AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

• URL: http://arxiv.org/abs/2605.29801v1
• Date: Thu, 28 May 2026 11:48:37 GMT
• Title: AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
• Abstract summary: We propose a lightweight and scalable agent safety alignment framework.<n>We update the agent safety taxonomy to accommodate emergent risks from Codex and OpenClaw execution scenarios.<n>Based on AgentDoG 1.5, we construct a highly efficient agentic safety SFT and RL training environment.<n>We deploy AgentDoG 1.5 as a training-free online guardrail for real-time safety moderation.
• Score: 147.50046962090795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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