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

Generative artificial intelligence tools like large language models are
rapidly transforming academic research and real world applications. However,
discussions on ethical guidelines for generative AI in science remain
fragmented, underscoring the urgent need for consensus based standards. This
paper offers an initial framework by developing analyses and mitigation
strategies across five key themes: understanding model limitations regarding
truthfulness and bias; respecting privacy, confidentiality, and copyright;
avoiding plagiarism and policy violations when incorporating model output;
ensuring applications provide overall benefit; and using AI transparently and
reproducibly. Common scenarios are outlined to demonstrate potential ethical
violations. We argue that global consensus coupled with professional training
and reasonable enforcement are critical to promoting the benefits of AI while
safeguarding research integrity.

发展分析和缓解策略，提供了一个初始框架来理解模型限制、尊重隐私、保密和版权、避免抄袭和政策违规、确保应用程序提供整体利益，并透明可重现地使用人工智能，以保护研究的完整性。

构建科学研究中生成型人工智能的道德准则

Building ethical guidelines for generative AI in scientific research

This paper addresses the problem of sharing drivers' face videos for
transportation research while adhering to proper ethical guidelines. The paper
first gives an overview of the multitude of problems associated with sharing
such data and then proposes a framework on how artificial intelligence-based
techniques, specifically face swapping, can be used for de-identifying drivers'
faces. Through extensive experimentation with an Oak Ridge National Laboratory
(ORNL) dataset, we demonstrate the effectiveness of face-swapping algorithms in
preserving essential attributes related to human factors research, including
eye movements, head movements, and mouth movements. The efficacy of the
framework was also tested on various naturalistic driving study data collected
at the Virginia Tech Transportation Institute. The results achieved through the
proposed techniques were evaluated qualitatively and quantitatively using
various metrics. Finally, we discuss possible measures for sharing the
de-identified videos with the greater research community.

本文提出了一种基于人工智能的面部替换技术用于去匿名化驾驶员面部数据来解决交通研究数据共享中的隐私问题，并通过实验证明了这种技术在保护驾驶员个人隐私数据和人类行为研究方面的有效性和前景，最终讨论了可能的共享措施。