Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Hacking, The Lazy Way: LLM Augmented Pentesting

Dhruva Goyal, Sitaraman Subramanian, Aditya Peela, Nisha P. Shetty

TL;DR

The paper tackles the inefficiency and human-dependence of traditional pentesting by introducing LLM-Augmented Pentesting with Pentest Copilot, a system that blends chain-of-thought reasoning, Retrieval-Augmented Generation (RAG), and in-browser infrastructure to automate sub-tasks while preserving expert oversight. It presents a comprehensive methodology, including model selection within the GPT lineage, a multi-prompt loop for command generation, summarization, and task updates, plus a step-chaining approach to manage a per-call context of $4{,}096$ tokens and a broader $128{,}000$-token capacity through RAG. Empirical results from a boot2root testbench across 30 scenarios show improved performance and reduced hallucinations, alongside a rich infrastructure supporting live tool chaining, file analysis, and GUI access. The work contributes a concrete blueprint for scalable, real-time pentesting automation and outlines concrete future directions, such as expanding domain-specific knowledge bases and enhancing red-team capabilities to handle more advanced attack scenarios.

Abstract

In our research, we introduce a new concept called "LLM Augmented Pentesting" demonstrated with a tool named "Pentest Copilot," that revolutionizes the field of ethical hacking by integrating Large Language Models (LLMs) into penetration testing workflows, leveraging the advanced GPT-4-turbo model. Our approach focuses on overcoming the traditional resistance to automation in penetration testing by employing LLMs to automate specific sub-tasks while ensuring a comprehensive understanding of the overall testing process. Pentest Copilot showcases remarkable proficiency in tasks such as utilizing testing tools, interpreting outputs, and suggesting follow-up actions, efficiently bridging the gap between automated systems and human expertise. By integrating a "chain of thought" mechanism, Pentest Copilot optimizes token usage and enhances decision-making processes, leading to more accurate and context-aware outputs. Additionally, our implementation of Retrieval-Augmented Generation (RAG) minimizes hallucinations and ensures the tool remains aligned with the latest cybersecurity techniques and knowledge. We also highlight a unique infrastructure system that supports in-browser penetration testing, providing a robust platform for cybersecurity professionals. Our findings demonstrate that LLM Augmented Pentesting can not only significantly enhance task completion rates in penetration testing but also effectively addresses real-world challenges, marking a substantial advancement in the cybersecurity domain.

Hacking, The Lazy Way: LLM Augmented Pentesting

TL;DR

The paper tackles the inefficiency and human-dependence of traditional pentesting by introducing LLM-Augmented Pentesting with Pentest Copilot, a system that blends chain-of-thought reasoning, Retrieval-Augmented Generation (RAG), and in-browser infrastructure to automate sub-tasks while preserving expert oversight. It presents a comprehensive methodology, including model selection within the GPT lineage, a multi-prompt loop for command generation, summarization, and task updates, plus a step-chaining approach to manage a per-call context of tokens and a broader -token capacity through RAG. Empirical results from a boot2root testbench across 30 scenarios show improved performance and reduced hallucinations, alongside a rich infrastructure supporting live tool chaining, file analysis, and GUI access. The work contributes a concrete blueprint for scalable, real-time pentesting automation and outlines concrete future directions, such as expanding domain-specific knowledge bases and enhancing red-team capabilities to handle more advanced attack scenarios.

Abstract

In our research, we introduce a new concept called "LLM Augmented Pentesting" demonstrated with a tool named "Pentest Copilot," that revolutionizes the field of ethical hacking by integrating Large Language Models (LLMs) into penetration testing workflows, leveraging the advanced GPT-4-turbo model. Our approach focuses on overcoming the traditional resistance to automation in penetration testing by employing LLMs to automate specific sub-tasks while ensuring a comprehensive understanding of the overall testing process. Pentest Copilot showcases remarkable proficiency in tasks such as utilizing testing tools, interpreting outputs, and suggesting follow-up actions, efficiently bridging the gap between automated systems and human expertise. By integrating a "chain of thought" mechanism, Pentest Copilot optimizes token usage and enhances decision-making processes, leading to more accurate and context-aware outputs. Additionally, our implementation of Retrieval-Augmented Generation (RAG) minimizes hallucinations and ensures the tool remains aligned with the latest cybersecurity techniques and knowledge. We also highlight a unique infrastructure system that supports in-browser penetration testing, providing a robust platform for cybersecurity professionals. Our findings demonstrate that LLM Augmented Pentesting can not only significantly enhance task completion rates in penetration testing but also effectively addresses real-world challenges, marking a substantial advancement in the cybersecurity domain.
Paper Structure (31 sections, 3 figures, 6 tables, 1 algorithm)

This paper contains 31 sections, 3 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Research Survey
  3. Educational and Static Evaluation of LLMs
  4. Modular LLM Frameworks with Limited Realism or Human Dependence
  5. Task-Specific or Narrowly Focused LLM Applications
  6. Pre-LLM RL-Centric Automation Efforts
  7. Recurring Limitations in Existing Work
  8. Methodology
  9. Large Language Model
  10. Selecting a class of models
  11. Surface Evaluation of GPT Models
  12. Testbenching Framework and Evaluation
  13. Prompt Engineering
  14. Jailbreaking GPT
  15. Dissecting Prompts
  16. ...and 16 more sections

Figures (3)

  • Figure 1: Flowchart on Dynamic Prompt Creation where new target information and previous context is injected into the user prompt based on the current state of pentest
  • Figure 2: Overview of a Single Pentest Loop covering the task from the user, generation of commands, running suggested commands followed by summarizing the pentest loop and updating the todo-list
  • Figure 3: Complete infrastructure of Pentest Copilot - All services and sandboxes are deployed within a single subnet, enabling seamless communication between containers, particularly when multiple researchers within the same organization may rely on the same server.