AI代理面临反复出现的安全漏洞；新的LLM工具可实现渗透测试自动化

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Kevin Eykholt, Dhilung Kirat, Xiaokui Shu, Jiyong Jang, Frederico Araujo, Ian Molloy · 2026-05-27 04:00

   对大规模智能体系统进行渗透测试的经验教训

   arXiv:2605.27042v1 Announce Type: cross Abstract: As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of these vulnerabilities are not fundamentally novel, but instead reflect recurrin…

2. arXiv cs.AI TIER_1 English(EN) · Ian Molloy · 2026-05-26 13:58

   对大规模智能体系统进行渗透测试的经验教训

   As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of these vulnerabilities are not fundamentally novel, but instead reflect recurring classes of weaknesses long observed in prior com…

3. arXiv cs.AI TIER_1 English(EN) · William Guanting Li (University of Queensland), Alsharif Abuadbba (CSIRO Data61), Kristen Moore (CSIRO Data61), Dan Dongseong Kim (University of Queensland) · 2026-05-26 04:00

   APT-Agent：使用大型语言模型进行自动化渗透测试

   arXiv:2605.24949v1 Announce Type: cross Abstract: Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and complexity. Large Language Models (LLMs) offer new opportunities for automating the…