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To sandbox AI agents so failures cannot reach production systems, separate the place an agent executes code from the trusted service that controls its tools, credentials, approvals, and deployment decisions. Give the sandbox only task-specific files, no production write credentials, and narrowly allowed network access. Treat every file mount, environment variable, exposed port, and network route as a deliberate grant of authority—not as a convenience setting.
This design limits the damage if an agent misunderstands a task, follows malicious instructions embedded in data, or runs compromised code. It does not make the agent trustworthy; it ensures the environment cannot directly grant it more power than the task requires.
What a sandbox can—and cannot—protect
A sandbox is an isolated execution environment in which an agent can perform work such as reading and editing files, running shell commands, installing packages, and producing artifacts. Its security value comes from what the environment does not expose: production credentials, unrestricted network routes, sensitive host files, and direct authority to deploy or change live systems.
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OpenAI’s Sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” That is the core rule for designing containment. If a secret is present in an agent-readable environment variable, a file is mounted into its workspace, or a service is reachable over the network, code running there may be able to use it. A model instruction saying “do not access production” is not a security boundary.
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Sandboxing is one layer in a broader design. It can reduce the reach of a mistaken or manipulated agent, but it does not prove that the agent’s output is correct, that a dependency is safe, or that a reviewed artifact is suitable for production. Those decisions need independent policy checks and, for consequential actions, human or automated authorization outside the sandbox.
Separate the trusted harness from sandbox compute
Use two distinct planes. The harness is the trusted orchestration service: it makes model calls, routes tool requests, authenticates users and services, applies policy, records audit events, requests approvals, and controls recovery. The sandbox is disposable or deliberately managed compute where model-directed work happens: file edits, commands, tests, package installation, and artifact generation.
OpenAI’s “Sandbox Agents” guide describes this separation and a sandbox environment with a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled access to external systems. Those features make the environment useful, but each is also a potential path to data or authority. Enable only the capabilities a task needs.
| Responsibility | Keep in the trusted harness | Allow in sandbox compute |
|---|---|---|
| Identity and credentials | Authenticate users and services; retrieve or broker narrowly scoped credentials. | No production identity by default; only task-scoped access explicitly needed for execution. |
| Policy and approvals | Validate tool requests, enforce limits, and obtain approval for sensitive actions. | Execute permitted work; do not decide that a request is authorized. |
| Audit and recovery | Keep run state, audit records, approval decisions, and recovery controls outside the workspace. | Write task files and outputs within its assigned workspace. |
| Deployment | Use an independent deployment pipeline to verify and authorize releases. | Prepare a patch or build artifact; no direct production deployment authority. |
The exact service boundaries depend on your architecture, but the principle is stable: code controlled by the agent should not be able to rewrite the rules that govern its own access. The harness should decide which tools exist, which arguments are allowed, and what outputs can leave the sandbox.
Define the production boundary before building the sandbox
Start by identifying what would cause material harm if an agent could read it, alter it, or trigger an irreversible action. Include both data and capabilities, not just servers labeled “production.” A production boundary commonly includes databases, customer records, deployment pipelines, cloud control planes, signing keys, administrative APIs, and systems that can change permissions or billing.
For each asset, record whether a proposed agent workflow needs no access, read-only access, write access, or an irreversible action. A task that writes a patch usually needs a writable project workspace; it does not follow that it needs production database credentials or permission to merge and deploy. Keep the model’s task description separate from the infrastructure policy: user prompts cannot define or expand the boundary.
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- Identify the asset: name the system, data class, or capability and its owner.
- Classify the action: distinguish reading, changing, deleting, deploying, and granting access.
- Set an explicit route: specify whether access is prohibited, brokered, or granted through a scoped identity.
- Choose an independent control: enforce the decision in infrastructure, tool code, or the deployment pipeline—not only in an agent instruction.
Build a task-scoped workspace
Give each run a workspace contract: what files are available, what the agent may change, where outputs go, and how long the environment and its data persist. OpenAI’s sandbox guidance describes manifests and mounts as ways to define a workspace and artifacts as outputs another system can review.
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Prefer a narrowly selected project directory or input set over a broad home-directory or host filesystem mount. Mount input data read-only when the task does not require changing it. Put writable work in a separate directory, and keep generated artifacts distinct from trusted source and control files. A project checkout does not need access to a developer’s SSH directory, cloud configuration, browser profile, or unrelated repositories.
Keep outputs outside the trust boundary until reviewed
Do not treat files produced by the sandbox as trusted just because they are valid-looking code, test reports, or build artifacts. Have the harness collect the specific outputs the task expects, inspect or validate them, and pass them to a separate review or build process. Avoid copying an entire sandbox filesystem into a trusted system. If the sandbox needs persistence or snapshots, define which data is retained, who can restore it, and how that state is isolated between tasks.
Make lifecycle behavior explicit
For short tasks, an isolated fresh environment can reduce unintended carryover between runs. Stateful workspaces may be necessary for long-running tasks, but persistence changes the threat model: files, credentials, package caches, and prior task state may remain available. Decide when a workspace is deleted, hibernated, resumed, or reset, and ensure a new task does not silently inherit another task’s writable state or identity.
Restrict network access separately from filesystem access
A filesystem boundary does not stop an agent from sending data through a network route it can reach. Deny broad outbound access by default, then allow only the endpoints a specific task requires. OpenAI’s sandbox security guidance recommends outbound allowlisting and describes routing third-party access through a trusted proxy or server so credentials can be added outside the sandbox.
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Make the allowlist meaningful at the connection layer, not just in the agent prompt. Consider whether access is needed to a package registry, source-control service, model endpoint, or task-specific API. If a task needs a third-party service, the harness or proxy can authenticate the request using a scoped credential rather than placing a long-lived key in the sandbox.
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Review routes that are easy to overlook
When validating egress, do not check only public websites. Test whether the sandbox can reach internal service names, local services, cloud metadata endpoints, administrative APIs, or other networks available to its host or cluster. Review DNS behavior, loopback and localhost access, exposed ports, and any route that could bridge from the sandbox to a trusted service. These are practical policy checks: the relevant question is what the deployed network configuration actually permits.
Also decide how the proxy behaves. It should permit only approved destinations and operations, avoid becoming a general-purpose tunnel, and keep its secrets inaccessible to sandbox processes. If the sandbox can ask the proxy to make arbitrary requests with a powerful credential, the proxy has simply moved the risk rather than contained it.
Keep credentials out of agent-readable execution contexts
Give the harness and sandbox distinct identities. Do not pass production credentials into the sandbox simply because the agent needs to edit code, run tests, or make a request. OpenAI’s security guidance warns that generated code can read environment variables and recommends keeping application API keys outside the environment, using vault-backed secrets and a proxy for hosted sandbox requests, and keeping third-party credentials outside self-hosted environments.
When access is genuinely required, grant the minimum permission for the shortest practical period and restrict the destinations that can receive the credential. A read-only token for one repository is different from a general source-control token; a task-limited API credential is different from a cloud administrator identity. Prefer a brokered operation that performs a specific action over handing the agent a reusable secret.
- Do not inject secrets by default: environment variables, mounted secret files, and command arguments may be readable by generated code or appear in logs.
- Scope the identity: bind access to the required resource and operation, not a broad account.
- Control the destination: prevent a credential from being sent to an unapproved host or endpoint.
- Plan for exposure: if a secret may have entered an agent-readable context, revoke or rotate it and review the associated activity.
Credential separation and egress restrictions work together. A token kept outside the sandbox is not safely brokered if the sandbox can make arbitrary authenticated requests through the harness; an allowlisted network is not enough if a powerful secret is mounted locally.
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Treat a model-generated tool call as an untrusted request. The harness should validate the tool name, arguments, target resource, and requested action against policy before execution. A tool that accepts a repository name, URL, database identifier, or deployment target should not assume the model chose an allowed value. Reject out-of-policy requests rather than relying on the agent to reconsider them.
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OpenAI’s March 11, 2026 article “Designing AI agents to resist prompt injection” frames the risk as untrusted content influencing an agent that has a consequential capability. For example, external content may influence an agent that can transmit information, follow a link, or use a tool. This is why prompt injection should be treated as a path from input to action—not merely as suspicious wording to filter.
Gate high-impact actions outside the model
Require an independent approval for actions such as production changes, destructive operations, permission grants, or sensitive data transmission. The approval should be bound to a specific action and target, rather than interpreted as blanket permission for whatever the agent later requests. For lower-risk actions, deterministic policy can allow known-safe operations without an approval prompt.
Make deployment an independent decision
The sandbox may produce a patch or build artifact, but it should not hold deployment authority. Pass the artifact to a trusted pipeline that performs the organization’s required checks and obtains the necessary authorization. The pipeline should not trust a status message from the agent as proof that a check ran successfully; it should execute or verify the checks itself.
Keep the harness’s tool controls, approval state, audit records, and recovery mechanisms outside agent-writable files. If an agent can change the policy file, erase its audit record, or mark its own request as approved, the separation is cosmetic.
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For Kubernetes-based workflows, Kubernetes SIG Agent Sandbox documents declarative resources for stateful, singleton workloads and describes runtime-agnostic support, including gVisor and Kata Containers as stronger runtime choices. The project documentation also describes applicable Kubernetes controls such as RBAC, namespaces, network policies, quotas, persistent storage, scheduled deletion, and hibernation. It does not establish a universal runtime winner or provide a quantified comparative escape-resistance ranking.
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| Runtime boundary | What to evaluate | Decision question |
|---|---|---|
| Shared-kernel container | Host and cluster configuration, process privileges, mounts, and the consequences of a kernel-level compromise. | Is the isolation adequate for this workload and its permitted access, given the consequences of a boundary failure? |
| Userspace-kernel sandbox | Compatibility with required system calls, packages, filesystem behavior, and networking, alongside the boundary it provides. | Can the workload run correctly while the sandbox limits what a compromised process can reach? |
| VM-grade isolation | Operational ownership, startup and lifecycle needs, persistence, and compatibility with the workload. | Does the stronger separation justify its operational cost for the threat and workload? |
Do not select a runtime by product label alone. Compare the exact configuration on five axes:
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- Isolation boundary: determine what a compromised process can reach and what other workloads share the boundary.
- Workload compatibility: check required system calls, packages, filesystem behavior, networking, and persistence.
- Network and identity: confirm that egress and per-task identity can be constrained independently of compute isolation.
- Lifecycle: decide whether work is ephemeral or stateful and how cleanup, snapshots, and resumption behave.
- Operations and evidence: account for patching, monitoring, ownership, and adversarial tests of the exact deployed configuration.
Kubernetes controls remain relevant regardless of runtime. Use least-privilege RBAC, isolate workloads with appropriate namespaces and network policies, and set resource quotas to constrain consumption. Those controls complement the runtime boundary; none should be treated as a replacement for limiting credentials, mounts, or routes.
Test containment, not just whether the task succeeds
A successful coding task shows that the agent could do useful work; it does not show that the sandbox contained an attack. Build negative tests around the boundary you intend to enforce. Run them from inside the deployed sandbox, then verify outcomes through logs or controls that the agent cannot modify.
- Test file boundaries: attempt to read host paths, unrelated user files, secret locations, and files outside the assigned workspace. Confirm access is denied and that the task can still use its intended inputs.
- Test network policy: attempt to reach an unapproved public destination, internal service, metadata endpoint, local service, and administrative route relevant to your environment. Confirm blocked attempts are observable.
- Test credential exposure: inspect the sandbox’s environment and filesystem for unexpected credentials, then test that its identity cannot perform production writes or access unrelated resources.
- Test tool policy: submit invalid targets, arguments, and requests for high-impact tools. Confirm the harness rejects them or requests the required approval.
- Test artifact handling: verify that only expected outputs can be exported and that the deployment process independently validates and authorizes them.
- Test recovery and cleanup: check that task state is handled as intended after completion, cancellation, or failure, and that a later run cannot inherit unauthorized access or writable state.
Record the policy version, runtime configuration, test case, and observed result. Repeat tests when changing mounts, identity, egress rules, tool implementations, runtime settings, or lifecycle behavior. A block observed in one configuration is not evidence that a changed deployment still enforces the same boundary.
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If an agent may have accessed a secret or production route, treat it as a security event rather than assuming that an instruction or sandbox label prevented misuse. Use trusted control-plane logs to determine the task identity, accessible resources, outbound requests, and tool calls. Revoke or rotate potentially exposed credentials, disable affected routes or identities if needed, and preserve relevant audit data outside the sandbox. Review outputs before promoting them, and reset or destroy affected workspace state according to its retention policy.
Containment is strongest when the recovery controls are independent of the execution environment: the agent should not be able to suppress the alert, erase the audit trail, or restore its own privileges.
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