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Evaluate an AI coding-agent sandbox by testing the boundary around the agent—not by relying on a prompt that tells it to behave. Start with your threat model, then verify host isolation, workspace and network rules, credential handling, approval controls, auditability, recovery, and independent evidence. Filesystem and network controls need to work together: a sandbox that protects the host but lets an agent freely send repository contents elsewhere does not contain the main risks.
The available designs make different trade-offs. OpenAI describes a provider-neutral sandbox architecture; Docker Sandboxes uses lightweight microVMs; Claude Code combines filesystem and network controls; and Codex uses sandboxing alongside approvals and managed configuration. Those descriptions are useful starting points, not substitutes for testing the exact configuration your team will deploy.
What should an AI agent sandbox protect?
A sandbox is an execution boundary for the agent’s commands and code. It is not simply a system prompt, a restricted tool list, or a promise that the model will avoid risky actions. The key question is what a compromised or manipulated agent can reach if it follows hostile instructions in a repository, dependency, build script, or other input.
Write down what the agent may read, change, execute, and contact. Consider both intentional misuse and prompt injection: an agent might encounter instructions in a checked-out project that try to make it expose secrets, install malware, push unauthorized changes, or contact an attacker-controlled service.
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Build a threat model before comparing products
Include at least these scenarios in the threat model:
- Repository prompt injection: instructions embedded in issues, source files, documentation, or test data induce the agent to take an unsafe action.
- Malicious code execution: dependency-install scripts, Git hooks, build steps, or tests run code the team did not intend to trust.
- Secret exposure: the agent or a process it launches reads credentials from environment variables, files, mounts, or local services.
- Exfiltration: the agent sends source code, credentials, or other data out over the network.
- Unauthorized repository changes: the agent pushes, opens, or merges changes without the intended review.
- Cross-boundary access: a process inside the sandbox reaches the host, another agent’s workspace, or a host service.
- Sandbox compromise: the agent obtains full privileges inside its environment. Ask what remains protected in that case.
Set the impact you are trying to contain for each scenario. For example, a team may accept an agent writing to a disposable clone but prohibit access to production credentials, host files, and unrestricted outbound connections. That distinction turns “secure enough” into testable requirements.
How do you evaluate the isolation boundary?
For untrusted code, look for a boundary that protects the host even if the agent’s process is compromised. A separate kernel or virtual machine is a stronger design signal than relying on a container alone, but the product’s actual configuration and escape protections still need verification.
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Docker says each sandbox runs in a lightweight microVM with its own Linux kernel and identifies the microVM as the primary trust boundary. OpenAI describes restricted containers inside lightweight VMs for containment. These are documented architectural approaches; they do not establish that every configuration, workload, or escape path has been independently tested.
Questions to put to the vendor
- Does the agent run in a separate VM or kernel, or only in a container sharing the host kernel?
- What host resources are reachable from inside the boundary, including mounted sockets, local services, and device interfaces?
- What is the expected protection if an attacker gets full privileges inside the sandbox?
- How are the sandbox image and runtime updated, and how are security fixes delivered?
- Can you provide an architecture diagram and relevant escape-test results?
Containers may be useful where execution density or compatibility matters. Evaluate them against the impact of a compromised container: what prevents access to the host, and what evidence supports that claim? Do not treat “runs in a container” as a complete answer to host isolation.
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How should filesystem access and workspace mode work?
Workspace semantics determine whether the agent edits the team’s working tree directly, works on a separate copy, or has no shared host workspace. This choice affects both the damage an agent can do and how easily a developer can inspect or discard its changes.
Docker documents three distinct modes: direct mode shares the host workspace read-write; clone mode gives the agent a private in-VM clone; and mountless mode shares no host workspace. OpenAI describes manifests, mounts, snapshots, and resumable state as explicit parts of its sandbox design. Ask what persists, what is mounted, and what cleanup means rather than assuming “sandboxed” implies disposable.
| Workspace pattern | What it means | Questions and trade-offs |
|---|---|---|
| Direct host workspace | The agent reads and writes the host working tree directly. Docker documents this behavior for direct mode. | Can developers inspect changes live? Can the agent alter files or hooks before review? What happens to uncommitted work if a run fails? |
| Private clone | The agent works in a private in-VM clone. Docker documents this for clone mode. | How are changes returned for review? Is the original tree untouched until a developer applies or merges changes? |
| Mountless | No host workspace is shared with the sandbox. Docker documents this for mountless mode. | How does source code enter and results leave? What information is deliberately unavailable to the agent? |
| Manifest-defined mounts and state | OpenAI documents manifests, mounts, snapshots, and resumable state as design choices. | Which paths are mounted, with what permissions, and what survives a restart? The architecture description does not state one universal default. |
For evaluation, include Git hooks, build files, generated files, and untracked work in the test cases. Check whether the agent can alter files that affect a later developer command, whether the change is visible before execution, and whether a failed or interrupted run leaves a recoverable state. If you want to limit accidental damage, prefer a review boundary that keeps the team’s original working tree separate until changes are inspected.
How do you control network access and credentials?
Test network policy and filesystem policy together. Anthropic says both filesystem and network isolation are needed to reduce prompt-injection damage and exfiltration risk: preventing an agent from reading a secret is different from preventing it from sending data it can already access.
Require a clear account of outbound traffic, DNS behavior, proxy behavior, and access to host services. Docker documents policy-controlled outbound TCP and host-side credential injection. These are useful capabilities to assess, but your team still needs to verify the allowlist and confirm that secrets do not cross into the sandbox in the first place.
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Network and credential checks
- Default policy: Is outbound access denied unless explicitly allowed, or does the agent start with broad network access?
- Allowlist scope: Can policy constrain destinations and ports? How are DNS lookups handled, including lookups for destinations not on the allowlist?
- Host-service access: Can sandbox processes reach services bound to the host or other internal endpoints?
- Credential path: Are secrets mounted as files, exposed as environment variables, or supplied through a proxy outside the sandbox?
- Revocation: Can a credential or proxy authorization be revoked promptly when a run ends or an incident occurs?
- Testing: Can you verify that a deliberately attempted connection or credential read is blocked and recorded?
Docker describes injecting credentials through a host-side proxy so credential values do not enter the VM. Ask which credentials this covers, what the proxy authorizes, and what telemetry it records; a proxy does not automatically make every credential safe. When a tool or dependency genuinely needs network access, allow only the destinations required for that workflow and confirm that the restriction cannot be bypassed from inside the environment.
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Approvals and sandboxing solve different problems and should be evaluated together. Sandboxing limits what an action can reach; approval policy determines which boundary-crossing actions need a person to review them. OpenAI’s guidance describes approvals for actions outside the sandbox, while Anthropic’s account of Claude Code emphasizes pairing filesystem and network boundaries with reduced permission prompts.
Map each consequential action to its policy: reading sensitive paths, making outbound connections, modifying shared files, pushing changes, or accessing credentials. Determine whether approval is one-time or applies for the session, what exactly the user sees before approving, and whether a later command can exceed the approved scope.
Anthropic reported in 2025 that sandboxing reduced permission prompts by 84% in its internal usage. That is a vendor-reported result for its own usage, not a general benchmark or a promise that another team will see the same reduction. Treat fewer prompts as an operational outcome to measure alongside containment, not as proof of security.
What should you compare across sandbox tools?
Use a common scorecard so that product labels do not obscure meaningful differences. Record the evidence for each answer, including whether it is documented, demonstrated in your own environment, or still unknown.
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| Evaluation area | Questions to ask | Evidence to request or collect |
|---|---|---|
| Host isolation | Is there a separate kernel or VM? What remains protected if the container or sandbox is compromised? | Architecture diagram, sandbox-escape tests, host-access tests. |
| Filesystem | Is the workspace directly mounted, cloned, mountless, or ephemeral? Can hooks or build files affect later host actions? | Mount and permission configuration, workspace cleanup behavior, before-and-after file inspection. |
| Network | Is egress denied by default? Can destinations and ports be allowlisted? Are DNS and host-service routes controlled? | Policy configuration, attempted-exfiltration tests, network audit records. |
| Credentials | Are secrets mounted, injected into the environment, or mediated by a proxy? Can access be scoped and revoked? | Credential-flow diagram, leak tests, revocation demonstration. |
| Workspace state | What persists between runs? Are snapshots and resumable sessions available? How is cleanup verified? | Persistence settings, snapshot and restore test, cleanup procedure. |
| Approvals | Which actions pause for review? Can approval policy be scoped to a session? | Policy configuration and tests showing which commands prompt or proceed. |
| Observability | Are command, file, network, and agent events logged? Can logs be exported, and who controls retention? | Sample audit trail, export and retention settings, incident-response workflow. |
| Operations | Is execution local, hosted, or hybrid? How does it fit scaling, support, and existing developer workflows? | Deployment architecture, operational requirements, support terms. Costs and scaling limits are not stated in the cited product descriptions. |
| Independent evidence | Has the system been assessed outside the vendor? Are results reproducible and relevant to your configuration? | Third-party assessments, test scope and date, disclosure and incident history. |
Use “not stated” rather than interpreting an undocumented feature as present. Also distinguish product-level documentation from evidence about the precise configuration, version, or deployment you are considering.
How do the documented approaches differ?
The approaches below illustrate different evaluation starting points. They are not a security ranking: the available descriptions do not provide a like-for-like independent test across products.
| Approach | Documented design points | What a team should verify |
|---|---|---|
| OpenAI Agents SDK sandboxes | Provider-neutral architecture with Unix-local, Docker, or hosted providers; manifests define workspace contracts, and sessions can persist or resume. | Provider-specific isolation, actual mount permissions, persistence and cleanup behavior, network controls, and approval policy. |
| Docker Sandboxes | Local agent environments based on lightweight microVM isolation, policy-controlled outbound TCP, credential proxying, and an in-sandbox Docker Engine. Workspace options include direct, clone, and mountless modes. | Effective egress rules, proxy scope and credential flow, chosen workspace mode, and the impact of full privileges inside the microVM. |
| Claude Code sandboxing | Filesystem and network boundaries designed to reduce approval prompts while constraining prompt-injected commands. Claude Code on the web uses a scoped Git proxy. | Filesystem and network settings in your deployment, the scope of the web Git proxy, and what actions still require review. |
| Codex containment controls | Sandboxing, approvals, managed configuration, network policies, and agent-native logs; higher-risk actions crossing boundaries can be sent for review. | Which actions cross a boundary in your policy, how managed settings apply, what logs capture, and how policy behaves during a session. |
These descriptions establish that the tools expose different architectural controls; they do not establish equivalent defaults, identical deployment models, or comparative escape resistance. Compare the controls you can configure and test in your intended setup rather than treating a feature name as proof of a particular protection level.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What evidence should you demand before deployment?
Ask vendors for concrete evidence and run your own tests on a disposable environment. Documentation tells you what a product is intended to do; a controlled test shows whether the policy and deployment you plan to use behave as expected.
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Evidence to request
- Architecture diagrams that show host, VM or container, network, proxy, and credential boundaries.
- Escape-test and network-exfiltration results, with scope, date, and the tested configuration.
- Credential-leak test results and an explanation of which credentials are protected by which mechanism.
- Patch and incident history, including how security issues are disclosed and fixed.
- Independent assessments, with the assessor, scope, and limitations clearly stated.
- Reproducible test instructions or benchmarks that your security team can validate.
The 2025 MIT AI Agent Index reported sandboxing or VM isolation documented for 9 of 30 agents, and no third-party testing information for 23 of 30. These are findings about the agents covered by that index, not a pass/fail judgment on any one product. Missing public evidence is procurement risk and a reason to ask more questions; it is not, on its own, proof that a system is unsafe.
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What tests should you run before allowing unsupervised work?
Test the exact image, policy, workspace mode, credentials, and approval settings the team will use. Begin with a non-sensitive repository and synthetic secrets, and make the expected blocked, allowed, and logged outcomes explicit before each test.
- Check workspace boundaries. Put a harmless marker outside the permitted workspace and confirm the agent cannot read or modify it. In a clone or mountless setup, confirm that changes do not silently alter the original host tree.
- Exercise repository-driven instructions. Add a test file or build instruction that requests access beyond the agent’s intended scope. Confirm that the sandbox blocks the action even if the agent attempts it.
- Test network denial and allowlists. Attempt a connection to a destination that should be denied, then test an explicitly permitted destination. Verify the result in both the sandbox and its audit records.
- Test secret handling. Use synthetic credentials and check whether processes inside the sandbox can read them directly or transmit them. Confirm that revocation takes effect as expected.
- Check approval boundaries. Exercise actions that should proceed automatically and actions that should pause. Confirm the approval applies only to the scope the reviewer intended.
- Interrupt and recover. Stop a run, resume it if supported, and inspect the resulting workspace and logs. Confirm cleanup or snapshot behavior matches policy.
- Review the record. Verify that command, file, network, and approval events needed for incident response can be found and exported.
Do not make the first unsupervised run the test. Expand access only after the team can demonstrate that prohibited actions are blocked, permitted actions are bounded, and events are visible to the people responsible for response.
How should a development team make the final decision?
Choose the configuration that meets the threat model with the least unnecessary access, and document any accepted gaps. A short decision record should identify the workspace mode, isolation boundary, network policy, credential path, approval scope, logging, recovery behavior, and evidence still outstanding.
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Frequently Asked Questions
Can a prompt alone keep a coding agent from doing something dangerous?
No. A prompt can express policy, but it does not enforce operating-system, filesystem, or network boundaries. Treat natural-language instructions as one layer of guidance, not as a substitute for containment.
Does a sandbox make every agent run safe to leave unattended?
No sandbox description establishes that all actions are safe or that every failure mode is contained. Unsupervised use should depend on the scope of access, tested controls, required approvals, and whether the team can detect and respond to failures.
What is a good first repository for a sandbox pilot?
Use a non-sensitive project with synthetic credentials and no production access. A pilot should exercise representative build and test workflows while keeping a clear boundary between agent changes and developer-owned work.
What should we do if a vendor will not share independent test results?
Ask for the test scope, architecture details, and reproducible demonstrations your own team can run. If meaningful evidence remains unavailable, record that as a procurement and risk decision rather than assuming the control works.
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