Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction
FuzzingBrain V2 is a multi-agent system designed to enhance automated vulnerability discovery and reproduction. It addresses challenges such as high false positive rates and suboptimal vulnerability localization in existing LLM approaches. The system has demonstrated a 90% detection rate and discovered multiple zero-day vulnerabilities in real-world applications.
- ▪Nearly 50,000 CVEs were reported in 2025, highlighting the critical need for improved vulnerability detection.
- ▪FuzzingBrain V2 achieved a 90% detection rate on the AIxCC 2025 Final Competition dataset.
- ▪The system discovered 29 zero-day vulnerabilities across 12 open-source projects, all confirmed and fixed by maintainers.
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Computer Science > Cryptography and Security arXiv:2605.21779 (cs) [Submitted on 20 May 2026] Title:FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction Authors:Ze Sheng, Zhicheng Chen, Qingxiao Xu, Kewen Zhu, Jeff Huang View a PDF of the paper titled FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction, by Ze Sheng and 4 other authors View PDF HTML (experimental) Abstract:Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise for automated vulnerability detection, three key challenges remain. First, LLM-generated vulnerability reports suffer from high false positive rates and lack reproducible verification.
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