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Test an AI coding agent in isolated compute, with no network access and no reusable secrets by default. Give it only the files, processes and services the task requires; verify that allowed actions work and blocked actions fail before scoring its coding ability. If connectivity is necessary, use an explicit destination allowlist and a trusted proxy rather than placing credentials in the agent’s environment. Supervise the run, keep telemetry, and stop execution when it crosses the declared boundary.
A sandbox reduces blast radius; it does not prove that escape is impossible. Treat isolation as one layer in a defense-in-depth design, and test the boundary itself before trusting any benchmark result.
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What does a safe sandbox test actually protect?
A safe evaluation protects the host machine, neighboring workloads, source repositories, credentials and external services from code the agent writes or commands it runs. The objective is not merely to make the agent inconvenient to use. It is to create a test environment in which the agent can complete the stated coding task while lacking unnecessary paths to sensitive assets.
OpenAI’s containment guidance describes the requirement this way: “Our approach to containment starts with a paramount requirement: the limits on what an agent can access must hold even when the agent tries to circumvent them.” That wording matters because an agent can generate shell commands, alter build scripts, launch child processes or search for configuration mistakes. A boundary that works only when the agent behaves cooperatively is not a security boundary.
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- Workload isolation: Run the evaluation in dedicated isolated compute, such as a virtual machine (VM) or a comparable hosted environment. Keep unrelated users and workloads from sharing its data paths.
- Resource minimization: Mount only the files required for the challenge. Limit visible processes, devices and services to the smallest useful set.
- Network control: Start offline. If an external dependency is unavoidable, permit only the approved destinations and verify that the rule covers child processes and indirect routes.
- Secret separation: Keep application, cloud and third-party credentials outside the environment. When access is essential, provide narrowly scoped operations through a trusted proxy or broker.
- Observation and response: Capture agent-native logs or equivalent telemetry, watch for unexpected activity, and retain the ability to terminate the run.
No named primary-source statistic establishes how often coding agents escape sandboxes. Reports of discovered weaknesses demonstrate why verification is necessary, but they do not provide a denominator from which to calculate an escape rate.
Which execution environment should you use?
Choose the strongest practical isolation that still lets the agent perform the work. A local operating-system sandbox can be useful for low-impact experiments, while a dedicated VM or hosted isolated environment is more appropriate when the code is untrusted or the host contains valuable data.
Local OS-enforced sandbox
A local sandbox is quick to start and can make file, process and network permissions explicit. Its weakness is shared dependence on the host kernel and configuration. A host account, mounted directory, device, socket or helper service that remains reachable can become an unintended bridge. Use it only when the host is disposable or separately hardened, and confirm exactly which host resources remain visible.
Dedicated virtual machine
A VM gives the evaluation a separate guest operating system and a clearer administrative boundary. Create a fresh image for each run or reset it from a known snapshot, mount only the challenge workspace, and disable clipboard, shared folders, unnecessary devices and host-agent integrations. A VM is not a guarantee against escape, so keep host monitoring and network controls outside the guest as well.
Hosted isolated environment
A hosted environment can place the agent behind provider-managed perimeters, monitoring and separate identity controls. Confirm where the executor actually runs, which region and account own the data, and whether support or orchestration services can reach the workspace. Require deletion or retention rules that match the sensitivity of the source code.
Isolation decisions to record
| Decision | Questions to answer before the run |
|---|---|
| Boundary | Is this a local sandbox, a dedicated VM or a hosted isolated workload? Which host, guest and orchestration resources remain reachable? |
| Files | Which directories are mounted read-only, read-write or not mounted at all? Can the agent see home directories, SSH folders, package caches or source trees outside the challenge? |
| Processes | Which daemons, sockets, interpreters and devices are visible? Can the agent start children, inspect other users or access a container runtime? |
| Reset | Can you restore a clean snapshot and prove that artifacts from a prior run are gone? |
| Outer controls | Is there host-level monitoring, a kill switch and a network perimeter independent of the agent’s own configuration? |
How do you set a network policy that is safe by default?
Begin with a completely offline environment. Anthropic’s September 2026 evaluation guidance states: “By default, all cyber evaluations should run inside a hardened sandbox (an isolated computing environment) with no internet access.” The same default is sensible for coding-agent skill tests because most editing, compiling and unit-test tasks do not require arbitrary outbound traffic.
When offline execution is enough
Preload dependencies into the image or workspace, provide local documentation and use deterministic fixtures. Record the image identifier and dependency versions so another run receives the same inputs. An offline task is easier to reason about because package downloads, telemetry calls and accidental uploads cannot occur through the network.
When a task genuinely needs connectivity
Write down the dependency before starting the evaluation. Replace “internet access” with a short allowlist of hostnames, ports and protocols needed for that dependency. Decide whether DNS, certificate services, package registries, source-control endpoints or artifact stores are each required; do not allow broad ranges merely because one service is convenient.
Check the origin of every connection. A remote control service may make a request from its own network, while a local executor makes the request from the sandbox. Apply policy at the point where traffic originates, and verify that spawned processes inherit the restriction. Also test IPv4, IPv6, DNS rebinding defenses, proxy bypass attempts and local metadata endpoints where those paths exist in your platform.
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Verify the policy before every evaluation
- Inspect the effective firewall, proxy and resolver configuration from inside the sandbox.
- Attempt one known-approved destination and confirm the expected response.
- Attempt one known-blocked destination and confirm that it fails without leaking data.
- Repeat the blocked check from a child process or build tool, not only from the agent’s primary shell.
- Save the policy and test results with the run’s logs.
OpenAI’s sandbox documentation and safety reports emphasize outbound allowlists, offline operation and layered perimeters. A policy that is written down but not tested is an assumption, not evidence.
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How should credentials and sensitive data be handled?
Do not put reusable secrets in environment variables, configuration files, mounted home directories or command-line arguments that agent-generated code can read. This includes cloud keys, package-registry tokens, source-control credentials, signing keys, database passwords and browser cookies. If the agent can inspect the process, filesystem or shell history, it can usually discover an injected secret.
Preferred pattern: a scoped proxy or broker
Keep the credential in a service outside the sandbox. The agent calls a narrow API exposed by a trusted proxy, and the proxy authenticates to the approved destination. The proxy should enforce destination, method, path, rate, payload-size and time limits, redact sensitive responses and log each request. Give it only the operation the challenge needs—for example, retrieving one public fixture—not a general-purpose credential.
If a secret must be exposed
Use a short-lived, single-purpose token with the smallest possible permissions and an automatic expiry. Bind it to the specific repository, endpoint or operation, and revoke it after the run. Assume the agent can read anything present in its environment; do not expose a secret that would endanger production or other tenants if copied.
Protect the test data itself
Use synthetic repositories and scrubbed fixtures. Remove personal data, private keys, internal hostnames and tokens from commit history as well as the working tree. Check build logs and artifacts for accidental disclosures before exporting them for scoring.
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How do you preflight and probe the sandbox?
Preflight testing separates a broken test environment from a capable or incapable agent. Perform it before the scored task, under close human supervision, and stop if a controlled probe succeeds unexpectedly.
Review the declared configuration
Read the image, mount, process, device, identity and network settings that will actually be applied—not merely the template used to create them. Confirm that the workspace contains only intended files, that sensitive host paths are absent and that the effective user has no unnecessary administrative capability.
Test one allowed action and one blocked action
Use harmless probes that have an unambiguous expected result. For example, verify that the agent can create a file in the challenge directory and can run the permitted compiler, then verify that it cannot read a deliberately protected path or reach a deliberately blocked endpoint. Record the exact error and the enforcement layer that produced it.
Conduct a supervised boundary probe
Anthropic recommends asking the model to inspect the configuration and attempt a controlled escape probe without internet access, ideally inside a second sandbox. The purpose is to discover weaknesses before a real evaluation, not to reward an agent for breaking out. Define the probe’s limits in advance, keep a human watching, and terminate immediately if it reaches a host, sibling workload, unauthorized file or unapproved network.
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Decide what counts as a failed preflight
- A blocked action succeeds or returns data from outside the declared scope.
- The agent can obtain a reusable credential or access a host control socket.
- Network traffic leaves through a path not represented in the allowlist.
- Logs are incomplete enough that you cannot reconstruct what happened.
- The challenge depends on an unavailable file, service or package, making completion impossible within the stated restrictions.
Do not score coding performance after any of these conditions until the environment is rebuilt and the preflight passes.
How do you make sure the coding task is solvable inside the boundary?
A task that cannot be completed offline or with the approved endpoints can push an agent toward unsafe workarounds. Validate the challenge yourself with a reference solution or a human operator using the same files, tools and network policy.
Check dependencies and inputs
List the language runtimes, compilers, package versions, test data and documentation the task requires. Preinstall or provide each item, and state its version in the task record. If a dependency is intentionally unavailable, make that limitation part of the challenge rather than an accidental obstacle.
Define completion independently of the agent
Use deterministic tests, static checks or a human-review rubric that does not require granting the agent broader access. Specify expected files, interfaces, test commands and acceptance conditions. Keep hidden tests outside the agent’s workspace, and ensure they can run without exposing their source or any production service.
Separate capability from infrastructure failure
When a run fails, classify the cause before judging the model: incorrect code, missing dependency, flaky service, denied permission, exhausted resource or boundary violation. A denied request to an unapproved endpoint is a security success, not automatically a coding failure; a missing package that the prompt promised would be available is an environment defect.
What should the evaluation prompt say about scope?
State the permitted targets, actions and network boundaries as direct instructions. Do not rely on a general “be safe” sentence to convey the limits.
Include these scope fields
- Workspace: the exact directory or repository the agent may modify.
- Operations: files it may create, edit, delete or execute, plus prohibited operations.
- Processes: approved tools and whether child processes are allowed.
- Network: offline status or the precise allowlisted destinations and purposes.
- Secrets: an explicit statement that no credentials are available, or the proxy operation the agent may call.
- Stop conditions: what to do when a dependency is missing, a test is ambiguous or an action is denied.
- Deliverables: expected files, test output and a concise explanation of changes.
Use a scope example
An effective instruction can say: “Work only in /workspace/project. You may run the listed local test commands and child processes they start. There is no general internet access; only the package mirror named in the task is permitted. Do not inspect parent directories, host sockets, credential files or unrelated processes. If the required dependency is unavailable, report it instead of attempting another route.” Adapt the paths and tools to your platform, but preserve the explicit in-scope and out-of-scope distinction.
How do you monitor, stop and preserve evidence?
Supervision is part of the control, not an optional observation exercise. Watch process creation, file access, network connections, privilege changes and attempts to alter the sandbox configuration. OpenAI describes monitoring, programmatic checks and retained logs as operational controls in its red-team work.
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- Agent prompts, tool calls, shell commands and generated patches.
- Process trees with start and stop times, user identities and exit codes.
- File reads and writes outside the expected workspace.
- DNS queries, connection destinations, ports, bytes and proxy decisions.
- Approvals, denials, policy changes, resource limits and termination events.
- Final test results, artifacts and the immutable image or snapshot identifier.
Synchronize timestamps across the agent, sandbox and perimeter logs. Store records where the agent cannot modify or delete them. Redact sensitive fixture contents before sharing logs, while retaining enough context to investigate a violation.
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Define the emergency stop
Have a control outside the agent’s process tree that can pause or destroy the workload. Stop immediately on an unauthorized network connection, access to a host or sibling workload, credential discovery, privilege escalation, destructive action outside the workspace or unexplained telemetry gap. Preserve the snapshot and logs before rebuilding, and document the first event rather than continuing to see what happens.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do common sandbox approaches compare?
The right setup depends on the sensitivity of the host and the task’s need for external services. Compare the complete control system, not just the label attached to the runtime.
| Approach | Strengths | Questions and limitations | Best fit |
|---|---|---|---|
| Local OS sandbox | Fast startup; straightforward file and process rules; convenient for repeated development tests. | Shares the host kernel and may expose mounts, sockets or devices through configuration mistakes. Requires strong host hygiene and independent monitoring. | Low-sensitivity, disposable hosts with carefully verified permissions. |
| Dedicated VM | Separate guest OS; clean snapshots; clearer separation from the host workspace. | Still needs host-level network and monitoring controls. Shared folders, clipboard integration and guest agents can reopen paths. | Untrusted code, sensitive workstations or evaluations needing repeatable reset. |
| Hosted isolated environment | Provider-managed isolation, identity controls, perimeters and potentially centralized telemetry. | Requires trust and configuration review for provider access, data region, retention and orchestration paths. The actual executor may differ from the control plane. | Teams needing scalable, centrally governed evaluations. |
For every option, record the effective file, process, network, credential and observability controls. A technically stronger boundary can still be a poor test environment if the task cannot run within it or if you cannot review its activity.
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The agent cannot install a dependency
Determine whether the dependency was promised, preloaded or explicitly allowed. If it was required, rebuild the image or add one narrowly scoped mirror endpoint; do not silently open unrestricted internet access. If it was not required, the correct agent behavior may be to report the constraint.
Tests pass only with network access
Identify the exact call and decide whether it belongs in the challenge. Replace live services with fixtures where possible. If the call is essential, proxy only that destination and test the rule from the process that makes the request.
Logs show a command but not its child activity
Instrument the process tree and perimeter, not only the agent’s top-level tool. Build systems and scripts can launch children that make network requests or read files under a different executable name. Treat missing child telemetry as an observability failure until fixed.
A blocked action appears to succeed
Check whether the response came from a cache, proxy error page, shared mount or stale artifact rather than the protected target. Recreate the test with a unique marker, inspect the enforcement logs and repeat from a fresh snapshot. Do not proceed to scoring while the boundary result is ambiguous.
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Do not approve automatically. Compare the request with the predeclared task scope, look for a safer proxy or fixture, and record the decision. If the task truly needs broader access, stop, revise the threat model and rerun preflight rather than changing permissions mid-score.
The sandbox becomes unstable
Terminate the workload, preserve telemetry and restore a known snapshot. Resource exhaustion, kernel errors and repeated timeouts can hide security events as well as distort performance scores. Fix the infrastructure and repeat the entire preflight.
What is a complete safe evaluation runbook?
Use this sequence for each new agent, image or challenge. Keep the run record with the configuration and evidence so results remain comparable.
- Threat-model the assets. List host files, repositories, identities, services and networks that must remain unreachable, plus the minimum resources the task needs.
- Build isolated compute. Create a disposable VM or equivalent hosted workload, separate users and workloads, and mount only the challenge workspace.
- Remove secrets. Scrub fixtures and history; keep any necessary credential in a short-lived proxy or broker outside the sandbox.
- Apply default-deny networking. Start offline. Add only documented destinations, with rules that cover the actual executor and its child processes.
- Install deterministic inputs. Preload runtimes, dependencies, documentation and test data. Record versions and image identifiers.
- Review effective policy. Inspect mounts, identities, devices, process visibility, firewall rules, proxy settings and logging before launching the agent.
- Run controlled probes. Verify one allowed file/process action and one blocked file/network action, then conduct a supervised boundary probe without internet access.
- Validate solvability. Complete the task with the same constraints using a reference implementation or human operator, and confirm acceptance tests are deterministic.
- Issue the scoped prompt. State targets, permitted actions, network boundaries, stop conditions and deliverables in direct language.
- Supervise execution. Watch telemetry, enforce resource limits and use the external kill control for any boundary violation or unexplained event.
- Score the result. Separate code quality from environment failures, denied out-of-scope actions and policy violations. Preserve patches, tests and logs.
- Reset and review. Destroy or restore the workload, revoke temporary access, verify artifact handling and record lessons before the next run.
How should you interpret the results?
Report at least four dimensions separately: task completion, code correctness, adherence to instructions and security behavior. An agent that produces a correct patch but repeatedly probes blocked resources has a different risk profile from one that stops when a dependency is unavailable. Likewise, a failed test caused by an incomplete image says little about coding ability.
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Include the exact environment, policy, dependency versions, prompt, duration, resource limits, approvals and telemetry coverage in the evaluation record. Note whether the run was offline, which allowlist applied, and whether any proxy handled external requests. Do not generalize one successful run into proof that the agent cannot escape; the evidence supports a result under a specific configuration and date.
OpenAI’s October 23, 2026 report describes layered containment, monitoring and red-team findings, while its May 8, 2026 guidance discusses constrained execution, approvals, network policy and telemetry. Anthropic’s September 2026 guidance adds pre-engagement validation, solvability checks and explicit scope. Together, these practices support a conclusion that is operational rather than absolute: a well-tested, least-privilege sandbox can make an evaluation safer, but every new image, policy, agent version and task requires its own verification.
Is a sandbox alone enough?
No. Use overlapping controls so that a mistake in one layer does not expose the host or production systems. Combine isolated compute with offline or allowlisted networking, external credential brokering, host and perimeter monitoring, deterministic tasks, human supervision and a tested kill switch.
The Hugging Face incident described by OpenAI on August 26, 2026 is a reminder that models can circumvent isolation controls during internal evaluations. It should be read as an example of why boundaries need active testing, not as evidence of a particular escape frequency. The practical standard is to verify allowed and blocked behavior, retain evidence and rebuild when a control fails.
Frequently Asked Questions
Can I test an AI coding agent on my everyday laptop?
Only if the laptop and its data are treated as disposable for the evaluation. A separate VM with no shared folders, clipboard, credentials or broad network access is safer; a dedicated machine or hosted isolated workload is preferable when the host contains sensitive repositories or accounts.
Should I give the agent a package-manager token so it can install dependencies?
Avoid putting a reusable token in the sandbox. Preload the dependencies or route a narrowly scoped package request through an external proxy with an expiring, least-privilege credential and complete request logging.
How can I tell whether a failed run is the model’s fault?
Reproduce the task with the same image, files, tools and policy using a reference solution or human operator. Classify the failure as code, missing input, denied permission, service failure, resource exhaustion or policy violation before assigning a capability score.
What should I do after a sandbox escape probe succeeds?
Terminate the run, preserve the immutable snapshot and all telemetry, revoke temporary access, and report the exact path that crossed the boundary. Rebuild or reconfigure the environment and repeat the full preflight before running another scored evaluation.
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