LLM-powered APT-Agent achieves 84% success in automated penetration tests

Researchers have developed APT-Agent, an automated penetration testing framework utilizing large language models to address challenges like hallucinated commands and limited context memory. This framework systematically handles reconnaissance, exploitation, and exfiltration, incorporating a rectification module for command recovery and a specialized memory architecture for multi-step attacks. In evaluations on Metasploitable 2, APT-Agent demonstrated an 84.29% end-to-end exploitation success rate, significantly outperforming existing methods like PentestGPT. AI

影响 This research demonstrates a significant advancement in LLM application for cybersecurity, potentially automating complex penetration testing tasks and improving security infrastructure defenses.

排序理由 The cluster contains an academic paper detailing a new LLM-based framework for automated penetration testing. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• APT-Agent
• Large Language Models
• Metasploitable 2
• PentestGPT

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