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

Research LLM Agents

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Merged summary

TL;DR - An arXiv paper identifying schema-formatted tool specifications as a primary cause of safety degradation in LLM agents, and proposing SafeKeep, an inference-time safeguard that restores refusal behavior without hurting task performance.

  • White-box representation analysis shows JSON/schema-style tool specs weaken the model's internal refusal signals, contributing to unsafe tool execution.
  • SafeKeep decouples safety judgment from execution: it evaluates requests against flattened textual tool specs while keeping the original schema-formatted specs for actual tool calls.
  • Across two benchmarks and four LLMs (white-box and black-box), refusal rate on harmful requests rose from 23.8% → 70.6%, and attack success under observation-level prompt injection fell from 25.6% → 2.5%.
  • Reported to outperform existing safeguards while preserving task-handling capability; code and data released at github.com/snowcatsmoking/SafeKeep.

Sources (1)

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

arXiv cs.AI Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan, Yu Jiang, Zhenpeng Chen 2026-07-31 arXiv:2607.29254
Public signals Hugging Face upvotes 2
Providers: Hugging Face · Upvotes 2 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-31 14:29:40.497399 UTC

TL;DR - An arXiv paper identifying schema-formatted tool specifications as a primary cause of safety degradation in LLM agents, and proposing SafeKeep, an inference-time safeguard that restores refusal behavior without hurting task performance.

  • White-box representation analysis shows JSON/schema-style tool specs weaken the model's internal refusal signals, contributing to unsafe tool execution.
  • SafeKeep decouples safety judgment from execution: it evaluates requests against flattened textual tool specs while keeping the original schema-formatted specs for actual tool calls.
  • Across two benchmarks and four LLMs (white-box and black-box), refusal rate on harmful requests rose from 23.8% → 70.6%, and attack success under observation-level prompt injection fell from 25.6% → 2.5%.
  • Reported to outperform existing safeguards while preserving task-handling capability; code and data released at github.com/snowcatsmoking/SafeKeep.
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