Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents

• URL: http://arxiv.org/abs/2607.29254v1
• Date: Fri, 31 Jul 2026 10:25:04 GMT
• Title: Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
• Abstract summary: We identify schema-formatted tool specifications as a primary source of agent safety degradation.<n>We propose SafeKeep, an inference-time safeguard that decouples safety judgment from tool execution.<n>It increases the average refusal rate for harmful requests from 23.8% to 70.6% and reduces the average attack success rate under observation-level prompt injection from 25.6% to 2.5%.
• Score: 21.622196229181103
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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. Across two representative benchmarks and four LLMs, including both white-box and black-box models, SafeKeep increases the average refusal rate for harmful requests from 23.8% to 70.6% and reduces the average attack success rate under observation-level prompt injection from 25.6% to 2.5%. It also outperforms existing safeguards and preserves task-handling capability. We release the code and data at https://github.com/snowcatsmoking/SafeKeep .

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