AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
arXiv:2605.29801v1 Announce Type: cross 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
The rapid advancement of open-world AI agents and frontier models necessitates urgent development of robust safety and alignment frameworks to address emerging risks.
The proliferation of powerful AI agents introduces complex safety and security challenges that, if unaddressed, could undermine the beneficial deployment of advanced AI, impacting various industries and national security.
This research introduces an updated safety taxonomy and a lightweight, scalable framework for aligning AI agents, specifically addressing new risks posed by advanced systems like OpenClaw and Codex.
- · AI safety researchers
- · AI development companies
- · Cybersecurity sector
- · Governments
- · Malicious actors
- · AI systems lacking robust safety
- · Organizations with inadequate AI governance
More secure and reliable deployment of advanced AI agents in diverse applications.
Increased public and institutional trust in AI technologies, accelerating adoption across critical sectors.
The establishment of international standards and collaborative efforts for AI agent safety and ethical deployment through shared frameworks and taxonomies.
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Read at arXiv cs.LG