AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework
AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate, communicate, and fail through physical processes, networked devices, and human operators. This article develops an assurance-oriented account of AI sandboxes as controlled environments for testing, evaluation, verification, and validation across digital AI, embodied autonomy, and cyber-physical deployments. We formalize the sandbox boundary and a weakest-link rule for composing per-dimension evidence...
| Authors | Inderjeet Singh, Haitham Mahmoud, Andrés Murillo |
|---|---|
| Primary category | cs.CR |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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