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AI agents face recurring security flaws; new LLM tool automates penetration tests

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore the security implications of AI agents and penetration testing. One paper analyzes vulnerabilities in proprietary AI agent systems, finding recurring weaknesses similar to older computing systems, and evaluates security improvements since 2025. The other paper introduces APT-Agent, an LLM-driven framework for automated penetration testing that achieves an 84.29% exploitation success rate by mitigating hallucinated commands and enhancing memory. AI

IMPACT New research highlights persistent security vulnerabilities in AI agents and introduces an LLM-based tool that significantly improves automated penetration testing success rates.

RANK_REASON The cluster contains two academic papers discussing AI security and penetration testing.

Read on arXiv cs.AI →

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

AI agents face recurring security flaws; new LLM tool automates penetration tests

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…

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