AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
Authors: name: Dongrui Liu, name: Yu Li, name: Zhonghao Yang, name: Peng Wang, name: Guanxu Chen, name: Yuejin Xie, name: Qinghua Mao, name: Wanying Qu, name: Yanxu Zhu, name: Tianyi Zhou, name: Leitao Yuan, name: Zhijie Zheng, name: Qihao Lin, name: Yimin Wang, name: Haoyu Luo, name: Shuai Shao, name: Chen Qian, name: Qingyu Liu, name: Ling Tang, name: Ruiyang Qin, name: Qihan Ren, name: Junxiao Yang, name: Kun Wang, name: Zhiheng Xi, name: Linfeng Zhang, name: Ranjie Duan, name: Bo Zhang, name: Wenjie Wang, name: Wen Shen, name: Qiaosheng Zhang, name: Yan Teng, name: Chaochao Lu, name: Rui Mei, name: Man Li, name: Jialing Tao, name: Xi Lin, name: Tianhang Zheng, name: Yong Liu, name: Quanshi Zhang, name: Lei Zhu, name: Xingjun Ma, name: Junhua Liu, name: Hui Xue, name: Xiaoxiang Zuo, name: Xiangnan He, name: Chao Shen, name: Xianglong Liu, name: Minlie Huang, name: Jing Shao, name: Xia Hu
Organization: shanghai ailab 

Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI models drastically lower attack barriers, rendering current agent alignment frameworks inadequate for real-world deployment. To tackle these emerging threats, we propose a lightweight and scalable agent safety alignment framework. Specifically, we update the agent safety taxonomy to accommodate emergent risks from Codex and OpenClaw execution scenarios. We further build a taxonomy-guided data engine with influence-function purification to train lightweight AgentDoG 1.5 variants (0.8B, 2B, 4B, and 8B parameters) using only around 1k samples, achieving comparable performance with leading closed-source models (e.g., GPT-5.4). Based on AgentDoG 1.5, we construct a highly efficient agentic safety SFT and RL training environment, which reduces deployment overhead in Docker-level environments by two orders of magnitude. Finally, we deploy AgentDoG 1.5 as a training-free online guardrail for real-time safety moderation. Extensive experimental results indicate that AgentDoG 1.5 achieves state-of-the-art performance in diverse and complex interactive agentic scenarios. All models and datasets are openly released.