Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
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 assesses requests using flattened textual tool specifications while retaining the original schema-formatted specifications for execution.
- ▪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 uns
- ▪Building on this finding, we propose SafeKeep, an inference-time safeguard that decouples safety judgment from tool execution: it assesses requests using flattened textual tool specifications while retaining the original schema-formatted sp
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.29254 |
| Publication time | Mon, 03 Aug 2026 15:37:00 +0000 |
| Retrieval time | 2026-08-03T15:45:42.943Z |
| Last seen | 2026-08-03T15:45:42.943Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2607.29254 (cs) [Submitted on 31 Jul 2026] Title:Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents Authors:Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan, Yu Jiang, Zhenpeng Chen View a PDF of the paper titled Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents, by Minghui Pan and 4 other authors View PDF HTML (experimental) Abstract: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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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