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Run a model with reduced cyber safeguards only inside an environment whose boundaries you have verified—not one you merely describe in a prompt. Get authorization, deny open internet access by default, keep credentials outside the sandbox, define allowed targets and actions, test the isolation before the evaluation, and monitor the run with a human able to stop it.
What does “without cyber guardrails” mean in a safe test?
It means evaluating a model with some of its behavioral safeguards reduced or absent. It does not mean removing the technical controls around the model. The sandbox, network policy, access limits, monitoring, and stop mechanism must remain in force even when the model is being tested for behavior that ordinary guardrails might block.
A prompt is not a security boundary. Instructions can tell the model what it is authorized to do, but they cannot prove that the environment prevents other actions. Treat containment as a technical control to verify before the run and monitor while it is underway.
What must be authorized and in scope?
Test only models, code, data, services, and targets you own or have express authorization to assess. OpenAI’s red-teaming guidance specifically warns against submitting third-party code or assets to its service without written permission.
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Before launching, write down the exact model and configuration, targets, allowed tools and actions, prohibited actions, network routes, data-handling rules, run limits, and conditions that require stopping. Make the boundary unambiguous to both the operator and the model.
| Scope item | Specify before the run |
|---|---|
| Targets | The exact systems, services, or challenge assets the model may interact with. |
| Permitted actions | Which tools and operations are allowed, and any limits on their use. |
| Prohibited actions | Systems, data, or activities that are out of bounds, including attempts to reach unrelated external services. |
| Network | Whether the environment is offline, may connect only to the model API, or has another explicitly approved route. |
| Data and access | What data may be used, what must not leave the environment, and which narrowly scoped permissions are necessary. |
| Stop conditions | Observable events that require review or immediate termination, and who is responsible for stopping the run. |
State the authorized boundary clearly in the model’s instructions as well. Anthropic’s partner guidance recommends directly naming the targets, permitted actions, and network boundaries, including what the model should not access. This instruction supplements—not replaces—the sandbox controls.
Which network setup should you use?
Anthropic recommends hardened cyber-evaluation sandboxes with no internet access by default. If the model needs an API connection, that should ordinarily be the only outside connection. Broader internet access should be an explicit exception tied to the evaluation question, not a convenience setting.
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| Network approach | When it fits | Key consideration |
|---|---|---|
| Offline | The model and challenge can be evaluated without outside connectivity. | Provides the narrowest network boundary; verify that the task remains solvable with the supplied local resources. |
| Model API only | The evaluation needs an external model API but no other internet access. | Allow only the necessary route, keep API keys outside the environment, and verify the restriction before each run. |
| Explicitly scoped internet | External connectivity is essential to the stated evaluation, such as a controlled cyber range. | Define approved destinations and detection and stop procedures in advance. Broad connectivity increases the chance that a configuration mistake exposes unrelated systems. |
OpenAI’s 2026 account describes two different evaluation situations: UK AISI intentionally enabled internet access in a cyber range to measure capability, while an Irregular CTF-style evaluation intended to be isolated was exposed to the public internet through a configuration error. In the latter case, a fictional target name matched a real domain and the model interacted with the real site. These examples show why both explicit authorization and technical verification matter; they do not establish a universal likelihood of sandbox escape.
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How do you validate the sandbox before testing the model?
Test the environment first, under close human supervision. Anthropic recommends asking the model to inspect the sandbox configuration for weaknesses and attempt to escape while offline, ideally within a second sandbox. Treat this as a controlled preflight, not permission to probe external systems. Stop and report if a boundary is crossed.
- Confirm the task is feasible. Check that the authorized challenge can be completed with the resources and permissions actually supplied. An impossible or underspecified task can create pressure to seek out-of-scope resources.
- Verify the configuration. Check the sandbox and network policy before every evaluation run, including that no unintended public internet route is available and that any permitted API route is limited to what is needed.
- Run the supervised boundary check. Have the model inspect the environment and attempt to escape while offline; use a second sandbox for this check where practical. Watch its actions and network activity.
- Resolve failures before continuing. If the model reaches a prohibited resource or the configuration behaves differently from the written scope, stop the run, investigate, and correct the setup before any further evaluation.
Do not treat one successful check as a permanent guarantee. Recheck the configuration before repeated runs because a changed model setup, tool, or network rule can alter the effective boundary.
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How should you monitor a live run?
Continuously compare the model’s actions, tool calls, and network activity with the approved scope. Use a monitor that knows the scope and can alert a human; higher-risk work calls for active human oversight rather than logs reviewed only after completion. Assign a responsible person and confirm the stop mechanism works before launch.
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- Keep credentials outside the sandbox. If access is necessary, provide only narrowly scoped permissions needed for that run.
- Do not include sensitive production systems or the open internet in a controlled workflow unless the evaluation specifically requires an approved, tightly scoped connection.
- Record the model version and configuration, dataset version, sandbox and network settings, monitor behavior, outcomes, and deviations so later runs can be compared meaningfully.
OpenAI’s security guidance also emphasizes excluding sensitive production systems and the open internet from controlled security workflows and regularly testing the sandbox boundary. Monitoring is not a substitute for isolation: it is a further chance to detect and stop a failure.
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How do you make the evaluation useful as well as contained?
Use ordinary evaluations and adversarial red teaming for complementary purposes. Benchmarks and application-relevant safety datasets help measure intended behavior; adversarial testing probes misuse, failure modes, and unexpected interactions. OpenAI describes red teaming as complementary to ordinary evaluations, while Google’s guidance recommends application-relevant datasets, diverse adversarial inputs, and held-out data when feasible.
Build coverage around the risks relevant to the application rather than relying on one generic prompt set. Google’s toolkit identifies areas including prompt injection, poisoning, adversarial inputs, prompt extraction, training-data privacy, model extraction, membership inference, denial of service, and increased-computation attacks. Not every evaluation needs every category; choose based on the system, tools, data, and threat model.
Promptfoo is identified in OpenAI’s red-teaming documentation as an open-source framework for evaluating prompts, agents, and AI applications. Such a framework can help generate adversarial cases and inspect results, but using it does not itself establish or guarantee a secure sandbox boundary.
What isolation approach should you choose?
There is no single configuration suitable for every evaluation, and the guidance below is a set of trade-offs rather than a vendor ranking or guarantee. Choose controls based on the model interface, tools, target, threat model, and whether outside connectivity is essential.
| Decision | Narrower-risk choice | When to consider more |
|---|---|---|
| Network access | Offline, or model-API-only connectivity when required. | Allow internet access only when the evaluation needs it and destinations, monitoring, and stop conditions are explicitly scoped. |
| Isolation strength | Use a hardened isolated environment and verify its boundary. | Higher-risk work may warrant more hardened virtualization and a separate preflight sandbox. |
| Monitoring | Log activity against the written scope. | Use real-time monitoring, human escalation, and a tested stop mechanism when the possible impact is higher. |
| Evaluation method | Use behavior metrics for intended tasks. | Add adversarial red-team probes to investigate misuse and unexpected interactions; mature programs can use both. |
| Data validity | Use cases relevant to the application. | Broaden adversarial coverage and reserve held-out assurance data when feasible. |
| Operational fit | Use a local workflow when it meets the evaluation needs and data-handling requirements. | For managed enterprise assessment, confirm provider terms and data handling directly. |
No source cited here establishes a general probability of sandbox escape or a numeric threshold that makes an environment “safe.” Treat the configuration as a control that must be validated for the particular run, not a guarantee inferred from its label or provider.
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