[2605.21779] FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction

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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<br>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

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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. Second, existing LLM-based approaches use suboptimal granularities for vulnerability localization: function-level analysis overlooks bugs when context becomes extensive, while line-level analysis lacks sufficient context. Third, existing approaches have difficulty reasoning about

vulnerabilities with complex cross-function dependencies and triggering conditions.

We present FuzzingBrain V2, a multi-agent system that addresses these gaps through four key contributions: (1) fully automated vulnerability analysis built on Google's OSS-Fuzz, ensuring all reported vulnerabilities are fuzzer-reproducible; (2) Suspicious Point, a novel control-flow-based abstraction for precise

vulnerability localization at the optimal granularity; (3) logic-driven hierarchical function analysis with dual-layer fuzzing enhancing function coverage under resource constraints; (4) MCP-based static and dynamic analysis tools with context engineering enhancing complex vulnerability reasoning.

On the AIxCC 2025 Final Competition C/C++ dataset, FuzzingBrain V2 achieved 90% detection rate (36 of 40 vulnerabilities). In real-world deployment, FuzzingBrain V2 discovered 29 zero-day vulnerabilities across 12 open-source projects, all confirmed and fixed by maintainers, with 2 assigned CVE IDs.

Subjects:

Cryptography and Security (cs.CR); Software Engineering (cs.SE)

Cite as:<br>arXiv:2605.21779 [cs.CR]

(or<br>arXiv:2605.21779v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2605.21779

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Ze Sheng [view email]<br>[v1]<br>Wed, 20 May 2026 22:17:14 UTC (1,827 KB)

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