Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions. Yet LLMs often become substantially less safe when deployed as agents, and the source of this degradation remains poorly understood. In this paper, we identify schema-formatted tool specifications as a primary source of agent safety degradation and show, through white-box representation analysis, that they weaken the model's internal refusal signals and contribute to unsafe tool execution. Building on this finding, we propose SafeKeep, an inference-time safeguard that decouples safety judgment from tool execution: it a...
| Authors | Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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