arXiv:2606.20023v1 Announce Type: cross 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 low
As LLM agents become increasingly autonomous and integrated into critical systems, their operational security and ethical decision-making are under intense scrutiny.
This research highlights a critical vulnerability in AI agent deployment, where over-privileged tool selection can lead to significant security risks and unintended consequences, affecting trust and adoption.
The focus shifts from mere functionality to security and privilege management in AI agent design, requiring new benchmarks and development practices to prevent 'privilege escalation' within autonomous systems.
- · AI security researchers
- · Cybersecurity firms
- · Developers of secure AI agent frameworks
- · Unsecured AI agent deployments
- · Organizations relying on unchecked AI autonomy
- · Developers neglecting security in AI agent design
Immediate industry focus will shift to developing and implementing rigorous privilege control mechanisms for AI agents.
New regulatory frameworks and compliance standards will emerge specifically addressing AI agent security and autonomous decision-making permissions.
The concept of 'least privilege' will become a foundational principle in AI system architecture, influencing broader software development practices.
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Read at arXiv cs.CL