The Tool Desk
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Safe tool-using agents require three separate controls: permissions determine which tools and actions are available, a sandbox technically limits files, network access and credentials, and an approval policy determines whether a permitted call runs automatically, is denied or pauses for a person. Use all three. A prompt telling the model to “be careful” is guidance, not an enforcement boundary.
The practical rule is least privilege inside an isolated environment, human approval for consequential operations, automatic evaluation only where its residual error is acceptable, and complete event logging so decisions can be investigated.
What each safety layer actually controls
Permissions decide what the agent can call
A permission is an authorization decision about a capability: reading a repository, writing a file, running a shell command, calling an MCP server, sending an email or using a deployment tool. Removing a tool removes that capability from the agent. A policy applied to an enabled tool does something different: it controls when that tool may execute.
Use narrow scopes. An agent that only edits files in a temporary checkout should not receive a general filesystem tool. A support agent that can read ticket data does not automatically need a database-write tool. Keep destructive operations separate from read-only and reversible operations so they can receive different approval treatment.
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Sandboxing enforces a technical boundary
A sandbox is the execution environment in which agent-generated code runs. Code can access whatever files, credentials and network routes that environment exposes; the model’s stated intention does not change those facts. OpenAI’s sandbox security guidance recommends isolated compute for workloads that should not share data, outbound-network allowlists and separate credential handling.
Sandbox controls are preventive constraints. They can make a write impossible outside an approved directory, block an unapproved destination and prevent the process from seeing a host secret. They do not decide whether an otherwise permitted action is appropriate for the current task; that is the role of permissions and approval.
Approval policies decide whether a call proceeds
Approval is a runtime gate. A call can be allowed without confirmation, denied, or paused for a human decision. “Always ask” is a true human checkpoint. An automatic evaluator is a risk decision made by software and may run the call before a person sees it.
OpenAI summarizes the relationship in its deployment guidance: “Approvals and sandboxing work together.” Sandboxing defines the technical boundary; approval policy determines when Codex must ask before crossing a boundary or performing another sensitive action. The two controls should be configured together rather than substituted for each other.
Model instructions are not a security boundary
System prompts and developer instructions can explain intent, but an agent may misunderstand a request, follow malicious content in a file or be manipulated by prompt injection. Treat instructions as behavior guidance. Enforce the actual limit with tool scopes, operating-system isolation, network policy, credential brokering and approval gates.
How the controls work together in one run
- Exposure: the agent receives only the tools and data sources needed for the task.
- Execution: generated code runs inside a sandbox whose filesystem, network routes and environment variables are constrained.
- Decision: the approval policy evaluates each enabled tool call. It may allow, deny or pause for a reviewer.
- Observation: prompts, calls, results, approvals and network decisions are recorded for audit and incident response.
For example, an agent may be permitted to edit /workspace/project but not /etc, and may reach only an internal package mirror. A request to modify a production deployment tool can still pause for a human even though the process is sandboxed. Conversely, approving a call does not grant access beyond the sandbox: a command approved by a reviewer still cannot read a path or contact a destination that the environment blocks.
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A practical configuration sequence
1. Classify actions by consequence
Make an inventory of every tool and classify operations as read-only, reversible write, externally visible, financial or destructive. Include indirect effects: a script that edits a configuration file may restart a service; a ticket update may notify customers. Identify which actions require a person under your risk policy before choosing product settings.
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2. Remove unnecessary tools and narrow scopes
- Expose read-only search instead of a general shell when search is all the task needs.
- Limit file tools to a project directory or a per-job checkout.
- Separate production and development credentials and tools.
- Give MCP integrations only the servers and methods required for the workflow.
Document the owner, data classification and intended use for each enabled capability. A disabled tool is unavailable to the agent; a denied call to an enabled tool is a runtime event that should be visible in logs.
3. Create an isolated execution environment
Use a disposable virtual machine or equivalent isolated compute when jobs must not share data. Build the image with only required runtimes and mount a temporary workspace. Set explicit read-only mounts for reference data and a separate writable directory for outputs. Destroy or reset the environment after the job when persistent state is not needed.
4. Set outbound network policy
Start with networking disabled for tasks that do not need it. For tasks that do, allowlist exact hosts and ports, such as an internal package mirror or a specified API endpoint. Route third-party access through a proxy that can enforce destination and method policy instead of handing the agent unrestricted network access. Record allow and deny decisions.
5. Keep powerful credentials outside agent-written code
OpenAI’s sandbox guidance recommends keeping an application API key outside the environment. Its example gives the executor an environment key that only connects environments. For third-party services, a proxy can present real credentials only to approved destinations. A secret injected directly into the environment is still readable by agent-generated code, so environment-variable injection is not equivalent to secret isolation.
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Use automatic execution for low-impact, reversible calls whose sandbox and network limits are strong. Require always_ask-style review for production changes, external messages, data exports, purchases, credential changes and irreversible deletion. Deny capabilities that are outside the workflow rather than relying on a reviewer to reject every attempt.
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7. Export and retain audit events
Capture enough context to reconstruct a decision: the user and agent identity, prompt or task identifier, tool name and arguments, approval result, tool output, sandbox violations, MCP usage and network allow or deny events. Protect logs from modification and define retention according to your incident-response and privacy requirements.
Designing least-privilege permissions
Permissions should answer “which capability?” while approval answers “under what conditions now?” Use a capability matrix rather than one broad agent role.
| Capability | Safer default | Escalation trigger |
|---|---|---|
| Read project files | Allow only the task checkout | Request to read another repository or sensitive directory |
| Write files | Allow a temporary workspace | Write outside the workspace or modify deployment configuration |
| Shell or code execution | Run in the sandbox with restricted mounts | Privileged command, persistence mechanism or destructive operation |
| Network requests | Disabled or limited to an allowlist | New host, upload of data or unrestricted egress |
| MCP or external tools | Enable only required servers and methods | Mutation, data export or action on behalf of a user |
Make denials enforceable. If a capability is unacceptable even with review, do not enable it or block it at the sandbox or proxy layer. An approval dialog is not a substitute for a technical deny rule when the operation is prohibited.
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Filesystem boundaries
Decide separately where the process may read and where it may write. Read access can expose secrets through source files, configuration, shell history or mounted home directories. Write access can enable persistence, tampering or data destruction. Mount only the directories needed for the job, prefer temporary workspaces and prevent traversal into host paths.
Network boundaries
Network access expands the attack surface beyond the local machine. A deny-by-default egress policy is easiest to reason about. When allowlisting, specify destination names and ports, resolve how redirects are handled and prevent an allowed service from becoming an unrestricted proxy. Treat uploads as high consequence even when downloads are harmless.
Credential boundaries
Credentials should be short-lived, scoped and delivered by a broker where possible. Keep application keys and host credentials outside the agent’s process. A proxy can authenticate to an approved service while exposing neither the raw key nor unrelated destinations. Rotate credentials after a suspected prompt-injection or sandbox escape, and review logs for attempted exfiltration.
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Isolation boundaries
Separate jobs that handle different customers, classifications or trust levels. OpenAI specifically recommends isolated compute for workloads that should not share data. A single long-lived environment can let one task read another task’s files or cached credentials even when each individual tool call appears authorized.
Approval modes and their trade-offs
Names differ by product, but three patterns recur:
- Always allow: an enabled tool executes without confirmation. This is suitable only for low-impact operations inside a well-constrained sandbox.
- Always ask: execution pauses until a person approves or rejects the call. Use this when review must occur before the side effect.
- Automatic evaluation: a classifier or reviewer decides to allow, deny or pause. It reduces interruptions but introduces model and classifier error.
Anthropic’s Managed Agents documentation states the distinction plainly: “auto is not a human checkpoint.” In that API, agent-tool permissions default to always_allow, MCP toolsets default to always_ask, and per-tool overrides are available. These are beta, product-specific details; do not assume the same defaults in another Anthropic product or deployment.
For a human gate to be meaningful, show the proposed tool, complete arguments, affected resources and likely side effects. Give the reviewer a way to deny, edit or narrow the request. Do not hide a consequential operation behind a generic “continue” button.
What the current product documentation says
| Documented system | Permission behavior | Sandbox or environment guidance | Approval behavior | Audit details |
|---|---|---|---|---|
| OpenAI agent environments | Tool exposure is configured by the application | Files, credentials and network available to the environment are accessible to generated code; isolated compute and outbound allowlists are recommended | Product-specific; use the documented environment and approval controls together | Sandbox guidance emphasizes credential and network boundaries |
| OpenAI Codex deployment guidance | Approvals govern when Codex asks before an action | Sandboxing defines technical limits for writes, network reach and protected paths | Auto-review can evaluate some boundary-crossing requests | OpenTelemetry export can include prompts, approvals, tool results, MCP usage and network-proxy decisions |
| Anthropic Managed Agents API | Disabling a tool removes it; enabled tools can have per-tool policies | Environment controls are configured separately from the permission policy | always_allow, always_ask or auto; auto may allow, deny or pause |
The cited permission documentation describes policy behavior rather than a universal logging schema |
| Anthropic Claude Code Auto mode | The default permissions ask before every file write and Bash command | Anthropic still recommends isolated environments | A classifier judges calls as safe, risky or requiring further interaction; safe calls may proceed before a person sees them | Use the product’s documented logging and deployment controls for your installation |
These rows describe the cited products and documents, not a neutral certification or a benchmark. Features, defaults and availability can change; verify the linked documentation for the version and deployment you operate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What automatic review can and cannot tell you
OpenAI’s 2026 report on Auto-review says that, in an internal deployment snapshot, 720 out-of-sandbox actions would have interrupted a user under manual approval. Auto-review examined them: 7 were rejected, 4 continued by a safer path and 3 stopped for user input. The same report gives vendor-reported recall figures of 90.3% for synthetic overeagerness cases, 99.3% for synthetic prompt-injection cases and 96.1% for MonitoringBench [Hard]. These figures are evaluations and an internal snapshot, not independent evidence or a guarantee for your environment; the report says ratios depend on the use case, environment and sandbox configuration.
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OpenAI also warns that Auto-review is not a security guarantee and reports red-team cases in which reviewers could be misled into approving commands. Anthropic similarly says Claude Code Auto mode can miss risk when intent or context is ambiguous, can block benign actions and does not eliminate risk. Use automatic review only after deciding which residual error your organization accepts, and retain a human gate for operations where a false approval is unacceptable.
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Logging, investigation and recovery
Events worth retaining
- Identity of the user, agent, workspace and execution environment.
- Prompt or task identifier and the model-generated plan where available.
- Tool name, arguments, policy result and reviewer identity or timestamp.
- Tool output, exit status and files changed.
- MCP server and method usage.
- Network-proxy allow and deny decisions, including destination.
- Sandbox violations, credential-broker events and retries after denial.
OpenAI describes exporting prompts, approval decisions, tool results, MCP usage and network-proxy allow or deny events through OpenTelemetry in its Codex deployment discussion. Exporting events does not make a configuration safe by itself; it makes decisions inspectable after a failure or dispute.
When an agent behaves unexpectedly
- Stop the run and revoke or rotate credentials that may have been exposed.
- Preserve the event trail, workspace image and relevant tool outputs before cleanup.
- Check whether the action was authorized by permissions, allowed by the sandbox and approved under the configured policy.
- Inspect prompt content, retrieved files and tool results for injection or untrusted instructions.
- Strengthen the narrowest failed layer: remove a tool, tighten a mount or egress rule, or change the operation to
always_ask. - Replay the workflow in a disposable environment with synthetic data before restoring access.
Common configuration mistakes
“The model promised not to touch production”
A promise does not prevent a tool call. Remove production tools, isolate production credentials and enforce the boundary in the environment.
“We turned on auto approval to eliminate interruptions”
Automatic evaluation can allow a call before a human sees it. Keep consequential tools on an always-ask policy and reserve automatic handling for bounded, reversible work.
“The secret is hidden in an environment variable”
Agent-written code in the same environment can often read environment variables. Use a credential broker or proxy and keep high-value keys outside the sandbox.
“A deny in the interface is enough”
If the process can reach the resource through another tool, shell command or network route, a single UI denial may not enforce the intended policy. Apply controls at every path to the resource.
“One sandbox is adequate for every customer”
Shared workspaces and caches can create cross-customer exposure. Isolate jobs whose data should not be shared and reset state between trust domains.
Policy checklist for a production rollout
- Every enabled tool has a named owner, purpose and least-privilege scope.
- Read and write paths are explicitly defined; sensitive host directories are not mounted.
- Outbound network access is disabled or allowlisted, with proxy decisions logged.
- Application and third-party credentials are brokered or kept outside agent-written code.
- Production, destructive, external-communication and data-export actions require human approval.
- Capabilities that must never run are disabled or technically denied, not merely routed to review.
- Automatic review is documented as fallible, with an owner for residual risk.
- Prompts, approvals, tool results, MCP activity and network decisions are retained for investigation.
- Denial, timeout, prompt-injection and suspected credential-exposure scenarios are tested in disposable environments.
- Product-specific defaults are verified against the current vendor documentation before deployment.
Scope of the vendor examples
The OpenAI and Anthropic behaviors above are documented examples, not a platform-neutral security standard or proof that a particular setup is secure. OpenAI’s Agent Security help page describes a global policy baseline plus local and Codex Cloud controls: a local or Codex Cloud environment without an override inherits the applicable global policy. It also says Codex Cloud settings do not automatically apply to Work Cloud, and local MDM and legacy managed-device requirements take priority over Agent Security. Keep those inheritance rules scoped to that documented OpenAI workspace policy system.
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