arXiv:2605.09163v3 Announce Type: replace Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as an organizational abstraction, but we argue it is also a privilege boundary that current models routinely exceed. We present \textbf{FORTIS}, a benchmark that evaluates over-privilege in agent skills across two stages: whether a model selects the minimally sufficient skill from a large overlapping library, and whether it executes that skill without expanding into br
The proliferation of large language model agents performing complex tasks necessitates a robust framework for evaluating their capabilities and potential security vulnerabilities, like over-privilege.
Evaluating and mitigating over-privilege in AI agents is critical for ensuring secure, reliable, and ethical deployment of autonomous systems, preventing unintended actions and data breaches.
The introduction of the FORTIS benchmark provides a standardized method to quantify and address over-privilege, potentially leading to more secure and finely controlled AI agent skill execution.
- · AI developers focused on security
- · Enterprises deploying AI agents
- · Cybersecurity researchers
- · Users of AI agent systems
- · Developers of insecure AI agents
- · Systems vulnerable to privilege escalation
- · Attackers exploiting AI agent flaws
AI agents will be developed with more granular control and better skill selection mechanisms.
Increased trust and adoption of AI agents in sensitive applications as security concerns are addressed.
New regulatory standards and certifications emerge for AI agent security and privilege management.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI