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

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-Pentest-Benchmark
• arXiv
• AutoPenBench
• PentestGPT
• Qwen3-32B
• VulnBot
• xOffense

• paper
• product

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