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xOffense framework uses adapted LLMs for autonomous, multi-agent penetration testing

By PulseAugur Editorial · [1 sources] ·

Researchers have developed xOffense, an autonomous multi-agent framework designed for penetration testing. This system utilizes a fine-tuned, mid-scale open-source LLM, specifically Qwen3-32B, to automate complex cybersecurity tasks. The framework assigns specialized agents for reconnaissance, vulnerability scanning, and exploitation, with an orchestration layer managing their coordination. Evaluations on benchmarks like AutoPenBench show xOffense achieving a 79.17% sub-task completion rate, outperforming existing systems such as VulnBot and PentestGPT. AI

IMPACT Autonomous penetration testing frameworks like xOffense could significantly reduce manual effort and increase the scalability of cybersecurity assessments.

RANK_REASON This is a research paper detailing a new framework and its evaluation on benchmarks.

Read on arXiv cs.AI →

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

xOffense framework uses adapted LLMs for autonomous, multi-agent penetration testing

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Phung Duc Luong, Le Tran Gia Bao, Nguyen Vu Khai Tam, Dong Huu Nguyen Khoa, Nguyen Huu Quyen, Van-Hau Pham, Phan The Duy ·

    xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models

    arXiv:2509.13021v2 Announce Type: replace-cross Abstract: This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-driven manual efforts to fully automated, machine-executable workflows capable of …

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