AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework

2026-05-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A 2026 article introduces a comprehensive framework for AI sandboxes, specifically addressing the complex assurance needs of physical AI, AIoT, and cyber-physical systems. It formalizes AI sandboxes as controlled, instrumented environments for testing, evaluation, verification, and validation under bounded cyber-physical risk. The framework includes a cross-domain definition and taxonomy, separating archetypes like simulation-based, digital twins, adversarial, regulatory, and agent-based sandboxes. Crucially, it presents a cyber-physical threat model that targets the evaluation apparatus and its evidence chain, not just the system under test, covering issues like isolation failure and attacks on digital-twin state. Additionally, a measurement framework with fifteen dimensions—including fidelity, controllability, observability, and reproducibility—is provided, along with a weakest-link rule for composing evidence, instantiated on three case studies. This work clarifies what sandboxes can validly test and the evidence they support for safety, security, and regulatory assurance.

Key takeaway

For MLOps Engineers deploying physical AI or cyber-physical systems, you must rigorously define your sandbox's experimental boundary. Over-interpreting simulation results without considering fidelity, containment, and governance artifacts introduces significant risk. Use the proposed 15-dimension measurement framework and weakest-link rule to validate evidence, ensuring your assurance claims are robust and standards-aligned. This approach helps prevent over-claiming and protects against attacks on the evaluation apparatus itself.

Key insights

AI sandboxing for physical and cyber-physical systems requires a system-level assurance discipline to interpret evidence validly.

Principles

• Sandbox boundaries define represented dynamics and contained risks.
• Evidence validity requires explicit assumption gates.
• A weakest-link rule composes per-dimension evidence.

Method

The article proposes a framework involving a cross-domain definition and taxonomy, a cyber-physical threat model targeting the evaluation apparatus, and a 15-dimension measurement framework with a weakest-link composition rule.

In practice

• Apply the 15-dimension measurement framework.
• Use the weakest-link rule for evidence composition.
• Design sandboxes aligned with AI risk management standards.

Topics

• AI Sandboxes
• Cyber-Physical Systems
• Threat Modeling
• Assurance Frameworks
• AI System Evaluation
• Regulatory Compliance

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.