# PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts

arXiv: https://arxiv.org/abs/2511.02780 · HTML: https://mlsanity.com/paper/2511.02780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 72 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security](https://mlsanity.com/paper/2406.05590/) (2024)
- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation](https://mlsanity.com/paper/2407.17788/) (2024)
- [Agentic AI Software Engineers: Programming with Trust](https://mlsanity.com/paper/2502.13767/) (2025)
- [Finding The Greedy, Prodigal, and Suicidal Contracts at Scale](https://mlsanity.com/paper/1802.06038/) (2018)
- [Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks](https://mlsanity.com/paper/2502.04227/) (2025)
- [DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects](https://mlsanity.com/paper/2305.08456/) (2023)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [Dynamic Risk Assessments for Offensive Cybersecurity Agents](https://mlsanity.com/paper/2505.18384/) (2025)
- [AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models](https://mlsanity.com/paper/2506.14682/) (2025)
- [FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation](https://mlsanity.com/paper/2206.10708/) (2022)
- [Oracle-Supported Dynamic Exploit Generation for Smart Contracts](https://mlsanity.com/paper/1909.06605/) (2019)
- [SC-Bench: A Large-Scale Dataset for Smart Contract Auditing](https://mlsanity.com/paper/2410.06176/) (2024)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [EF/CF: High Performance Smart Contract Fuzzing for Exploit Generation](https://mlsanity.com/paper/2304.06341/) (2023)
- [FORAY: Towards Effective Attack Synthesis against Deep Logical Vulnerabilities in DeFi Protocols](https://mlsanity.com/paper/2407.06348/) (2024)
- [Smart Contract Fuzzing Towards Profitable Vulnerabilities](https://mlsanity.com/paper/2501.08834/) (2025)
- [Precise Attack Synthesis for Smart Contracts](https://mlsanity.com/paper/1902.06067/) (2019)
- [Evaluation of ChatGPT's Smart Contract Auditing Capabilities Based on Chain of Thought](https://mlsanity.com/paper/2402.12023/) (2024)
- [Do Automated Fixes Truly Mitigate Smart Contract Exploits?](https://mlsanity.com/paper/2501.04600/) (2025)
- [Automated Attack Synthesis for Constant Product Market Makers](https://mlsanity.com/paper/2404.05297/) (2024)

## Built on by (most cited first, 8 of 8)

- [PBFuzz: Agentic Directed Fuzzing for PoV Generation](https://mlsanity.com/paper/2512.04611/) (2025)
- [TxRay: Agentic Postmortem of Live Blockchain Attacks](https://mlsanity.com/paper/2602.01317/) (2026)
- [A Dual-Loop Agent Framework for Automated Vulnerability Reproduction](https://mlsanity.com/paper/2602.05721/) (2026)
- [PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy](https://mlsanity.com/paper/2604.06618/) (2026)
- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)
- [LLMs + Security = Trouble](https://mlsanity.com/paper/2602.08422/) (2026)
- [Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning](https://mlsanity.com/paper/2606.19988/) (2026)
- [WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses](https://mlsanity.com/paper/2609.36635/) (2026)

## Abstract (arXiv)

Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses. Thus, smart contracts are subject to security audits. In auditing, proof-of-concept (PoC) exploits play a critical role by demonstrating to the stakeholders that the reported vulnerabilities are genuine, reproducible, and actionable. However, manually creating PoCs is time-consuming, error-prone, and often constrained by tight audit schedules. We introduce PoCo, an agentic framework that automatically generates executable PoC exploits from natural-language vulnerability descriptions written by auditors. PoCo autonomously generates PoC exploits in an agentic manner by interacting with a set of codeexecution tools in a Reason-Act-Observe loop. It produces fully executable exploits compatible with the Foundry testing framework, ready for integration into audit reports and other security tools. We evaluate PoCo on a dataset of 23 real-world vulnerability reports. PoCo consistently outperforms the Zero-shot and Workflow baselines, generating well-formed and logically correct PoCs. Our results demonstrate that agentic frameworks can significantly reduce the effort required for high-quality PoCs in smart contract audits. Our contribution provides actionable knowledge for the smart contract security community.