As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in security automation. We address this by introducing Zer0n, a framework that anchors the reasoning capabilities of Large Language Models (LLMs) to the immutable audit trails of blockchain technology. Specifically, we integrate Gemini 2.0 Pro for logic-based vulnerability detection with the Avalanche C-Chain for tamper-evident artifact logging. Unlike fully decentralized solutions that suffer from high latency, Zer0n employs a hybrid architecture: execution remains off-chain for performance, while integrity proofs are finalized on-chain. Our evaluation on a dataset of 500 endpoints reveals that this approach achieves 80% detection accuracy with only a marginal 22.9% overhead, effectively demonstrating that decentralized integrity can coexist with high-speed security workflows.

翻译：随着漏洞研究日益采用生成式人工智能，对不透明模型输出的关键依赖已经显现，这在安全自动化中形成了一个“信任鸿沟”。我们通过提出Zer0n框架来解决这一问题，该框架将大型语言模型（LLMs）的推理能力锚定在区块链技术的不可变审计追踪上。具体而言，我们整合了Gemini 2.0 Pro用于基于逻辑的漏洞检测，并利用Avalanche C-Chain进行防篡改的成果物记录。与那些因高延迟而受限的完全去中心化解决方案不同，Zer0n采用混合架构：执行过程为保障性能保持在链下进行，而完整性证明则在链上最终确认。我们在包含500个端点的数据集上的评估表明，该方法实现了80%的检测准确率，且仅带来22.9%的边际开销，有效证明了去中心化的完整性保障能够与高速安全工作流共存。