AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?

• URL: http://arxiv.org/abs/2411.01236v1
• Date: Sat, 02 Nov 2024 13:24:30 GMT
• Title: AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?
• Authors: Benlong Wu, Guoqiang Chen, Kejiang Chen, Xiuwei Shang, Jiapeng Han, Yanru He, Weiming Zhang, Nenghai Yu,
• Abstract summary: We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
• Score: 54.65079443902714
• License:
• Abstract: Penetration testing is essential to ensure Web security, which can detect and fix vulnerabilities in advance, and prevent data leakage and serious consequences. The powerful inference capabilities of large language models (LLMs) have made significant progress in various fields, and the development potential of LLM-based agents can revolutionize the cybersecurity penetration testing industry. In this work, we establish a comprehensive end-to-end penetration testing benchmark using a real-world penetration testing environment to explore the capabilities of LLM-based agents in this domain. Our results reveal that the agents are familiar with the framework of penetration testing tasks, but they still face limitations in generating accurate commands and executing complete processes. Accordingly, we summarize the current challenges, including the difficulty of maintaining the entire message history and the tendency for the agent to become stuck. Based on the above insights, we propose a Penetration testing State Machine (PSM) that utilizes the Finite State Machine (FSM) methodology to address these limitations. Then, we introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs, which utilizes the inherent inference ability of LLM and the constraint framework of state machines. Our evaluation results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model and improves the task completion rate from 22% to 41% on the benchmark target. Compared with the baseline framework and manual work, AutoPT also reduces time and economic costs further. Hence, our AutoPT has facilitated the development of automated penetration testing and significantly impacted both academia and industry.

Related papers

• PentestAgent: Incorporating LLM Agents to Automated Penetration Testing [6.815381197173165]
  Manual penetration testing is time-consuming and expensive. Recent advancements in large language models (LLMs) offer new opportunities for enhancing penetration testing. We propose PentestAgent, a novel LLM-based automated penetration testing framework.
  arXiv  Detail & Related papers  (2024-11-07T21:10:39Z)
• Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements [1.4433703131122861]
  Large language models (LLMs) have shown potential across various domains, including cybersecurity. There is currently no comprehensive, open, end-to-end automated penetration testing benchmark. This paper introduces a novel open benchmark for LLM-based automated penetration testing.
  arXiv  Detail & Related papers  (2024-10-22T16:18:41Z)
• AutoPenBench: Benchmarking Generative Agents for Penetration Testing [42.681170697805726]
  This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction.
  arXiv  Detail & Related papers  (2024-10-04T08:24:15Z)
• MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains [54.117238759317004]
  Massive Multitask Agent Understanding (MMAU) benchmark features comprehensive offline tasks that eliminate the need for complex environment setups. It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents.
  arXiv  Detail & Related papers  (2024-07-18T00:58:41Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies. We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• Test Oracle Automation in the era of LLMs [52.69509240442899]
  Large Language Models (LLMs) have demonstrated remarkable proficiency in tackling diverse software testing tasks. This paper aims to enable discussions on the potential of using LLMs for test oracle automation, along with the challenges that may emerge during the generation of various types of oracles.
  arXiv  Detail & Related papers  (2024-05-21T13:19:10Z)
• Selene: Pioneering Automated Proof in Software Verification [62.09555413263788]
  We introduce Selene, which is the first project-level automated proof benchmark constructed based on the real-world industrial-level operating system microkernel, seL4. Our experimental results with advanced large language models (LLMs), such as GPT-3.5-turbo and GPT-4, highlight the capabilities of LLMs in the domain of automated proof generation.
  arXiv  Detail & Related papers  (2024-01-15T13:08:38Z)
• PentestGPT: An LLM-empowered Automatic Penetration Testing Tool [20.449761406790415]
  Large Language Models (LLMs) have shown significant advancements in various domains. We evaluate the performance of LLMs on real-world penetration testing tasks using a robust benchmark created from test machines with platforms. We introduce PentestGPT, an LLM-empowered automatic penetration testing tool.
  arXiv  Detail & Related papers  (2023-08-13T14:35:50Z)
• Getting pwn'd by AI: Penetration Testing with Large Language Models [0.0]
  This paper explores the potential usage of large-language models, such as GPT3.5, to augment penetration testers with AI sparring partners. We explore the feasibility of supplementing penetration testers with AI models for two distinct use cases: high-level task planning for security testing assignments and low-level vulnerability hunting within a vulnerable virtual machine.
  arXiv  Detail & Related papers  (2023-07-24T19:59:22Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.