mlsanityData to 7 Oct 2026

RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents

arXiv 2502.16730 ↗First posted 23 Feb 2025cs.CR cs.AIcited by 10

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

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  7. 2024GPT-4o System Cardcited by 4,467
  8. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  9. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  10. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  11. 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 94
  12. 2023Getting pwn'd by AI: Penetration Testing with Large Language Modelscited by 82
2 more
  1. 2024PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediationcited by 30
  2. 2025A Unified Modeling Framework for Automated Penetration Testingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
  2. 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
  3. 2024HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
  4. 2026APT-Agent: Automated Penetration Testing using Large Language Models
  5. 2025ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
  6. 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
  7. 2025Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning
  8. 2026Big Enough to Break Out: Tracking the Rising Capability of LLM Penetration-Testing Agents