FuzzingBrain V2
#39 in Agent Frameworksunknown · v2 · since 2026-05-20 · 2× · last seen Jun 29, 2026
FuzzingBrain V2 is a multi-agent LLM system developed at Texas A&M University (authors: Ze Sheng, Zhicheng Chen, Qingxiao Xu, Kewen Zhu, Jeff Huang) for fully automated software vulnerability discovery and reproduction, published as an arXiv preprint on May 20, 2026. The system is built on Google's OSS-Fuzz infrastructure and uses the Model Context Protocol (MCP) as the communication standard between specialized sub-agents. In benchmarks on the AIxCC 2025 dataset, it achieved a 90% detection rate (36 of 40 vulnerabilities); in real-world deployment, 29 zero-day vulnerabilities were discovered across 12 open-source projects and confirmed by maintainers. The system currently focuses on C/C++ with limited Java support via Jazzer.
Features
| Deployment Duration (Long-Running) | Configurable time limits: 120 minutes (delta scan) and 240 minutes (full scan) per challenge; first finding result in under 5 minutes (per fuzzingbrain.github.io) |
| Price Tier | Open source (Apache 2.0 license); evaluation budget in paper: $150 (delta scan) or $400 (full scan) per challenge; $200 per project in real-world deployment (API costs for LLM calls) |