arXiv — NLP / Computation & Language · · 3 min read

When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents

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Computer Science > Software Engineering

arXiv:2606.20023 (cs)
[Submitted on 18 Jun 2026]

Title:When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents

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Abstract:As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safety-relevant. However, prior tool-selection studies focus on safety-agnostic metadata preferences, leaving privilege-sensitive choices underexplored. To address this gap, we study over-privileged tool selection, in which an agent selects or escalates to a higher-privilege tool despite a sufficient lower-privilege alternative. We introduce ToolPrivBench to evaluate whether agents choose higher-privilege tools despite sufficient lower-privilege alternatives, measuring both initial selection and escalation after transient tool failures. Across eight domains and five recurring risk patterns, we find that over-privileged tool selection is common among mainstream LLM agents and is further amplified by transient failures. We further find that general safety alignment does not reliably transfer to least-privilege tool choice, while prompt-level controls provide only limited mitigation under transient failures. We therefore introduce a privilege-aware post-training defense that teaches agents to prefer sufficient lower-privilege tools and escalate only when necessary. Our mitigation experiments show that this defense substantially reduces unnecessary high-privilege tool use while preserving general capabilities.
Comments: code: this https URL
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2606.20023 [cs.SE]
  (or arXiv:2606.20023v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2606.20023
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kaiyue Yang [view email]
[v1] Thu, 18 Jun 2026 09:54:48 UTC (980 KB)
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