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Put an autonomous AI agent’s commands in a disposable, task-scoped sandbox, and assume any code it generates is untrusted. For higher-impact threats, run that sandbox inside a VM or microVM; keep credentials and orchestration outside it; deny internet access unless specific destinations are required; and enforce resource ceilings for every run. A sandbox is a boundary to test and maintain—not a guarantee that an agent cannot escape.
What does sandboxing an autonomous AI agent protect?
A sandbox limits what an agent can affect when it runs commands, executes generated code, installs packages, or handles files. It should confine those actions to a designated execution environment rather than giving them the same access as the host, the operator’s account, or the systems that manage the agent.
This matters because the agent’s output is not inherently safe just because a model produced it. OpenAI’s sandbox security guidance warns that agent-generated code can access whatever files, credentials, and network connections are available to its environment. A prompt telling an agent not to read a file is not an access-control boundary; the operating system, network policy, and credential design must enforce that restriction.
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Keep the control plane separate from the execution plane
The trusted control plane should own model calls, tool routing, approvals, authentication, billing, audit logs, recovery state, and run lifecycle. The execution plane should contain the agent’s commands and task files. OpenAI’s Agents SDK describes this distinction: the harness manages runs and tool decisions, while the sandbox handles files, commands, packages, ports, and snapshots.
Do not put the orchestration service, approval logic, or durable authorization state inside the same environment that executes agent-generated code. If the execution environment is compromised, the attacker should not automatically gain the ability to approve actions, mint credentials, alter audit records, or control other runs.
How should I choose a sandbox boundary?
Choose isolation according to the impact of a successful escape, not just startup speed. A container shares the host kernel; gVisor adds a user-space kernel boundary; and a VM or microVM gives the workload a separate kernel. A hosted sandbox can reduce infrastructure work, but you still need to evaluate its isolation, network defaults, data handling, and credential paths.
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| Option | Isolation boundary | Useful when | Trade-offs to evaluate |
|---|---|---|---|
| Container only | Process and filesystem isolation, but a kernel shared with the host | Lower-risk tasks or environments where the container runs inside a separate VM | Fast and portable, but a kernel vulnerability or misconfiguration can affect the host. Do not treat a container alone as equivalent to a VM. |
| gVisor | A user-space kernel boundary used with containers | You want an additional boundary while retaining a container-oriented workflow | Check syscall compatibility for the workload and maintain the runtime. Anthropic’s reference harness runs both agent processes and target code in gVisor. |
| VM or microVM | A separate guest kernel, with a hypervisor boundary between guest and host | Higher-impact workloads where host separation matters | Typically requires more startup time and resources than a container. Docker documents a local agent-sandbox design with a separate Linux kernel per microVM. |
| Hosted sandbox | Provider-managed execution boundary; exact properties depend on the service | You want managed sessions or less infrastructure to operate yourself | Review provider isolation, network policy, data handling, session lifecycle, and how credentials reach tools. OpenAI documents container sizes, package setup, restricted domains, and vault-backed secrets. |
These options are not mutually exclusive in every architecture. For example, a container can run inside a VM, and a network proxy can protect workloads regardless of which execution boundary you select. Docker’s sandbox design describes layers for the hypervisor, network, Docker Engine, workspace, and credential proxy. Layering boundaries means one failed control does not necessarily give an agent unrestricted access to the host.
OpenAI’s containment report describes defense in depth involving sandboxes, VMs, monitoring, network controls, and cloud perimeters. Its red-team agents found vulnerabilities, misconfigurations, sandbox escapes, movement between systems, and routes to the live internet. That is a practical reason to evaluate combinations of controls rather than assuming any single runtime is infallible.
How do I set up a safe execution boundary?
Build the environment around a specific task, with only the files, tools, and permissions needed to complete it. Keep durable state and decisions outside the execution environment, and make each boundary enforceable rather than dependent on model instructions.
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- Define the allowed task. List the files the agent needs to read or write, the commands and packages it needs, any destinations it must contact, and the actions that require human approval. Identify sensitive data and systems that must remain unreachable.
- Choose isolation based on escape impact. For code that could expose a host, tenant, or production system, prefer a VM or microVM boundary, or place a container within a VM. If using gVisor, test the workload’s compatibility and keep the runtime patched.
- Create a disposable, task-scoped workspace. Mount only the task’s input. Use an isolated output or artifact directory for results. Avoid mounting a broad home directory, the host root, shared caches, or directories containing unrelated projects.
- Harden the process environment. Run as a non-root user, use a read-only root filesystem where feasible, drop unnecessary Linux capabilities, and block host sockets. Prevent access to host credentials, SSH agents, orchestration sockets, and cloud metadata endpoints.
- Set the network policy before execution. Start with no egress. If the task needs network access, permit only the specific destinations it requires through a policy-enforcing proxy.
- Keep credentials in a trusted broker or vault. Issue narrow, short-lived authorization for the task rather than placing a standing application or cloud credential in the sandbox.
- Apply resource and lifecycle ceilings. Set limits for compute, storage, processes, network use, wall-clock time, and concurrent runs. Define what happens when each hard limit is reached.
- Record events outside the sandbox. Retain run lifecycle events, policy denials, resource exhaustion, network destinations, and credential-broker activity where feasible in the trusted control plane.
- Test the boundary before production. Exercise escape, exfiltration, exhaustion, and cross-tenant scenarios. Patch the runtime and dependencies, then repeat tests after meaningful changes.
- Review artifacts before promotion. Treat any file that will later run on a host or enter a build, CI pipeline, or deployment as untrusted until it has been reviewed.
How do I restrict an agent’s internet access?
Deny outbound access by default. Give an agent an explicit, narrow route to only the destinations needed for its task; do not rely on a general-purpose firewall rule or on the model’s promise not to browse elsewhere.
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OpenAI’s hosted sandbox documentation describes disabled and restricted network modes, with exact allowed hostnames. A hostname allowlist must account for redirects and subdomains: allowing one name does not automatically authorize every related hostname. Google’s Gemini guidance says managed agents have unrestricted outbound access by default unless an allowlist is configured, illustrating why operators should set an explicit policy rather than inherit a permissive default.
Use a proxy as the egress enforcement point
In a self-hosted setup, place the agent on a network without a default route to the internet and expose a small egress proxy only when access is necessary. Where the application permits it, enforce destination, HTTP method, path, and rate limits at that proxy—not just a broad domain rule. Anthropic’s reference sandbox uses an internal Docker network with no direct internet route and an allowlist proxy that forwards required model traffic.
Include more than ordinary web requests in the network threat model. DNS lookups, redirects, internal hostnames, metadata services, and alternate protocols can become routes around an incomplete policy. Test whether the agent can bypass the proxy, resolve or reach internal addresses, use an unapproved protocol, or cause a permitted service to forward a request somewhere else.
Log allowed and denied destinations outside the sandbox. Alert on attempts to reach metadata services, internal address ranges, host services, unauthorized domains, or sibling-agent channels. If a task has no legitimate need for network access, disabled egress is simpler to reason about than an allowlist that is broader than necessary.
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Do not place long-lived credentials where agent-generated code can read them. Keep application keys and cloud credentials in a vault or trusted credential broker outside the execution boundary, and grant only the minimum authorization needed for one task.
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Make each grant narrow, temporary, and auditable
- Use a separate credential per task or tenant when practical, so one compromised run does not automatically expose other work.
- Scope authorization to the minimum operations and destinations required. A read-only task should not receive write access just because a broad token is easier to issue.
- Set a short expiration and revoke access when the task ends or an anomaly occurs.
- Log credential issuance and use in the trusted control plane, not only in logs the sandbox can alter.
- Keep package-manager caches containing tokens, SSH agents, cloud metadata access, and orchestration credentials outside the namespace.
Use the conservative rule that any secret readable by agent-generated code is exposed. Hiding a token in an environment variable does not protect it from a process that is allowed to inspect its environment. A broker that performs a narrowly authorized operation without revealing the underlying credential reduces that exposure.
What CPU, memory, disk, process, and timeout limits should I set?
There is no universal safe CPU, memory, timeout, or disk quota for every agent task. Set limits from measured workload needs and the impact of exhaustion, then enforce hard ceilings that stop a run instead of allowing it to consume shared resources indefinitely.
OpenAI’s hosted sandbox documentation offers small, medium, and large container sizes, but does not prescribe universal safe numbers. The appropriate ceiling depends on the task, the isolation layer, the service capacity, and how much resource contention or denial-of-service risk you can tolerate.
| Resource | What to limit | Policy and enforcement questions |
|---|---|---|
| CPU | CPU time or share available to a run | What is the hard ceiling, what usage triggers an alert, and does sustained use terminate the task? |
| Memory | Memory available to the sandbox and its processes | How does the runtime behave at the limit? Ensure an exhausted run cannot destabilize the host or other tenants. |
| Disk and inodes | Workspace size and file count | Can a task fill a shared volume or create excessive tiny files? Keep outputs in a bounded artifact area. |
| Processes and child depth | Process count and, where supported, process-tree depth | Can the run create unbounded child processes? Define a termination response for a process storm. |
| Open files | File descriptors available to processes | Set an upper bound so accidental or deliberate descriptor exhaustion cannot consume shared capacity. |
| Network | Permitted destinations, request rate, and bytes transferred | Use the proxy or network layer to enforce policy and record denials and unusual volume. |
| Wall-clock duration | Maximum run time and, if applicable, idle time | Expire stalled jobs and make cancellation effective across child processes. |
| Concurrency | Simultaneous runs per task, tenant, and service | Prevent one caller or runaway workflow from exhausting the pool needed by other work. |
For each resource, write down the limit, the enforcement mechanism, an alert threshold, and the termination action. Measure ordinary workloads before raising a ceiling; do not make limits so generous that they cease to contain a failure. When a hard ceiling is exceeded, terminate the run, preserve relevant logs and artifacts for review, and investigate rather than silently continuing with a weakened boundary.
What should I monitor and test?
Monitor the boundary from outside the sandbox so a compromised process cannot erase the evidence needed to investigate it. Logs should help answer what started, what it touched, what it attempted to reach, what the policy denied, and why the run ended.
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- Record process starts, lifecycle events, resource exhaustion, and policy denials.
- Capture network destinations and credential-broker requests where feasible, while applying appropriate privacy and data-retention controls.
- Track file access where the operating system and workload make it practical.
- Alert on attempts to contact metadata services, host sockets, internal address ranges, unauthorized domains, or another agent’s communication channel.
- Keep logs in the trusted control plane and correlate them with task, tenant, and run identifiers.
Red-team the actual configured boundary, not just the model’s responses to safety prompts. Include path traversal and symlink escapes, container or VM escape attempts, DNS rebinding, proxy bypass, credential exfiltration, fork bombs, disk exhaustion, and cross-tenant communication. OpenAI’s containment report describes red-team findings involving vulnerabilities and misconfigurations that enabled escapes and internet access; testing is necessary because configuration and software defects can defeat an otherwise sensible design.
How should I handle files and outputs safely?
Give the agent a dedicated workspace and expose only the inputs it needs. If it needs to create persistent work, write to a separate artifact directory, then review the files before moving them into a host-visible project or executing them elsewhere.
Broad read-write mounts can turn sandboxed code into a change to the host’s future behavior. Docker notes that direct workspace mode can let an agent modify host-visible files such as build files, CI configuration, and Git hooks—files that may be executed implicitly later. Treat generated scripts, configuration, and build instructions as code requiring review, even if the initial task was described as editing or summarizing files.
Use snapshots or disposable sessions when rollback is useful. Keep host credentials, package-manager caches that contain tokens, SSH agents, cloud metadata endpoints, and orchestration sockets outside the mounted namespace. A read-only root filesystem helps prevent changes to the base environment, but it does not make a writable project directory safe to execute without review.
What does the 2025 AI Agent Index say about sandboxing?
The MIT AI Agent Index’s 2025 snapshot reports that sandboxing or VM isolation was documented for 9 of the 30 agents it surveyed. It also reports that 25 of 30 disclosed no internal safety results and 23 of 30 had no third-party testing information.
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Those figures describe the systems and disclosures included in that index, not a universal estimate of the agent market or proof that any particular product is unsafe. They do show why readers should look for concrete documentation of isolation boundaries and testing rather than treating the label “agent” or “sandboxed” as evidence of a specific security property.
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How should I respond when a boundary fails?
Plan for containment failure before deploying the agent. A detected escape attempt, unexpected destination, credential anomaly, or unexplained resource spike should trigger a defined response rather than an informal review after the run continues.
- Stop the affected run. Terminate the process tree and revoke task-scoped authorization through the control plane.
- Isolate the environment. Block its network path and prevent it from communicating with sibling runs or shared services.
- Preserve evidence. Retain external logs, policy decisions, and relevant artifacts without allowing the sandbox to overwrite the record.
- Assess exposure. Determine which files, destinations, credentials, tenants, and host services were actually reachable, then rotate credentials that may have been exposed.
- Restore from a known-good state. Rebuild or discard the execution environment rather than assuming cleanup inside a potentially compromised sandbox is sufficient.
- Fix and retest. Patch the runtime or dependency, correct the policy or configuration, and repeat relevant adversarial tests before restoring production access.
Keep recovery state and the authority to revoke credentials outside the execution environment. That way, an agent process cannot prevent operators from stopping a run or recovering the service if the sandbox itself is no longer trustworthy.
Frequently Asked Questions
Can an agent safely read sensitive files if it cannot write to them?
Read-only access prevents modification, not disclosure. If the agent can process sensitive content, it may still include that content in an output or send it over any communication channel the sandbox permits, so limit inputs to data appropriate for the task.
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Use observed task failures and resource telemetry, not guesswork: identify which policy denied or terminated the run, then compare that event with successful runs for the same workload. Change one limit at a time and verify both task completion and containment behavior.
Should every agent task run in its own sandbox?
Separate task environments reduce the chance that one run can read another run’s files or interfere with its processes. Where reuse is necessary, treat it as a deliberate sharing exception and verify that workspace, credentials, network permissions, and cleanup are isolated between tasks.
Does a sandbox make prompt injection harmless?
No. Prompt injection can still steer an agent toward actions it is permitted to take, including misusing exposed files or approved network destinations. Sandboxing constrains impact; it does not replace careful tool permissions, approval rules, and review of sensitive actions.
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