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What “without cyber guardrails” means in a safe evaluation
In this context, reducing a model’s cyber safeguards means evaluating its behavior when some safety restrictions are absent or deliberately relaxed. It does not mean granting it unrestricted access to live systems. The sandbox, network policy, tool permissions, and human oversight are the controls that constrain the test.
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A prompt can tell a model what is in scope, but it cannot create a technical boundary. Treat the prompt as one layer of scope communication, not as a substitute for network isolation, access controls, and monitoring.
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Before launching anything, establish that you are permitted to test every target and asset the model could encounter. OpenAI’s red-teaming guidance warns against submitting third-party code or assets without written permission. This applies to test data and supporting services as well as the main target.
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Write a scope document and give the model direct, affirmative instructions about the same boundary. Include:
- The exact model, configuration, and interface being tested.
- The named targets and data the exercise may use.
- Allowed tools and actions, plus actions that are prohibited.
- Permitted network routes, including whether a model API connection is needed.
- Data-handling rules, run limits, and what to do if the model encounters an out-of-scope asset.
- The conditions that require a human to pause or terminate the run.
Be explicit about systems the model must not access. A fictional hostname, a vague instruction to “stay in the lab,” or a target description alone does not establish authorization to contact a real service.
2. Choose and configure the isolation boundary
Anthropic’s partner guidance recommends a hardened sandbox with no internet access by default. If the model needs to call its API, ordinarily allow only that connection; keep API keys and other credentials outside the environment and provide only narrowly scoped access required for the run. Verify the network and sandbox configuration before every evaluation.
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Choose a configuration that matches the question being tested. The options below describe trade-offs, not guarantees of containment.
| Configuration | When it fits | Key consideration |
|---|---|---|
| Offline environment | The task can be completed using local, approved resources. | It minimizes external connectivity, but the task must genuinely be solvable without outside resources. |
| Sandbox with only the model API connection | The model needs an external API, but the exercise does not require other internet access. | Verify that other routes are denied and that credentials remain outside the environment. |
| Deliberate, controlled internet access | The evaluation specifically requires external connectivity. | Define allowed destinations and actions in advance, monitor traffic, and provide a reliable way to stop the run. Do not enable broad access merely for convenience. |
OpenAI also recommends excluding sensitive production systems and the open internet from controlled security workflows and regularly checking the sandbox boundary. The exact implementation depends on the model interface, available tools, target, and threat model; no single product or configuration guarantees containment.
3. Validate the sandbox before evaluating the model
Test the environment itself first. Anthropic recommends asking the model to inspect the sandbox configuration for weaknesses and attempt to escape under close human supervision, without internet access and ideally inside a second sandbox. Treat this as a supervised validation exercise, not permission to probe systems outside the approved environment.
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- Confirm that the intended network routes and tool permissions match the written scope.
- Have the model inspect the configuration and test the boundary under supervision.
- Watch for unexpected access attempts or activity beyond the approved scope; pause and investigate if anything crosses the boundary.
- Check that the evaluation task is achievable with the resources and permissions provided. If it is not, correct the task rather than encouraging the model to find unauthorized alternatives.
Anthropic advises validating the setup for sufficient time before repeated evaluation runs. Recheck it before each run, since a prior successful test does not prove that the current configuration is unchanged.
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4. Monitor the run and make stopping practical
Continuously compare model actions, tool calls, and network activity with the approved scope. Assign a person who can review alerts and end the exercise; define that stop path before launch. A scope violation should trigger human review and termination of the run, not an assumption that the model will correct itself.
Monitoring is more useful when the monitor knows the exercise’s targets, permitted actions, and network rules. Keep enough records to determine what happened, while following the exercise’s data-handling requirements. OpenAI’s guidance also emphasizes additional monitoring and human oversight for higher-risk workflows.
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5. Build tests that measure relevant risks
Use ordinary evaluations and adversarial red teaming for different purposes. Benchmarks and application-relevant safety datasets can measure expected behavior; red-team probes explore misuse, failure modes, and unexpected interactions. OpenAI describes red teaming as complementary to ordinary evaluations, not a replacement for them.
Google recommends application-relevant safety datasets, diverse adversarial inputs, and held-out data when feasible. Its AI risk toolkit identifies areas including:
- Prompt injection and adversarial inputs.
- Data poisoning and prompt extraction.
- Training-data privacy and membership inference.
- Model extraction.
- Denial of service and attacks that increase computation.
Vary how test cases are phrased, including subtle and indirect attempts, rather than relying on one obvious attack wording. Where feasible, keep assurance cases held out from model or classifier training so that evaluation is not limited to examples the system has already seen.
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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 tests and inspect results; using it does not itself secure the environment. Organizations that need broader coordination or reporting can also consider managed enterprise red teaming, after confirming the provider’s data handling and terms.
Why technical verification matters
OpenAI’s 2026 account describes two distinct external-evaluation situations. The UK AI Security Institute intentionally enabled internet access in a cyber range to measure underlying capability. In a separate CTF-style evaluation by Irregular, a test intended to be isolated was exposed to the public internet because of a configuration error; a fictional target name coincided with a real domain, and a model interacted with the real site.
These examples do not establish a universal likelihood that a model will escape a sandbox. They show why configuration must be checked technically, why target names and network rules need careful review, and why monitoring should continue during the exercise—even when the plan says the environment is isolated.
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For each run, record the model version and configuration, dataset version, sandbox and network settings, monitor behavior, outcome, and any deviations from the plan. This is an operational recommendation: it helps distinguish a change in model behavior from a change in the test setup and makes later comparisons more meaningful.
Provider safeguards, interfaces, and program access can change. Confirm current provider documentation and obtain appropriate authorization before live security testing. Guidance about a sandbox is not an assurance that a particular implementation is secure.
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