The Tool Desk
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Why can’t we just trust an AI agent to follow instructions?
Because an agent’s judgment is only one part of the security picture. Its practical reach also depends on the tools it can call, the orchestration that connects those tools, and the environment where it runs. A well-behaved model in a broadly privileged environment can still cause serious harm if it makes a mistake or is manipulated. The same failure in a tightly bounded system may have fewer consequences.
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Anthropic’s response to a NIST request for information frames the point this way: “Agent security is a property of the whole system, not just the model.” The response is the authors’ perspective, not a NIST standard or endorsement. Its related observation is concise: “The failure is identical. The consequences are not.”
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That is why prompts such as “never reveal secrets” or “only use approved files” should not be the only safeguards. A prompt expresses intent; an execution boundary limits what can happen even when the agent does not follow that intent.
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What does sandboxing actually protect?
Sandboxing limits an agent’s access to resources outside the model’s reasoning process. Depending on how it is configured, that can include files, network connections, credentials, tools, or the ability to affect other workloads. It does not prove that the agent will behave safely, but it can restrict the paths available to a mistake or an attack.
Filesystem boundaries limit local access
Restrict the process to the files and directories needed for its task. Consider not only files it can edit, but also sensitive files it can read, mounted data, shared working directories, and state that might persist between runs. An agent that cannot read a secret file cannot directly copy its contents from that file.
Network boundaries limit connections
Restrict outbound connections to the destinations the task actually requires, preferably with an explicit allowlist rather than unrestricted access. A process that can reach the public internet may be able to send out data it has already read, even if it cannot modify local files.
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Anthropic describes filesystem restrictions as controls over accessible or modifiable directories and network restrictions as controls over connections to approved servers. In its account of Claude Code sandboxing, it says: “It’s by using both techniques that we can provide a safer and faster agentic experience for Claude Code users.” That is a product-specific description, not a guarantee that every sandbox is safe.
Do filesystem and network restrictions need to be used together?
When an agent executes commands or model-directed code, use both where feasible because they block different routes. Filesystem restrictions alone do not stop a process from transmitting data it can read over an open network. Network restrictions alone do not stop it from reading or changing local files that should have been out of reach.
Think of the controls as complementary: filesystem rules constrain what data the process can obtain or alter; network rules constrain where it can send information or retrieve more content. The right policy depends on the task, but leaving either route broad can undermine the other boundary.
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How do I keep an agent from accessing secrets or sending data out?
Keep high-value credentials outside the agent environment
Do not put an application API key or broad third-party credentials in a location that agent-generated code can read. OpenAI warns that code produced by an agent can access the files, credentials, and network available to its execution environment. Where access is needed, use a broker or proxy that supplies narrowly scoped capabilities instead of exposing a reusable secret to the agent.
Give tools only the permissions they need
Start from the task’s required data, tools, and actions, then remove unused access. Scope each tool by operation and resource: a tool that can read one project should not automatically be able to write across an account, and a tool that can draft an action need not be able to execute it. Use separate tool sets where tasks have different trust levels.
OWASP recommends minimizing available tools, scoping permissions per tool, and enforcing those limits independently of the model’s context. The policy should be applied by the tool or execution layer, not merely described in a prompt.
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Separate proposals from sensitive execution
For actions with meaningful consequences, let the agent propose an action and have an independent policy layer validate it before execution. Bind approval to the actor, tool, target, and normalized parameters. If the target or parameters change, require approval again rather than treating an earlier approval as blanket permission.
Handle outside content as untrusted
Web pages, documents, emails, and API responses can contain instructions that conflict with the task. Treat them as data, not authority: delimit the content, validate it before storing it or using it in later sessions, and do not let text from an external source silently authorize a tool call.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I compare when choosing an agent sandbox?
There is no single benchmark or universal product ranking established by these sources. Compare the actual boundaries and enforcement model rather than relying on a general label such as “sandboxed.”
- Files and data: Which files, mounts, and secrets are visible to the agent process? Can it write to shared or persistent locations?
- Tools: Which operations and resources can each tool access? Are permissions scoped individually, and can the model invoke the tool without an independent check?
- Network: Is outbound access denied by default or restricted to an explicit list of approved endpoints?
- Credentials: Are secrets kept outside the execution environment and supplied through scoped brokering when needed?
- Approvals: Does an independent execution layer validate sensitive actions and the exact target and parameters that were approved?
- Isolation and state: Are workloads separated across users and tasks? What data or state can carry over between runs?
- Verification: What logs, adversarial tests, and release gates demonstrate that the intended boundaries are enforced?
How should teams put these boundaries into practice?
- Define the task’s minimum access. List the data, tools, operations, and destinations needed to complete it. Remove permissions that do not serve that task.
- Enforce limits outside the model. Apply filesystem, network, and tool policies in the execution environment or tool layer. Do not depend on instructions in the prompt to enforce access control.
- Keep credentials out of reach. Store high-value credentials outside the agent-accessible environment and provide narrowly scoped access through a broker or proxy when required.
- Put checks before consequential actions. Have an independent layer validate the action and its exact parameters; request renewed approval if those details change.
- Control untrusted inputs. Treat retrieved and user-supplied external content as untrusted, and validate it before persistence or use in later sessions.
- Test how boundaries fail. Before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers, test tool misuse, privilege escalation, data exfiltration, approval bypass, and cross-agent boundary failures.
What boundaries can—and cannot—promise
These controls reduce the resources an agent can reach and the consequences available to it; they do not guarantee that an agent is safe or prevent every attack. OWASP presents its recommendations as architectural guidance, while vendor descriptions of their own systems should not be read as universal guarantees. Use boundaries alongside model and tool safeguards, monitoring, approval processes, and ongoing adversarial testing.
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