RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior approaches that focus primarily on post-exploitation or require a human-in-the-loop, RapidPen leverages large language models (LLMs) to autonomously discover and exploit vulnerabilities, starting from a single IP address. By integrating advanced ReAct-style task planning (Re) with retrieval-augmented knowledge bases of successful exploits, along with a command-generation and direct execution feedback loop (Act), RapidPen systematically scans services, identifies viable attack vectors, and executes targeted exploits in a fully automated manner. In our evaluation against a vulnerable target from the Hack The Box platform, RapidPen achieved shell access within 200-400 seconds at a per-run cost of approximately 0.3-0.6, demonstrating a 60% success rate when reusing prior "success-case" data. These results underscore the potential of truly autonomous pentesting for both security novices and seasoned professionals. Organizations without dedicated security teams can leverage RapidPen to quickly identify critical vulnerabilities, while expert pentesters can offload repetitive tasks and focus on complex challenges. Ultimately, our work aims to make penetration testing more accessible and cost-efficient, thereby enhancing the overall security posture of modern software ecosystems. Fore more information, visit this link: https://secdevlab.com/rapidpen
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 30 references that are in the map. First posted on arXiv 23 Feb 2025.
Builds on
14 of 30 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2024GPT-4o System Cardcited by 4,467
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 94
- 2023Getting pwn'd by AI: Penetration Testing with Large Language Modelscited by 82
Built on by
Citing papers per year: 2025: 7; 2026: 3.
10 papers.
- 2025Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Designcited by 13
- 2026What Makes a Good LLM Agent for Real-world Penetration Testing?cited by 8
- 2025PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilitiescited by 6
- 2025xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Modelscited by 2
- 2025Living Off the LLM: How LLMs Will Change Adversary Tacticscited by 1
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFscited by 1
- 2026CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environmentscited by 1
- 2025Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Researchcited by 0
- 2025SoK: Measuring What Matters for Closed-Loop Security Agentscited by 0
- 2026From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-Worldcited by 0
- 2026From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World11 May 2026
- 2026CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environments7 Apr 2026
- 2026What Makes a Good LLM Agent for Real-world Penetration Testing?19 Feb 2026
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs20 Oct 2025
- 2025PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities13 Oct 2025
- 2025Living Off the LLM: How LLMs Will Change Adversary Tactics13 Oct 2025
- 2025SoK: Measuring What Matters for Closed-Loop Security Agents2 Oct 2025
- 2025xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models16 Sep 2025
- 2025Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Research10 Jun 2025
- 2025Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design14 Apr 2025
Similar, not cited
Close in wording, no citation either way
- 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
- 2024HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
- 2026APT-Agent: Automated Penetration Testing using Large Language Models
- 2025ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
- 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
- 2025Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning
- 2026Big Enough to Break Out: Tracking the Rising Capability of LLM Penetration-Testing Agents