YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models
Junyu Lin, Meizhen Liu, Xiufeng Huang, Jinfeng Li, Haiwen Hong, Xiaohan Yuan, Yuefeng Chen, Longtao Huang, Hui Xue, Ranjie Duan, Zhikai Chen, Yuchuan Fu, Defeng Li, Lingyao Gao, Yitong Yang

TL;DR
YuFeng-XGuard is a novel, reasoning-centric guardrail model for LLMs that provides interpretable, multi-dimensional risk assessments with configurable policies, balancing safety, efficiency, and flexibility.
Contribution
It introduces a structured, reasoning-based approach for risk perception in LLMs, enabling interpretable, flexible, and efficient safety guardrails without retraining.
Findings
Achieves state-of-the-art safety performance on public benchmarks.
Balances decision speed and interpretability effectively.
Offers both full and lightweight model variants for diverse deployment.
Abstract
As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained, interpretable, and adaptable risk assessment. However, existing solutions often rely on rapid classification schemes or post-hoc rules, resulting in limited transparency, inflexible policies, or prohibitive inference costs. To this end, we present YuFeng-XGuard, a reasoning-centric guardrail model family designed to perform multi-dimensional risk perception for LLM interactions. Instead of producing opaque binary judgments, YuFeng-XGuard generates structured risk predictions, including explicit risk categories and configurable confidence scores, accompanied by natural language explanations that expose the underlying reasoning process. This formulation enables safety decisions that are both actionable and…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI) · Safety Systems Engineering in Autonomy
