PulseAugur
EN
LIVE 15:27:24

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

By PulseAugur Editorial · [3 sources] ·

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 →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Kevin Eykholt, Dhilung Kirat, Xiaokui Shu, Jiyong Jang, Frederico Araujo, Ian Molloy ·

    Lessons from Penetration Tests on Large-Scale Agent Systems

    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 ·

    Lessons from Penetration Tests on Large-Scale Agent Systems

    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) ·

    APT-Agent: Automated Penetration Testing using Large Language Models

    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…

RELATED ENTITIES

RELATED TOPICS