KNOX runs generative models in a TEE regulatory sandbox
Original title: KNOX: Hardware-assisted Regulatory Sandbox for Generative Models
An attested monitor allows only matching tool and system calls, with authors reporting overhead under 1%.
KNOX is a regulatory sandbox that runs AI workloads inside trusted execution environments so regulators can monitor behavior without receiving model weights. For each prompt it derives an attested policy manifest of rules, expected tool calls, and permitted system calls, and a reference monitor allows an action only if it matches. Actions and verdicts are stored in an attested execution graph. The authors say it can flag regulatory breaches and prompt-injection deviations.
Unconfirmed: Compliance coverage and the sub-1% overhead are the authors' results, not an independent evaluation.
- Stated goals are compliance, weight confidentiality, attestability, and multi-regulator checks.
- The demonstration uses agentic workloads.
- Authors say placing a model in a TEE alone is not enough for compliance.
- Reported overhead is under 1%.
From the original article
1 min read at arxiv.orgRegulations require companies to run AI models in a regulatory sandbox, an isolated environment that enables monitoring model behavior by regulators, prior to deployment. But, they do not spell out the technical mechanisms to design such a sandbox. Ideally, a regulatory sandbox should show (i) compliance (checks against regulations, and deviations from intended behavior, i.
By Vasisht Duddu · arxiv.org
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