AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security
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.
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Dongrui Liu
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2026-02-20T08:38:14.334Z
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Dongrui Liu
shenqiorient
user
shenqiorient
697831d9026bdf0473116e5d
Qihan Ren
claimed_verified
2026-01-28T11:31:15.765Z
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Qihan Ren
jasonrqh
user
jasonrqh
697831d9026bdf0473116e5e
Chen Qian
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Shuai Shao
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Yuejin Xie
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Yu Li
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Zhonghao Yang
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Haoyu Luo
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Peng Wang
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Qingyu Liu
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Binxin Hu
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Ling Tang
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Jilin Mei
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Dadi Guo
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Leitao Yuan
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Junyao Yang
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Guanxu Chen
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Qihao Lin
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Yi Yu
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Bo Zhang
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Jiaxuan Guo
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Jie Zhang
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Wenqi Shao
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Huiqi Deng
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Zhiheng Xi
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Wenjie Wang
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Wenxuan Wang
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Wen Shen
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Zhikai Chen
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Haoyu Xie
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Jialing Tao
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Juntao Dai
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Jiaming Ji
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Zhongjie Ba
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Linfeng Zhang
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Yong Liu
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Quanshi Zhang
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Lei Zhu
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Zhihua Wei
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Hui Xue
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Chaochao Lu
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Jing Shao
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Xia Hu
AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security
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.
AI agents face safety and security challenges from autonomous tool use and environmental interactions, requiring advanced guardrail frameworks for risk diagnosis and transparent monitoring.
agentic guardrail three-dimensional taxonomy agentic safety benchmark Diagnostic Guardrail framework agent safety and security agent trajectories root cause diagnosis fine-grained monitoring model variants state-of-the-art performance