In the vast domain of cybersecurity, the transition from reactive defense to
offensive has become critical in protecting digital infrastructures. This paper
explores the integration of Artificial Intelligence (AI) into offensive
cybersecurity, particularly through the development of an autonomous AI agent,
ReaperAI, designed to simulate and execute cyberattacks. Leveraging the
capabilities of Large Language Models (LLMs) such as GPT-4, ReaperAI
demonstrates the potential to identify, exploit, and analyze security
vulnerabilities autonomously.
This research outlines the core methodologies that can be utilized to
increase consistency and performance, including task-driven penetration testing
frameworks, AI-driven command generation, and advanced prompting techniques.
The AI agent operates within a structured environment using Python, enhanced by
Retrieval Augmented Generation (RAG) for contextual understanding and memory
retention. ReaperAI was tested on platforms including, Hack The Box, where it
successfully exploited known vulnerabilities, demonstrating its potential
power.
However, the deployment of AI in offensive security presents significant
ethical and operational challenges. The agent's development process revealed
complexities in command execution, error handling, and maintaining ethical
constraints, highlighting areas for future enhancement.
This study contributes to the discussion on AI's role in cybersecurity by
showcasing how AI can augment offensive security strategies. It also proposes
future research directions, including the refinement of AI interactions with
cybersecurity tools, enhancement of learning mechanisms, and the discussion of
ethical guidelines for AI in offensive roles. The findings advocate for a
unique approach to AI implementation in cybersecurity, emphasizing innovation.

在网络安全的广阔领域中，从防御到进攻的转变对保护数字基础设施至关重要，本研究探讨了将人工智能（AI）应用于进攻性网络安全的整合，特别是通过开发一款自主 AI 代理程序 ReaperAI，用于模拟和执行网络攻击，利用大型语言模型（LLMs）如 GPT-4 的能力，ReaperAI 展示了自主识别、利用和分析安全漏洞的潜力，同时还提出了 AI 在网络安全中的道德和操作挑战，以及未来的研究方向。

人工智能作为新黑客：为攻击性安全开发代理

Artificial Intelligence as the New Hacker: Developing Agents for  Offensive Security

Organizations struggle to handle sheer number of vulnerabilities in their
cloud environments. The de facto methodology used for prioritizing
vulnerabilities is to use Common Vulnerability Scoring System (CVSS). However,
CVSS has inherent limitations that makes it not ideal for prioritization. In
this work, we propose a new way of prioritizing vulnerabilities. Our approach
is inspired by how offensive security practitioners perform penetration
testing. We evaluate our approach with a real world case study for a large
client, and the accuracy of machine learning to automate the process end to
end.

本文介绍了一种新的漏洞优先级排序方法，受渗透测试的启发，通过一个真实的案例来验证了机器学习自动化流程的准确性。