Hacking, The Lazy Way: LLM Augmented Pentesting

This is my personal note about the paper. https://arxiv.org/abs/2409.09493

Abstract

This research suggests "LLM-Augmented Pentesting" as an efficient approach to penetration testing.
The tool, named "Pentest Copilot," supports security professionals' tasks using GPT-4 Turbo.

Objective

This approach aims to bridge the gap between high-quality manual testing and low-quality automated tool testing.

Methods

1. LLM
   1. GPT-4 Turbo is demonstrated to perform better based on the testing results.
2. Prompt design
   1. Jailbreaking GPT: "Penetration testing assistant, collaborating with a security researcher"
   2. Designed for interactive and dynamic responses:
      1. If the user provides no information, the LLM prompts the user with questions.
      2. If the user provides detailed initial test information, the LLM skips initial test steps.
3. RAG
   1. Query processing
   2. Information Retrieval
   3. Data Integration
4. File Analysis with LLM
   1. Determines the file format using the Linux file command.
   2. Executes the appropriate analysis based on the command output.

There are many other tips described in the paper.

Results

Pentest Copilot has shown remarkable utility for penetration testers. In particular, the GPT-4 Turbo model, combined with RAG and file analysis methods, proved effective for this approach. In the future, fine-tuned models for penetration testing and red team activities could further enhance these capabilities and provide stronger support for penetration testers.

Interesting points

• This paper uses GPT-4 Turbo. I'm curious about what would happen if the o1 model were used instead.
• The prompt design is detailed and seems very helpful.
• The diagrams make the concepts easy to understand.

Phrase

LLM Augmented Pentesting is a novel approach to this problem,