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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18491 |
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| _version_ | 1866908988667330560 |
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| author | Liu, Dongrui Ren, Qihan Qian, Chen Shao, Shuai Xie, Yuejin Li, Yu Yang, Zhonghao Luo, Haoyu Wang, Peng Liu, Qingyu Hu, Binxin Tang, Ling Mei, Jilin Guo, Dadi Yuan, Leitao Yang, Junyao Chen, Guanxu Lin, Qihao Yu, Yi Zhang, Bo Guo, Jiaxuan Zhang, Jie Shao, Wenqi Deng, Huiqi Xi, Zhiheng Wang, Wenjie Wang, Wenxuan Shen, Wen Chen, Zhikai Xie, Haoyu Tao, Jialing Dai, Juntao Ji, Jiaming Ba, Zhongjie Zhang, Linfeng Liu, Yong Zhang, Quanshi Zhu, Lei Wei, Zhihua Xue, Hui Lu, Chaochao Shao, Jing Hu, Xia |
| author_facet | Liu, Dongrui Ren, Qihan Qian, Chen Shao, Shuai Xie, Yuejin Li, Yu Yang, Zhonghao Luo, Haoyu Wang, Peng Liu, Qingyu Hu, Binxin Tang, Ling Mei, Jilin Guo, Dadi Yuan, Leitao Yang, Junyao Chen, Guanxu Lin, Qihao Yu, Yi Zhang, Bo Guo, Jiaxuan Zhang, Jie Shao, Wenqi Deng, Huiqi Xi, Zhiheng Wang, Wenjie Wang, Wenxuan Shen, Wen Chen, Zhikai Xie, Haoyu Tao, Jialing Dai, Juntao Ji, Jiaming Ba, Zhongjie Zhang, Linfeng Liu, Yong Zhang, Quanshi Zhu, Lei Wei, Zhihua Xue, Hui Lu, Chaochao Shao, Jing Hu, Xia |
| contents | The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk awareness and transparency in risk diagnosis. To introduce an agentic guardrail that covers complex and numerous risky behaviors, we first propose a unified three-dimensional taxonomy that orthogonally categorizes agentic risks by their source (where), failure mode (how), and consequence (what). Guided by this structured and hierarchical taxonomy, we introduce a new fine-grained agentic safety benchmark (ATBench) and a Diagnostic Guardrail framework for agent safety and security (AgentDoG). AgentDoG provides fine-grained and contextual monitoring across agent trajectories. More Crucially, AgentDoG can diagnose the root causes of unsafe actions and seemingly safe but unreasonable actions, offering provenance and transparency beyond binary labels to facilitate effective agent alignment. AgentDoG variants are available in three sizes (4B, 7B, and 8B parameters) across Qwen and Llama model families. Extensive experimental results demonstrate that AgentDoG achieves state-of-the-art performance in agentic safety moderation in diverse and complex interactive scenarios. All models and datasets are openly released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18491 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security Liu, Dongrui Ren, Qihan Qian, Chen Shao, Shuai Xie, Yuejin Li, Yu Yang, Zhonghao Luo, Haoyu Wang, Peng Liu, Qingyu Hu, Binxin Tang, Ling Mei, Jilin Guo, Dadi Yuan, Leitao Yang, Junyao Chen, Guanxu Lin, Qihao Yu, Yi Zhang, Bo Guo, Jiaxuan Zhang, Jie Shao, Wenqi Deng, Huiqi Xi, Zhiheng Wang, Wenjie Wang, Wenxuan Shen, Wen Chen, Zhikai Xie, Haoyu Tao, Jialing Dai, Juntao Ji, Jiaming Ba, Zhongjie Zhang, Linfeng Liu, Yong Zhang, Quanshi Zhu, Lei Wei, Zhihua Xue, Hui Lu, Chaochao Shao, Jing Hu, Xia Artificial Intelligence Computational Complexity Computation and Language Computer Vision and Pattern Recognition Machine Learning The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk awareness and transparency in risk diagnosis. To introduce an agentic guardrail that covers complex and numerous risky behaviors, we first propose a unified three-dimensional taxonomy that orthogonally categorizes agentic risks by their source (where), failure mode (how), and consequence (what). Guided by this structured and hierarchical taxonomy, we introduce a new fine-grained agentic safety benchmark (ATBench) and a Diagnostic Guardrail framework for agent safety and security (AgentDoG). AgentDoG provides fine-grained and contextual monitoring across agent trajectories. More Crucially, AgentDoG can diagnose the root causes of unsafe actions and seemingly safe but unreasonable actions, offering provenance and transparency beyond binary labels to facilitate effective agent alignment. AgentDoG variants are available in three sizes (4B, 7B, and 8B parameters) across Qwen and Llama model families. Extensive experimental results demonstrate that AgentDoG achieves state-of-the-art performance in agentic safety moderation in diverse and complex interactive scenarios. All models and datasets are openly released. |
| title | AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security |
| topic | Artificial Intelligence Computational Complexity Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.18491 |