Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety guardrails excel in English or multilingual settings, they lack adaptation to Chinese-specific regulatory policies, cultural context and linguistic nuances, failing to support fine-grained risk classification for diverse deployment needs. In this paper, we introduce a 5-macro, 31-micro category fine-grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guardrail. To address the critical scarcity of high-quality annotated Chinese safety data, we propose a scalable multi-stage data construction pipeline: we expand multi-source corpus via retrieval-augmented generation, generate implicit harmful samples through prompt engineering rewriting, and refine high-quality data via multi-model voting-based label calibration. Based on this, we build CHILLGuardTrain, a large-scale training set with 405,007 samples, and CHILLGuardTest, a rigorously curated annotated test set with 51,745 samples. We then train CHILLGuard on CHILLGuardTrain under a generator-classifier collaborative framework via Model-aware Direct Preference Optimization. Extensive experiments under multiple settings demonstrate the state-of-the-art performance of CHILLGuard, e.g., a 15.92% improvement of F1 score over Qwen3Guard-8B-Strict on our benchmark. We will release our resources at https://github.com/cswbyu/CHILLGuard.

翻译：大语言模型生成的恶意内容可能引发严重的安全风险和伦理问题。现有的大语言模型安全护栏在英文或多语言环境中表现优异，但缺乏对中文监管政策、文化语境和语言细微差别的适配，无法支持多样化部署所需的细粒度风险分类。本文提出适用于中文场景的5大类31小类细粒度风险分类体系，并构建了专用中文大语言模型内容安全护栏CHILLGuard。针对高质量中文安全标注数据严重稀缺的问题，我们提出可扩展的多阶段数据构建流水线：通过检索增强生成扩展多源语料，利用提示工程改写生成隐式有害样本，并基于多模型投票的标签校准精炼高质量数据。在此基础上构建了包含405,007条样本的大规模训练集CHILLGuardTrain与严格筛选的51,745条标注测试集CHILLGuardTest。随后在生成器-分类器协同框架下，采用模型感知直接偏好优化（Model-aware DPO）在CHILLGuardTrain上训练CHILLGuard。多场景实验表明，CHILLGuard达到当前最优性能，例如在基准测试中F1分数较Qwen3Guard-8B-Strict提升15.92%。相关资源将发布于https://github.com/cswbyu/CHILLGuard。