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Give an AI agent a disposable execution environment, not your workstation or production account. The safe pattern is to isolate its compute, mount only task-specific files, deny outbound network access by default, keep real credentials in a vault or trusted proxy, and route every consequential action through an approval-aware harness.
The sandbox limits what model-directed code can reach; it does not make an agent trustworthy by itself. Authorization rules, audit logs, credential issuance, and recovery state must remain outside the sandbox so a compromised process cannot rewrite its own controls.
What a sandbox actually protects
A sandbox is an isolated runtime—such as a separate virtual machine, container, or hosted execution environment—with an intentionally small view of the world. Its security boundary is the complete set of files, credentials, network paths, processes, and tools that code inside it can reach.
OpenAI’s Sandbox security guidance states: “Agent-generated code can access the files, credentials, and network available to its environment.” Treat that as an engineering rule. If a secret is mounted, placed in an environment variable, returned by a tool, readable through a shared directory, or reachable over the network, assume the agent can use it.
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What isolation can and cannot do
- It can reduce blast radius: a disposable workspace limits damage from a malicious prompt, a compromised dependency, or an agent that misunderstands its task.
- It cannot decide intent: instructions alone do not prevent an agent from sending the wrong message or modifying the wrong record.
- It cannot protect reachable resources: unrestricted egress, broad mounts, or long-lived credentials can defeat an otherwise strong container boundary.
The security architecture: untrusted executor, trusted harness
Split the system into two planes. The executor runs model-generated commands, reads approved files, and produces drafts or artifacts. The harness runs in trusted infrastructure outside that executor and owns orchestration, authorization, policy evaluation, audit records, billing, and recovery.
Keep these responsibilities outside
- Credential storage and issuance.
- Allowlist and policy decisions.
- Human-approval state and the record of who approved an action.
- Immutable or append-oriented audit logs.
- Production identifiers, rollback information, and recovery state.
The sandbox may request an operation, but it should not be able to approve its own request, change the policy that evaluates it, or erase the evidence afterward. If model orchestration itself runs in the same environment as the task, a successful escape or dependency compromise can affect both execution and authorization.
Use a separate environment per workload
Provision a separate VM, container, or hosted sandbox for each user or isolated workload. Start with a disposable workspace and an explicit working directory. Destroy the workspace when the task ends, especially after handling untrusted files or testing a new tool integration. Persistence and snapshots can be useful for debugging, but they should be deliberate rather than an accidental way to retain secrets or customer data.
A permission ladder for agent actions
Do not jump directly from “the agent can inspect data” to “the agent can change production.” Move through progressively more consequential states, with a policy decision between them.
| Stage | What the agent may do | Required control |
|---|---|---|
| Observe | List metadata, read a small test file, or fetch a non-sensitive API response. | Scope paths and endpoints; log the request and response size. |
| Draft | Prepare an email, API payload, code change, or report without transmitting or committing it. | Store the draft in the workspace for review; do not expose a send or write capability. |
| Stage | Write to a quarantine directory, test tenant, or other reversible destination. | Show the exact diff or payload and keep production credentials unavailable. |
| Approve | Request authorization for a named recipient, scope, destination, and side effect. | Require a human or independent policy engine to approve the specific parameters. |
| Commit | Send the email, mutate production data, or perform the external write. | Use a broker that enforces the approved parameters and records credential use. |
Separate read and write tools so a read-only workflow cannot invoke mutation functions by changing a parameter. An approval should expire, identify the exact operation, and be unusable for a different recipient, endpoint, attachment, or record set.
Control the filesystem first
Start with no mounts, then add the smallest directory that lets the task succeed. Give the process an explicit working directory rather than its host’s home directory or root filesystem.
Mount only task data
- Expose a dedicated input directory and a separate output or quarantine directory.
- Mount sensitive reference data read-only whenever possible.
- Do not mount home directories, SSH keys, cloud credential folders, source-control tokens, browser profiles, device files, or host sockets.
- Keep the harness’s logs, policy database, and recovery files outside the executor’s mounts.
Make artifacts reviewable
Have the agent write generated files to a quarantine location. The harness should inventory filenames, sizes, and hashes, then present a diff or preview before anything is exported. Reject unexpected file types, paths that escape the workspace, and outputs whose size exceeds the task’s limit. Review artifacts before they are copied to a user-visible or production location.
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Use synthetic data during development
Test with harmless accounts and synthetic files before granting access to customer records. Include decoy secrets and files with unusual names to verify that path checks, redaction, and export rules behave as intended. A successful test should show that the agent can complete the intended task without seeing unrelated material.
Design a safe email workflow
Email combines sensitive content, an external audience, and an irreversible side effect. Treat composition and transmission as different capabilities.
1. Read only approved mail data
Expose a read function that accepts an approved mailbox, folder, and message identifier. Enforce those limits in the tool implementation, not in the prompt. Return only the fields needed for the task, and cap the amount of text or attachment data returned to the model.
2. Produce a draft, never a send request
During drafting, the agent should output recipients, subject, body, and attachment names as data in the quarantine area. It should not receive a reusable SMTP token or a general-purpose mail API key.
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The harness should display the exact recipient list, reply-to address, attachment paths, and rendered body. Check for external domains, hidden recipients, sensitive attachments, and instructions that conflict with the user’s request. Require an explicit approval tied to this exact draft.
4. Send through a constrained broker
After approval, a broker—not the model—retrieves a narrowly scoped credential and sends only the approved message. The broker should reject changed recipients, attachments, or body content. Record the policy decision, identity, destination, credential use, and result.
Give API access without handing over the keys
For APIs, distinguish observation from mutation and production from testing. A read endpoint that returns a non-sensitive response belongs at the lowest permission stage; a write to a production record belongs at the highest.
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Use a test tenant or staging destination
Permit writes only to a test tenant while developing the workflow. Let the agent produce the request and show the exact diff. Promotion to production should require a separate approval and, ideally, a newly issued credential restricted to the approved operation.
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Broker credentials outside the sandbox
Store application keys in a vault or trusted proxy. The sandbox receives an environment key that can connect to its execution service, but the application API key remains outside the model-visible environment. The proxy should verify the host, HTTP method, path, tenant, object identifiers, and approved parameters before injecting a short-lived secret for one operation.
Limit responses and destinations
Allow only the domains and ports required by the task. Cap response sizes and reject redirects or destinations that are not on the allowlist. Large or unexpected responses can expose data unnecessarily and may indicate that the agent reached an unintended service.
Default-deny network egress
Start with outbound network access disabled. Add narrowly defined domain and port rules only after the workflow’s destinations are known. A sandbox with unrestricted egress can exfiltrate mounted files, probe internal services, or transmit credentials even when its filesystem appears isolated.
Separate executor and remote-tool traffic
Executor traffic and remote MCP traffic may originate from different infrastructure. Apply policy to both paths instead of assuming that a restriction on the container’s network namespace also covers a remote tool server. Log the destination, request type, identity, policy decision, and response size for every allowed call.
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An allowlist should cover the resolved destination and the final connection, not only a string in the original URL. Reject unexpected redirects and attempts to connect by raw IP address when the task requires a named service. Keep the rule set small enough that a reviewer can understand why each destination is present.
Protect credentials throughout their lifecycle
- Never place long-lived secrets in prompts, system instructions, source code, images, logs, or generated artifacts.
- Issue narrowly scoped credentials only after the harness approves a specific host and operation.
- Make them short-lived and revoke them immediately after suspected exposure or task completion.
- Prevent leakage in diagnostics: redact headers, tokens, cookies, and authorization errors before returning tool output to the model.
- Rotate routinely: a rotation schedule limits the value of a secret that is accidentally retained in a snapshot or log.
The environment key used to connect the sandbox to its execution service is not a substitute for an application credential. Keep those trust domains separate so compromise of the executor does not automatically grant access to external systems.
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Filter tools and MCP functions
Expose only the functions needed for the current workflow. Agents SDK documentation describes MCP tool filters and input guardrails that can block a call before execution, while output guardrails inspect the converted result before it returns to the model.
Input checks
- Allow
read_fileonly beneath an approved directory; reject path traversal and symlink escapes. - Allow
send_emailonly when the recipient, attachment list, and approval identifier match the reviewed draft. - Permit API writes only to the approved test tenant or the exact production objects named in the approval.
- Reject unexpected hosts, methods, payload fields, and oversized requests.
Output checks
- Cap response size and truncate or deny oversized results.
- Redact secrets and unrelated personal data before returning content to the model.
- Validate returned JSON or files against the expected schema.
- Record what was filtered, not only what was allowed, so an investigator can reconstruct the decision.
Choose an implementation model
| Option | What you control | When it fits | Main trade-off |
|---|---|---|---|
| Hosted sandbox | Packages, files, setup commands, environment variables, container size, and network settings exposed by the provider. | You want managed Linux compute without operating the isolation layer. | You must verify the provider’s regions, data handling, network controls, and credential-brokering capabilities. |
| Self-hosted or Docker-backed sandbox | Image, private network, data locality, runtime policy, mounts, and lifecycle. | Regulated workloads or internal services that cannot be exposed to a provider network. | Your team owns patching, capacity, monitoring, escape-risk management, and incident response. |
| Trusted harness outside the sandbox | Model orchestration, approval policy, billing, audit logs, credential issuance, and recovery state. | Every deployment; it provides the control plane for either execution model. | It adds integration work, but omitting it puts authorization in the same trust boundary as model-directed code. |
Compare implementations on isolation strength, filesystem and mount controls, egress policy, credential brokering, MCP and tool filtering, approval hooks, artifact review, persistence and snapshots, auditability, and operational burden. A convenient hosted runtime is not automatically suitable for regulated data, and a self-hosted container is not automatically safe if it has broad mounts or unrestricted network access.
Build the sandbox step by step
- Define the task boundary. Write down the exact files, mailbox data, API resources, destinations, and side effects the workflow needs. Anything not on that list starts unavailable.
- Classify actions. Mark each operation as observe, draft, stage, approve, or commit. Do not combine a read function with a mutation function merely because both use the same service.
- Create a disposable runtime. Use a separate VM, container, or hosted sandbox for the workload, with an explicit working directory and no inherited home directory.
- Add minimal mounts. Mount only the approved input directory and a quarantine output directory. Make reference data read-only and keep harness state outside.
- Disable egress. Start with no outbound network. Document every domain and port that must be enabled, then add only those rules.
- Build the broker. Put application credentials in a vault or trusted proxy. Require the broker to verify operation, destination, parameters, approval, and credential lifetime.
- Register filtered tools. Expose only the MCP functions and local tools needed for the workflow. Add path, host, method, tenant, size, and schema checks before execution.
- Implement draft and stage outputs. Save email drafts, API payloads, diffs, and generated files in quarantine. Make the exact proposed side effect visible to a reviewer.
- Add approval gates. Require an independent human or policy engine to approve recipients, scope, destination, attachments, and irreversible changes. Bind approval to immutable parameters and an expiration time.
- Commit through the broker. Only the broker performs sending or production mutation, using a short-lived credential and the approved values.
- Instrument everything. Record tool requests, policy decisions, credential use, destinations, response sizes, exported artifacts, and approval identity in trusted logs.
- Test and tear down. Exercise failures with synthetic files and harmless accounts, verify that denied operations stay denied, revoke credentials, and destroy the workspace when the task ends.
Common failure modes and fixes
The agent reads files outside the assignment
Cause: a broad mount, inherited working directory, symlink, or path traversal.
Fix: mount a dedicated directory, enforce canonical paths in the tool, reject symlink escapes, and test attempts to access parent directories.
A secret appears in a prompt or log
Cause: credentials were injected as environment text, included in debugging output, or returned by an API error.
Fix: move the secret to the broker, redact tool results and logs, rotate the exposed credential, and revoke it immediately if exposure is suspected.
The agent sends an unreviewed email
Cause: the send capability was available during drafting, or approval was not bound to the final recipients and attachments.
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Fix: remove send access from the drafting tool, require a reviewed draft identifier, and make the broker reject any changed field.
An API write reaches production
Cause: a production key or broad endpoint was available before staging and approval.
Fix: use a test tenant by default, restrict host and tenant in the broker, and issue a separate short-lived production credential only after promotion is approved.
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Cause: remote MCP traffic, redirects, DNS resolution, or a second runtime was not covered by the egress policy.
Fix: apply allowlists to every execution path, validate final destinations, reject unexpected redirects, and log each network decision.
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Fix: write trusted, append-oriented records outside the executor containing identity, request, destination, decision, approval, credential event, and result metadata.
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- Use a disposable workspace and a non-production account for initial runs.
- Mount the smallest possible directory; make sensitive mounts read-only.
- Deny network access until a destination allowlist exists.
- Keep secrets outside prompts and files; use a broker or vault.
- Separate read, draft, stage, and commit tools.
- Require approval for email sends, record changes, deletion, and infrastructure deployment.
- Log every tool request, policy decision, credential use, destination, and output.
- Review generated artifacts before exporting them.
- Revoke credentials and destroy the workspace when the task ends.
- Test denial paths with synthetic files and harmless accounts before exposing real data.
The practical test is simple: if the agent is compromised, can it reach anything beyond the current task, obtain a reusable credential, contact an unapproved destination, or perform an irreversible action without an independent decision? If the answer is yes, narrow the sandbox or move that capability behind the trusted harness.
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