The Tool Desk
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Usually, no. Start an AI agent evaluation with no external network access, then allow only the connections the task genuinely requires. If the evaluation is specifically measuring browsing or live API use, provide controlled egress through an enforced policy or proxy, keep credentials outside the sandbox, isolate each run, and monitor network activity. The right setting depends on what the agent is supposed to do—not on a blanket preference for either realism or restriction.
Why does network access change what an agent evaluation measures?
Network access is part of an evaluation’s conditions, not just a convenience for the agent. It changes which information and services are available, what actions the agent can attempt, and what a successful result means. An offline coding test, for example, measures work against the files and tools already provided. A browsing test measures something different if the agent can reach the public internet or a specified set of services.
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The UK AI Safety Institute (AISI) describes three network settings: full internet (N0), allowlisted domains (N1), and no external network (N2). Anthropic’s guidance for cyber evaluations says to use a hardened sandbox with no internet access by default, allowing a connection to the model API while keeping API keys outside the environment. Taken together, these recommendations support a practical approach: deny external access initially, then add only what the evaluation needs.
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Which network setting should I choose for an eval?
Choose the narrowest setting that still lets the agent solve the task as intended. AISI’s N0/N1/N2 framework makes the trade-off explicit: more access can improve task fidelity when live services matter, but it also increases the containment and reproducibility demands.
| Setting | Network access | Best fit | Main trade-off |
|---|---|---|---|
| N2 | No external network | Offline coding, file-based tasks, reasoning tests, and containment tests that do not require outside services. | May make a task unrealistic or unsolvable if it depends on live information or APIs. |
| N1 | Only allowlisted domains | Tasks requiring specified APIs, package mirrors, documentation sites, or a controlled web corpus. | Requires a carefully maintained allowlist and enforcement that the agent cannot bypass. |
| N0 | Full internet access | Tasks where unrestricted browsing itself is the capability being measured. | Creates the broadest exposure and requires strong isolation, monitoring, disposable credentials, and an incident plan. |
The labels describe the network axis, not a complete safety rating. An N2 environment can still be poorly isolated from its host, and an N0 environment is not automatically a valid browsing benchmark. Evaluate networking alongside filesystem, process, credential, and control-plane boundaries.
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Does blocking the network make an evaluation unrealistic?
It can, if the task depends on network resources the agent cannot reach. AISI cautions that overly restrictive environments can reduce realism and comparability. But realism does not mean granting every task unrestricted internet access: it means providing the resources the scenario is meant to include, under conditions that can be described and repeated.
For a task intended to test whether an agent can use a particular API, blocking that API prevents the evaluation from measuring the intended behavior. Conversely, giving full internet access to an offline code-editing task changes the scenario without adding a relevant capability. An allowlist can be a useful middle ground: it makes the required destination available while keeping unrelated sites out of scope.
When comparing results, record the access policy as part of the test conditions. Two runs with the same prompt and code but different network access are not necessarily measuring the same task. Where live responses can change, preserving the network conditions and recording responses helps distinguish a change in the agent from a change in what it could retrieve.
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How should I decide whether a task needs egress?
Write down the task’s required tools, hosts, APIs, and data flows before running the model. That turns “Does it need the internet?” into a more answerable question: which exact external dependency is needed to make this scenario solvable?
- Define the capability under test. Decide whether the evaluation is about offline work, use of named services, controlled web research, or unrestricted browsing.
- List necessary destinations and data flows. Identify the services or sources the scenario depends on. If none are required, use N2.
- Choose the smallest workable scope. Use N1 for specified destinations. Reserve N0 for evaluations where broad browsing is itself in scope.
- Check whether the access actually supports the task. Confirm that permitted endpoints expose the resources the scenario requires, rather than relying on the agent to explain that it could not reach them.
- Record the policy with the run. Preserve the network setting and relevant environment versions so that a later result can be compared against the same conditions.
This decision should be made at the scenario level. A benchmark may contain different tasks with different requirements; giving every task the most permissive policy is not necessary just because one task needs the network.
How can I let an eval call APIs safely?
Use an enforced network boundary and broker credentials outside the sandbox. OpenAI recommends isolated compute, outbound allowlists, and credential brokering through a proxy or trusted server. Docker’s documentation describes proxy-enforced outbound TCP rules and UDP being disabled by default. These are examples of controls that operate outside the agent’s own instructions.
Enforce the policy outside the agent
Use a proxy, firewall, or runtime policy to decide which outbound connections are permitted. Prompting the agent not to visit other sites is not a substitute for a network control: the policy should still apply if the agent ignores its instructions or the task code behaves unexpectedly. For N1, state the destinations the scenario needs and have the enforcement layer limit access accordingly.
Keep secrets in the trusted part of the system
Do not bake API keys into sandbox images or expose them in logs. Keep application and third-party credentials in the harness or a secrets manager, and use a trusted broker to make authorized requests. Where credentials are needed, scope them to the task and make them short-lived where the setup allows. Anthropic’s guidance likewise keeps model API keys outside the evaluation environment.
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A broker also makes the division of responsibility clearer: the agent operates in the sandbox, while a trusted component handles access to credentials and external services. The sandbox should not become the place where long-lived secrets are stored simply because the agent needs to call an API.
How should the sandbox and evaluation harness be separated?
Keep the harness control plane separate from sandbox execution. OpenAI’s sandbox guidance describes this separation, while NVIDIA recommends giving each evaluation problem its own filesystem, network, and process space. The aim is to prevent model-directed code in one run from inheriting authority over evaluation orchestration or neighboring runs.
The harness should retain responsibility for approvals, audit records, recovery, and billing outside the sandbox. The evaluation environment should be limited to the task’s compute and data. Giving each problem its own filesystem, network, and process space also limits unintended sharing between tasks and makes it easier to treat a run as a distinct unit.
Isolation should be considered across boundaries, not reduced to “the container has no internet.” A restrictive egress policy does not itself establish that the sandbox is separated from the host, the harness, or other runs. The environment and control plane have different jobs, so they should have different boundaries.
What should I test and monitor before trusting the setup?
Verify both the environment and its network policy before an evaluation batch, then observe activity during the run. AISI’s SandboxEscapeBench offers a sandbox-within-a-sandbox approach for testing breakout capability before relying on a setup. It is a way to probe containment, not a substitute for evaluating the policy and configuration actually used in the benchmark.
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- Validate the image and policy: confirm that the intended environment and access rules are the ones being launched.
- Probe containment: test the sandbox boundary before trusting it with evaluation runs.
- Observe activity: log commands, network requests, policy decisions, and violations.
- Stop out-of-scope runs: define a response for activity that violates the evaluation’s permitted scope.
- Recheck each batch: verify the configuration rather than assuming a previously tested setup has not changed.
Monitoring is useful only if it is tied to a policy and an operational response. Decide in advance what counts as a scope violation and who or what can stop a run; otherwise, an audit trail may document an incident without containing it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I make network-enabled eval results reproducible?
Record the conditions that can change what the agent sees or does. The recommended implementation pattern includes tracking image, policy, dependency, fixture, and network-response versions. Together, these make it possible to identify whether a result changed because the agent behaved differently or because the environment did.
Network access makes this especially important: an endpoint can return different data on different runs, and a changed allowlist can alter which information is available. Where practical, use controlled fixtures or a controlled web corpus for tasks that do not require live public browsing. For live-service tasks, record the relevant responses and policy details so later comparisons have a meaningful account of the inputs.
Reproducibility is not the same as denying all external access. It is a record of the conditions under which the result was produced. A well-described N1 or N0 task can be more informative than an N2 task if live access is central to the capability being evaluated.
What are the main trade-offs to compare?
Assess each proposed setup against the task, not against a single “secure versus realistic” score. The useful questions are whether the agent can reach required data, what can be affected if it behaves outside scope, and whether the run can be understood and repeated.
- Task fidelity: Can the agent reach the services or data the scenario requires?
- Containment: What limits impact on the host, neighboring runs, and external systems?
- Egress granularity: Are destinations, methods, ports, and paths constrained and enforced below the agent process?
- Credential exposure: Are secrets kept outside the sandbox and provided only through a trusted broker?
- Reproducibility: Can the image, policy, snapshots, fixtures, dependencies, and network responses be recorded?
- Observability: Are commands, requests, policy decisions, and violations logged?
- Operations: What latency, cost, and maintenance burden do the isolation and proxy controls add?
There is no universal failure-rate figure established for network-enabled evaluation sandboxes across platforms. Treat safety as something to validate for the particular architecture and policy, rather than assuming one percentage predicts the risk of a different setup.
Frequently Asked Questions
Can an AI agent still use tools if its sandbox has no internet?
Yes. External network access and access to tools already present in the evaluation environment are separate questions. A task can provide local files or other in-environment capabilities while blocking connections to outside systems.
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Should I allow access to a package registry during a coding benchmark?
Only if installing or retrieving packages is part of the intended task. If it is, specify the required package source and include that access in the recorded evaluation conditions; otherwise, provide the needed dependencies in the environment.
Is an outbound proxy enough to secure an agent evaluation?
No single proxy establishes the whole sandbox boundary. It can enforce or broker outbound access, but isolation of compute, filesystems, processes, credentials, and the harness still needs to be considered and validated.
How do I compare results from evals with different network settings?
Treat the settings as different test conditions unless you have established that the network difference could not affect the task. Report the access policy alongside the result so readers can see what information and services were available.
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