How can security teams tell whether sandbox and proxy controls are actually containing an AI agent?

How to prove the sandbox is real, not just the upload button

The boundary should be tested at the paths the agent can actually use, not only at the path you intended. If a different client, runtime, or terminal route can still reach the internet, move files, or invoke tools, then the containment model is leaky. That is a practical control test, not a theoretical one.

One AI Coding Agents Security Guide angle to verify is whether the sandbox still holds when the agent operates outside the sanctioned command path, because real agent workflows often span the IDE, terminal, and CI/CD contexts. A test that only covers one client can miss the path the agent will actually choose under pressure.

What containment failures usually look like

Containment breaks when the policy is attached to one surface, but the agent can reach the same capability through another. That can happen with proxy bypasses, alternate runtimes, local helper scripts, or tools that inherit a more permissive network context than the sanctioned upload flow. The failure is not always a dramatic breakout, it is often a quiet policy mismatch.

An Browser and Computer-Use Agent Security Guide perspective is useful here because an agent that uses a human session or a different execution surface may appear “contained” while still preserving an indirect path to data transfer. That is why the strongest test is whether the agent can still complete the same class of action after the obvious network path is denied.

Zero Trust for AI Agents reinforces the same point: the control has to be per request and per action, not just per entry point. If the decision is made once at the front door, but the agent can later pivot through another path, the trust boundary was only partially enforced.

How to run a containment test that reflects real agent behaviour

The most useful test is a negative one: deny the most obvious route and then see whether the agent can still accomplish the same objective through a different channel. If it can move a file, reach a remote endpoint, or fetch external content another way, then the containment design has not constrained capability, only convenience.

AI Agent Authorisation Guide is relevant because the control question is not “can the agent ever do this?” but “is each action individually authorised under the least privilege needed for the task?” If a denied path can be replaced by an equivalent allowed path without a new decision, the policy is too coarse.

MCP Security Guide is a good analogue for this kind of test: tool mediation and token handling can create a false sense of containment if the model can reach the same capability through another client or server integration. The practical question is whether the control holds across all routes, not just the one you instrumented first.

Practitioner Guidance

What to verify: Test at least one alternate execution path, such as a different client, runtime, or terminal workflow, and confirm that the same task fails when the intended boundary is in place. If the agent can still complete the action through a secondary route, treat the boundary as incomplete rather than partially successful.

Decision rule: If you can only stop the agent by blocking one command or one tool, the control is too narrow. Good containment means the agent cannot substitute another route to achieve the same outcome without a new, explicit policy decision.

Practitioner takeaway: A sandbox is only meaningful when it constrains capability across the agent’s real paths, not just the path the security team happened to test first.

Related resources from NHI Mgmt Group

• How can security teams tell whether AI agent approval controls are actually working?
• How can security and platform teams tell whether AI coding agent rollout is actually controlled?
• How can security teams tell whether an AI agent compromise is actually contained?
• How can security teams tell whether AI safety controls are actually independent?