# APT-Agent: Automated Penetration Testing using Large Language Models

arXiv: https://arxiv.org/abs/2605.24949 · HTML: https://mlsanity.com/paper/2605.24949/

## Key facts

- Cited by 2 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 20 of its 32 references that are in the map.
- First posted on arXiv 24 May 2026.

## 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, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [LLM Agents can Autonomously Hack Websites](https://mlsanity.com/paper/2402.06664/) (2024)
- [PentestGPT: An LLM-empowered Automatic Penetration Testing Tool](https://mlsanity.com/paper/2308.06782/) (2023)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [PentestAgent: Incorporating LLM Agents to Automated Penetration Testing](https://mlsanity.com/paper/2411.05185/) (2024)
- [PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation](https://mlsanity.com/paper/2407.17788/) (2024)
- [LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing](https://mlsanity.com/paper/2310.06936/) (2023)
- [Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2405.00648/) (2024)
- [Automated Progressive Red Teaming](https://mlsanity.com/paper/2407.03876/) (2024)
- [Large Language Models for Blockchain Security: A Systematic Literature Review](https://mlsanity.com/paper/2403.14280/) (2024)
- [From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs](https://mlsanity.com/paper/2506.13434/) (2025)
- [Penetration Testing == POMDP Solving?](https://mlsanity.com/paper/1306.4714/) (2013)
- [Penetration Testing for System Security: Methods and Practical Approaches](https://mlsanity.com/paper/2505.19174/) (2025)
- [Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN](https://mlsanity.com/paper/2407.15656/) (2024)
- [ExploitFlow, cyber security exploitation routes for Game Theory and AI research in robotics](https://mlsanity.com/paper/2308.02152/) (2023)

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

- [Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response](https://mlsanity.com/paper/2607.25379/) (2026)
- [Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing](https://mlsanity.com/paper/2609.07344/) (2026)

## Abstract (arXiv)

Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and complexity. Large Language Models (LLMs) offer new opportunities for automating these tasks, but existing approaches face two persistent challenges: hallucination of technical entities and insufficient long-term contextual memory. To address these issues, we present APT-Agent, a fully automated LLM-driven penetration testing framework that systematically orchestrates reconnaissance, exploitation, and exfiltration. APT-Agent introduces a hybrid rectification module to recover hallucinated commands and a command-specific memory architecture to preserve operational context across multi-step attack sequences. We evaluate our APT-Agent on Metasploitable 2 against seven vulnerable services spanning web, database, and network protocols. APT-Agent achieves an 84.29% end-to-end exploitation success rate, compared to 48.57% (Script Kiddie) and 18.57% (PentestGPT) under matched conditions. By reducing cognitive burden and minimizing reliance on human intervention, APT-Agent represents a step toward scalable, reliable, and cognitively efficient automation for penetration testing.