APT-Agent: Automated Penetration Testing using Large Language Models
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
IMPACT This research demonstrates a significant advancement in LLM application for cybersecurity, potentially automating complex penetration testing tasks and improving security infrastructure defenses.