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NIST’s glossary defines a sandbox as a restricted, controlled execution environment that prevents potentially malicious software from accessing resources except those it is authorized to use (NIST CSRC, “Sandbox – Glossary”). For an AI agent, that means designing enforceable boundaries around its execution and connections, not relying on the model to honor a rule.
Start with the task and the threats
Before choosing a runtime or writing an allowlist, specify what the agent must do and what it must not be able to reach. Name the working files, permitted destinations, tools, and actions. Then consider realistic failure paths: the agent may run generated code that reads available files; retrieved content may try to steer it; a tool may permit a broader action than the task requires; or accessible data may be sent over an allowed connection.
Turn those risks into explicit boundaries. If the task only needs to inspect a document, the agent should not have write access to the repository. If it needs to produce a report but not browse the web, it should have no outbound network access. If it must query a service, the tool should authorize only the necessary resources and operations. The objective is least privilege: make unnecessary access unavailable, rather than asking the model not to use it.
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Separate trusted orchestration from agent execution
Keep the trusted control plane—the service or harness that manages model calls, routing, credentials, approvals, audit, and recovery—separate from the compute where model-directed commands and code run, where practical. Give the execution environment only the task-specific inputs, narrow mounts, and runtime configuration it needs. OpenAI’s sandbox-agent documentation describes this separation and notes that putting the harness inside the sandbox places orchestration and model-directed execution within the same compute boundary. That can simplify a prototype, but it changes the security boundary and should be a deliberate trade-off.
Use separate environments when users or workloads must not share data. A restricted workspace does not, by itself, establish isolation between tenants; verify how the selected runtime separates workloads and what persists after a run. Avoid assuming a container, virtual machine, or hosted sandbox is invulnerable. Identify the boundary it enforces, the resources it exposes, and the consequences if the boundary fails.
Limit filesystem access to the working set
Mount or expose only the files the task needs. Keep unrelated repositories, sensitive host paths, deployment materials, configuration, and credentials outside the agent’s reachable workspace. Make write permissions as narrow as read permissions: use read-only access when inspection is sufficient, and limit writable output to a designated location. Review outputs before transferring them into trusted systems.
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For a local coding agent, this means granting access to the project area it needs without treating the whole machine as its workspace. For hosted execution, it means supplying task-specific files or mounts rather than assuming the environment can safely see a user’s broader storage. Exact manifest and permission syntax depends on the runtime; these are design rules, not universal configuration commands. OpenAI’s sandbox guide and sandbox security documentation describe hosted execution controls and their security considerations.
Restrict network access as well as file access
Filesystem rules and network rules address different risks. A path restriction can keep data out of reach, but data the agent can read may still be sent to an arbitrary destination if outbound connections are open. Conversely, a network allowlist does not prevent the agent from reading unrelated local files. Use both controls where the workload warrants them. Anthropic’s Claude Code guidance states, “It is worth noting that effective sandboxing requires both filesystem and network isolation” (Anthropic, “Making Claude Code more secure and autonomous with sandboxing”).
Start with outbound access disabled when the task permits it. If connections are needed, route them through a proxy, firewall, or provider policy that the agent cannot rewrite, and allow only required destinations and ports. Consider where each connection originates: a local executor and a remote tool provider may need different network policies. A domain allowlist is not a complete authorization model for a sensitive service; the service or tool must still check identity, resource scope, and allowed operation.
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Keep broad credentials out of the sandbox
Do not put secrets in prompts, repositories, generated scripts, images, or logs. Code executing inside an environment can read credentials available to that environment. OpenAI’s security documentation puts the risk plainly: “Agent-generated code can access the files, credentials, and network available to its environment” (OpenAI, “Sandbox security”).
Prefer keeping application-level credentials in a trusted secret manager or proxy, outside the sandbox. Have that trusted component supply credentials only for approved destinations and operations. If the sandbox must receive a credential, make it as narrow and short-lived as possible; assume code in the environment can read it. Keep any executor credential separate from broader application keys, and rotate or revoke credentials after suspected exposure. Environment variables are not secret from code running in the same environment.
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A contained shell is not enough if the agent can also call an unrestricted database, email, file, deployment, or administrative tool. Treat every tool as part of the permission boundary. Give each task only the tools it needs, authorize resources and operations at the service layer, and default to read-only access where it is sufficient. Avoid wildcard command and resource permissions.
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Put high-impact actions—such as production writes, deployments, payments, administrative changes, or externally visible messages—behind deterministic policy checks and, when appropriate, independent human approval. The approval interface should show the actual proposed action and relevant data flow, not merely ask for approval of a vague intention. A person cannot make an informed decision if the request obscures which resource will change or what information will leave the system. OWASP’s AI Agent Security Cheat Sheet covers least privilege, tool abuse, and authorization practices; OpenAI’s prompt-injection guidance also recommends confirmation for sensitive actions.
Assume external content may try to manipulate the agent
Prompt injection is an attempt to steer an agent through content included in its context. It can arrive in a web page, email, document, repository file, or tool response. Treat retrieved content as data, not authority: a page or document should not be able to grant itself permissions or override the policies enforced by the harness, operating system, network, or service.
Use clear, specific task instructions, but do not mistake them for containment. Reduce the data and tools available to the agent, validate tool requests outside the model, and require review for sensitive actions. OpenAI explains the risk in its prompt-injection guidance; OWASP’s agent security guidance and Anthropic’s containment article discuss layered defenses and untrusted content.
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Test that the boundaries deny access
Test enforcement at the runtime or service boundary, not just whether the model says it will comply. Use benign test cases that attempt to:
- Read a path outside the assigned workspace.
- Write outside the permitted output location.
- Connect to an unapproved domain or destination.
- Access another user’s or workload’s data.
- Invoke a tool, resource, or operation that the task was not authorized to use.
Confirm that each attempt is denied by the relevant boundary and that the denial is recorded. Log authorization decisions and useful actions while avoiding sensitive content in logs. Tailor tests to the deployed runtime, tools, and threat model; the cited guidance does not establish a universal test suite or a general sandbox-escape rate. Revisit the tests when mounts, network rules, tools, credentials, or runtime behavior change. OWASP’s agent security practices, OpenAI’s sandbox security guidance, and Anthropic’s containment discussion support layered controls and validation, but none certifies a particular deployment.
Choose an architecture for the workload
No single sandbox arrangement is right for every task. Compare the actual isolation and operational controls you need, rather than relying on labels such as “container,” “VM,” or “hosted.” Vendor descriptions below characterize particular implementations; they are not guarantees about all products in a category.
| Approach | What it can provide | Trade-off to evaluate |
|---|---|---|
| Ephemeral hosted workspace | Anthropic describes its claude.ai code-execution environment as server-side, ephemeral, and unable to access the user’s filesystem (Anthropic, “How we contain Claude across products”). | Less continuity and workspace capability than a persistent local project environment; confirm the actual product’s data separation, persistence, and available controls. |
| Local coding-agent sandbox | Anthropic describes Claude Code as using OS-level primitives and a proxy to restrict filesystem and network access (Anthropic, “Making Claude Code more secure and autonomous with sandboxing”). | Useful project access must still be granted; activity outside the boundary may require approval, depending on the implementation. |
| Hosted container or VM execution | OpenAI documents agent sandboxes with workspace and manifest concepts, alongside separate security guidance on workload isolation, outbound allowlists, and credential handling (OpenAI, “Sandbox Agents”; OpenAI, “Sandbox security”). | Provider behavior and configuration matter. Evaluate mounts, egress, persistence, subprocess coverage, and whether the trusted harness and broad credentials remain outside the execution environment. |
| Human review for sensitive actions | Can add a decision point before consequential operations when reviewers see the action and relevant context (OpenAI prompt-injection guidance; OWASP AI Agent Security Cheat Sheet). | Approval does not make broad access safe. Reviewers need enough context and authority to judge the specific action. |
When comparing candidates, check host and tenant isolation, path-level read/write rules, network egress, tool authorization granularity, credential exposure, persistence and cleanup, subprocess coverage, auditability, recovery, and operational effort. One vendor-reported result illustrates why usability figures should not be mistaken for security outcomes: Anthropic said its Claude Code sandboxing was associated with 84% fewer permission prompts in its internal usage, a measure of prompts rather than security incidents or sandbox effectiveness (Anthropic, 2025).
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Model-level refusal and instruction-following can add another layer, but they do not replace access controls. Anthropic reports roughly 0.1% attack success on single attempts and around 5–6% after 100 adaptive attempts for Claude Opus 4.7 on Gray Swan’s Agent Red Teaming benchmark; these are vendor-reported model-layer benchmark results, not sandbox escape rates or guarantees about other agents (Anthropic, “How we contain Claude across products”). The practical security boundary remains what the process, network, and authorized tools permit.
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