用AI辅助黑客攻击测试，提升效率但需警惕伦理风险
AI-Augmented Ethical Hacking: A Practical Examination of Manual Exploitation and Privilege Escalation in Linux Environments
- 结合生成式AI辅助手动漏洞利用与权限提升
- 可自动识别攻击路径并解析复杂输出中的敏感信息
- 适合安全研究人员与红队成员参考实践
本研究探讨生成式AI（GenAI）在基于Linux的渗透测试环境中，对手动漏洞利用与权限提升任务的应用。基于先前关于GenAI在道德黑客生命周期中作用的研究，本文在受控虚拟环境中开展实证分析，评估其在这些关键且常需人工操作任务中的实用性。结果表明，GenAI能有效简化流程，如识别潜在攻击向量、在权限提升过程中解析复杂输出以提取敏感数据。研究还揭示了其优势与挑战：包括效率与可扩展性提升，以及数据隐私、意外发现漏洞和滥用风险等伦理问题。本文强调人机协作的重要性，尤其在需要审慎决策的场景中，而非完全替代人类判断。
原文摘要 · Abstract (English)
This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments. Building on previous research into the role of GenAI in the ethical hacking lifecycle, this paper presents a hands-on experimental analysis conducted in a controlled virtual setup to evaluate the utility of GenAI in supporting these crucial, often manual, tasks. Our findings demonstrate that GenAI can streamline processes, such as identifying potential attack vectors and parsing complex outputs for sensitive data during privilege escalation. The study also identifies key benefits and challenges associated with GenAI, including enhanced efficiency and scalability, alongside ethical concerns related to data privacy, unintended discovery of vulnerabilities, and potential for misuse. This work contributes to the growing field of AI-assisted cybersecurity by emphasising the importance of human-AI collaboration, especially in contexts requiring careful decision-making, rather than the complete replacement of human input.
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