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No—not by default. Run an AI agent evaluation without external network access unless the task specifically measures browsing or interaction with live services. When network access is necessary, allow only the destinations the task requires, enforce the rules outside the agent’s control, and keep credentials and evaluation controls outside the sandbox.
Why should an agent evaluation start without network access?
A sandbox limits what model-directed code can affect. External connectivity can widen that impact: an agent may contact services beyond the intended task, use credentials in an unintended way, or make a run depend on external systems that change over time. Blocking external network access reduces those paths while you measure offline coding, file operations, reasoning, or containment.
Anthropic’s guidance for cyber evaluations is to use a hardened, isolated environment with no internet access by default. It allows a connection to the model API, but says API keys should remain outside the environment. That is a useful default distinction: the harness may need to communicate with the model, but the code or tools being evaluated do not automatically need general internet access.
“Sandboxed” alone does not settle the question. The relevant issue is what the sandbox can reach, how those permissions are enforced, and whether each run is isolated from other runs and the surrounding control system. Network policy is part of the evaluation design, not a setting to assume is safe because an agent has been instructed to behave.
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When does an evaluation need network access?
Provide connectivity when access to external information or services is part of the ability being measured. Match the permission to the scenario rather than choosing unrestricted internet access for convenience.
- No external network (N2): Suitable for tasks that can be completed from local files, preloaded data, or offline tools, including offline coding, file manipulation, reasoning, and containment tests.
- Allowlisted domains (N1): Suitable when the scenario requires particular APIs, package mirrors, documentation sites, or a controlled web corpus.
- Full internet (N0): Consider only when unrestricted browsing is itself under evaluation. This calls for strong isolation, disposable credentials, monitoring, and an explicit incident plan.
The AISI network categories make an important point: environments that are too restrictive can reduce realism and comparability. If a task requires an agent to retrieve information from a specified service, a disconnected setup measures a different task. Conversely, full internet access is not necessary just because the agent is capable of browsing.
How do you choose the right level of access?
Write down the scenario’s required data flows before a run. Then grant the narrowest access that makes the scenario solvable, and check that the environment measures the behavior you actually care about.
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| Decision factor | Question to answer | Why it matters |
|---|---|---|
| Task fidelity | Which real services, information, or data must the agent reach? | A task that depends on live information cannot be faithfully tested if that information is unavailable. |
| Containment | What prevents effects on the host, neighboring runs, or external systems? | Network permissions determine which destinations are reachable, while isolation limits what a run can affect. |
| Egress granularity | Are allowed destinations, methods, ports, and paths specified and enforced? | A broad permission can expose more access than the task requires. |
| Credential exposure | Are secrets kept out of the sandbox and provided through a trusted broker? | Credentials inside model-directed code can be exposed or misused. |
| Reproducibility | Can the image, policy, fixtures, and network responses be recorded or pinned? | Changing external systems can make otherwise similar runs behave differently. |
| Observability | Are commands, network requests, policy decisions, and violations logged? | Records help explain what the agent did and whether it stayed within scope. |
| Operations | What latency, cost, and maintenance do isolation and proxies add? | Connectivity controls have operational trade-offs that should be considered alongside fidelity and containment. |
Use this assessment to distinguish a task that needs one named API from one that genuinely tests open-ended browsing. If only a few hosts are necessary, an allowlist preserves more control than broad access while retaining the relevant task capability.
How can you safely let an evaluation call APIs?
Use policy-enforced egress and credential brokering, not prompt instructions alone. OpenAI’s sandbox guidance recommends isolated compute, outbound allowlists, and credentials brokered through a proxy or trusted server; Docker documents proxy-enforced outbound TCP rules and UDP disabled by default.
- Specify the task’s dependencies. List the tools, hosts, APIs, and data flows the scenario needs before launching the model. Treat any additional destination as out of scope until it is justified.
- Create an isolated environment for the run. Start with no external network and give each run or evaluation problem its own filesystem, network, and process space. NVIDIA describes per-problem isolation across those resources.
- Allow only the required egress. Add the necessary destinations and enforce the policy through a proxy, firewall, or runtime rule. Do not rely on the agent’s instructions to constrain its own network behavior.
- Keep secrets in the trusted side of the system. Store application and third-party credentials in the harness or a secrets manager. Use scoped, short-lived credentials and broker requests rather than baking keys into images or logs.
- Keep control functions outside model-directed code. Separate the harness control plane from sandbox execution so approvals, audit logs, recovery, and billing are not managed by code the agent can direct.
- Check the setup and observe the run. Validate the image and network policy, probe for escapes, and verify the configuration before each batch. Monitor actions and network activity; stop runs that violate scope.
- Record what makes the run reproducible. Save the image, policy, dependency, and fixture versions, along with network-response versions or records, so connectivity does not turn a benchmark result into an opaque snapshot of changing services.
These controls address different problems. An allowlist restricts destinations; a broker keeps secrets away from the agent’s environment; per-run isolation limits cross-run effects; and logging helps reveal what occurred. None should be treated as a substitute for the others.
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Does blocking the network make an evaluation unrealistic?
It can, if the benchmark is meant to measure a capability that depends on network access. A browsing agent evaluated with no access to the web is not being tested under the conditions its task requires. But giving every benchmark unrestricted internet access can introduce irrelevant permissions and external variability.
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Keep the result’s meaning tied to its conditions. A score from an offline run and a score from a live-web run describe different evaluation setups. Recording the network policy and relevant responses helps readers and operators understand what an agent could actually access.
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How can you tell whether the sandbox contains the agent?
Do not infer containment solely from the sandbox label or from a successful ordinary task. Check the actual configuration and probe for escape behavior before trusting it with evaluation runs.
AISI’s SandboxEscapeBench describes a sandbox-within-a-sandbox approach for testing breakout capability. In practice, the evaluation operator should validate the image and enforced policy, run escape probes, and verify the setup before each batch. Monitor the run continuously and stop it if it violates its scope.
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What should you record when network access is enabled?
Connectivity can make runs depend on changing services, so record the conditions that shaped the result. At minimum, preserve the image, policy, dependency, and fixture versions, plus versions or records of network responses. Also retain logs of commands, network requests, policy decisions, and violations.
These records help distinguish an agent change from an environment change. If two runs use different dependencies, fixtures, or external responses, a score difference may not reflect a change in agent behavior alone. Pinning what can be pinned and recording what cannot makes comparisons more interpretable.
Frequently Asked Questions
Does the model API connection count as internet access for an eval?
It is a network connection, but it need not give the evaluated code general internet access. Anthropic’s guidance distinguishes the model API connection from internet access inside the evaluation environment and says API keys should remain outside that environment.
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Can I use a package registry in an offline agent benchmark?
If the task needs packages that are not already available locally, a disconnected run may fail for setup reasons rather than because of the behavior being measured. Decide whether package retrieval is part of the scenario; if it is not, preloading dependencies or allowing only the required mirror keeps the setup aligned with that choice.
Why can two runs of the same network-enabled eval differ?
A live API, website, or other external service can return different information over time, and dependency or fixture changes can also alter the run. Recording versions and network responses helps identify those environmental differences when comparing results.
Should evaluation logs include the API credentials used by the agent?
No. The credential-handling pattern described for these evaluations keeps secrets in the harness or a secrets manager and brokers requests, rather than baking keys into images or logs. Logs should make requests and policy decisions observable without exposing the secret itself.
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