framework for RAG Adaptation
• Understanding Generalization in Role-Playing Models via Information Theory
• Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges
• Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism
• FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
• ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge

Source: arXiv cs.AI | 2026-04-22