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This matters whenever an agent can run code, read or change files, call APIs, or communicate over a network. A prompt, model safety feature, or approval dialog may reduce risk, but none independently contains the consequences of mistaken or manipulated instructions.
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What an execution boundary separates
An agent system has two useful architectural planes. The control plane manages the agent loop, model calls, tool routing, handoffs, approvals, tracing, recovery, and run state. The execution plane is where model-directed work can read and write files, run commands, install dependencies, use mounted storage, expose ports, or preserve workspace state. OpenAI describes this division in its Agents SDK documentation.
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Keep sensitive application functions—such as identity checks, authorization, billing, audit records, human review, and recovery—under trusted application control rather than inside model-directed compute. The sandbox should receive only the workspace, mounts, packages, and tools needed for the task.
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Not every model call needs a sandbox. A short response that does not need a persistent workspace may use a simpler runtime. An isolated execution environment becomes more important when a task needs code or commands, generated artifacts, preview services, mounted data, or resumable work.
Why the boundary matters
An agent can encounter untrusted content and act through tools. That combination creates a path from manipulated instructions to real side effects. Treat model output as a proposal, not as proof that an operation is authorized. The OWASP AI Agent Security Cheat Sheet puts the principle plainly: “The agent can propose an action, but a policy service or execution component should independently validate scope, privilege, and approval state before execution.”
Validate the specific operation at the point where it will be dispatched: the actor, tool, target, parameters, privileges, and any required approval. A broad approval to “use tools” is not equivalent to approval for a particular consequential action.
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Build the boundary into the architecture
Keep control-plane services outside execution
Run the harness and sensitive services in infrastructure controlled by the application. Give the execution environment only the inputs and capabilities needed for the current task. Where workloads must not share data, use separate per-user or per-workload environments. For stateful jobs, define what is persisted, how a session resumes, and what is deleted at completion; persistence is useful, but it also increases the amount of state that must be governed.
Limit files and mounts
Define a workspace contract for each session: which input files, repositories, output directories, and mounts are available. Mount only the data the agent should access, and review artifacts before moving them out of the sandbox—especially when private documents or mounted data were present.
For self-hosted environments, consider a non-root process, removal of unnecessary Linux capabilities, a read-only root filesystem, and mounts limited to directories the tools need. The operator’s responsibilities for self-hosted sandboxes are described in Anthropic’s Claude Platform self-hosted sandbox security model.
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Start with outbound allowlists for destinations required by the workflow. Account for where connections originate: an executor in your infrastructure and a remote MCP connection may need different network paths. A trusted proxy can enforce destination rules and attach scoped credentials to approved requests.
Network and filesystem restrictions solve different problems. Network controls can limit exfiltration; filesystem controls can prevent access to sensitive local material. Anthropic’s sandboxing article argues that the two work together. Allowing one does not replace restricting the other.
Keep credentials out of model-directed execution
Do not place long-lived application keys in prompts, instructions, images, source code, or logs. OpenAI warns that an executor environment key is readable by agent-generated code and recommends keeping the application key outside that environment. Environment variables do not make a secret safe from code that runs in the same environment.
For third-party APIs, have a trusted proxy or application-side function hold credentials and broker the request. Give it only the access needed for the approved operation, then return only the result the agent needs.
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Use deterministic policy checks at the component that actually dispatches an action. Classify risk; where appropriate, allow explicitly low-risk operations to proceed without review, but fail closed for unknown actions or when policy, approval, or audit checks fail.
For high-impact operations, bind approval to the actor, tool, target, normalized parameters, timestamp, and expiry. Use replay protection and step-up authentication for critical actions, and make operations idempotent when possible. An approval should authorize the operation it describes, not become a reusable permission for materially different parameters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an environment by its real boundary
Hosted and self-hosted environments shift operational responsibility differently. The available provider documentation describes designs and responsibilities; it does not establish an independent cross-vendor security benchmark. Compare the deployment against your workload and threat model rather than ranking products from feature descriptions alone.
| Decision axis | Questions to answer |
|---|---|
| Trust boundary and ownership | Who operates the harness, execution worker, sandbox image, and tool processes? Which security responsibilities remain with your team? |
| Isolation scope | Are files, subprocesses, mounted storage, and network controlled separately? Which tools or MCP servers run inside the same boundary? |
| Network control | Can you allowlist outbound destinations? Is a proxy available, and where do remote tool connections originate? |
| Credential exposure | Do application keys stay outside execution? Are session credentials scoped, and can a proxy broker access to third-party services? |
| Data location and lifecycle | Where do session content, memory copies, logs, and artifacts live? Who retains and deletes them? |
| Operational fit | Does the task need resumable work, persistent state, package installation, mounted data, or exposed ports? |
What a sandbox does not guarantee
A sandbox reduces the resources available to model-directed code; it does not remove the need to secure the runtime, policy service, tool integrations, or data lifecycle. For a self-hosted deployment, the operator remains responsible for runtime hardening, egress rules, data retention, image integrity, and isolation between tools within the sandbox.
Anthropic reported 84% fewer permission prompts in its internal Claude Code usage after introducing sandbox boundaries, in its 2025 article. That is a vendor-reported internal observation, not an independent test, a measure of attacks prevented, or a result teams should expect in other environments.
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