RATION: Towards Quantitative Risk-Based Access Control for AI Agents

Reported by arXiv cs.CR (Yuntao Du, Harsh Chaudhari, Yuetian Chen, Ron Heichman, Ashish Jayamohan, Kumar Pallav, Mahim Patel, Kaiyuan Zhang, Kyle Polley, Jerry Ma, Ninghui Li)

Summary by Cyber Security News20 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.24311

arXiv:2610.24311v1 Announce Type: new Abstract: AI agents increasingly interact with external environments to complete complex user tasks. These interactions can lead to unexpected behavior and expose agents to security threats such as indirect prompt injection. Current agent security systems largely protect agents by restricting their actions, which can result in a substantial security-utility trade-off for complex, open-ended tasks.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

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