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

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

RANK_REASON 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]

Read on arXiv cs.AI →

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

• paper
• product
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →