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Prevent an AI agent from making unauthorized changes by controlling what it can do outside the model: give it a separate identity with task-scoped permissions, start it in read-only mode, confine it to a narrow workspace, and require independent approval for high-impact actions. Add network and credential limits, external audit logs, alerts, and a tested way to revoke access. A prompt telling an agent not to delete or overwrite files is not an access control.
What does it take to prevent unauthorized changes?
Use several controls together rather than relying on a single safeguard. The agent should have only the permissions needed for its current task; an external system should enforce those permissions; and a human should approve actions whose consequences are difficult to reverse. Sandboxing, restricted network access, monitoring, and a tested shutdown and recovery process limit the damage if another control fails.
This distinction matters because an agent can act through tools: it may read or write files, run commands, call APIs, or use connected services. Instructions in a prompt describe the intended behavior, but access controls determine what the agent is able to do. The U.S. Department of Energy’s GEAR guidance puts it plainly: a prompt such as “never delete files” is not an access control.
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How should you set up an agent’s identity and permissions?
Give each agent a distinct, auditable identity and grant only the task-specific access it needs. Begin with read-only access, then add narrowly scoped write or execution permissions only when the task requires them.
Use a dedicated identity and named owner
Do not let an agent quietly inherit a person’s broad account permissions. Assign it an identity that can be traced to a named owner and a defined purpose. If multiple agents or workflows share one identity, it becomes harder to determine which activity caused a change and harder to revoke one workflow without disrupting others.
Scope access to the task, not the job title
A role such as “developer” or “administrator” can cover far more than a particular task requires. Prefer explicit permissions for the relevant workspace, files, commands, tools, data sources, and downstream systems. For example, an agent asked to review code may need to read a repository and produce a proposed patch, but not push a branch, merge it, alter permissions, or deploy it.
Review effective permissions beyond the agent’s own interface. A tool may let the agent reach other systems using its identity or a connected service’s access. Check those downstream permissions too; a narrow-looking agent setting does not help if a connected API token can modify production data.
Prefer short-lived credentials and quick revocation
Use ephemeral or short-lived credentials where available, and avoid embedding long-lived secrets in the agent’s environment. If elevated access is temporarily necessary, make it task-specific and revoke it as soon as the elevation is no longer needed. Microsoft’s identity guidance emphasizes unique identities, task-based scopes, allowlists, and tested revocation.
How can you make an AI agent read-only?
Make read-only behavior an enforced permission boundary, not just a setting in the agent’s prompt or interface. Remove write access to files and connected services, block state-changing commands or API operations, and verify the effective permissions from the systems the agent can reach.
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- Identify every route to a change. List the agent’s file tools, shell or command runner, APIs, plugins, connected applications, and any MCP servers. Include indirect routes such as a tool that can write to a repository or modify a cloud resource.
- Grant read access only to the necessary scope. Limit access to the specific workspace or data needed for the task. Do not mount an entire home directory or expose unrelated repositories and production data.
- Enforce read-only access outside the model. Use operating-system permissions, application roles, API authorization, or another policy layer that denies writes. A user-interface toggle is not sufficient unless the underlying tools and services enforce it.
- Test the boundary. In a safe environment, verify that attempted writes, deletions, permission changes, and state-changing API calls are denied. Confirm that the agent cannot use another connected tool to achieve the same change indirectly.
- Keep the task read-only unless a change is needed. If the work later requires edits, approve only the necessary workspace and actions rather than converting the identity into a broad writer.
OWASP’s DevSecOps guidance recommends starting from deny and explicitly allowing needed actions. That approach is easier to audit than granting broad access and trying to remove dangerous capabilities afterward.
What should a coding agent be allowed to do?
Give a coding agent the smallest set of repository, command, and network permissions needed for its assignment. Separate producing a proposed change from applying, pushing, merging, or deploying that change.
| Task stage | Reasonable access pattern | Keep outside the agent’s authority |
|---|---|---|
| Inspect or explain a codebase | Read-only access to the relevant repository and documentation | Writes, pushes, merges, deployments, and unrelated data access |
| Prepare a code change | Write access to a narrow workspace or disposable branch where changes can be reviewed | Production credentials and permission to merge or deploy its own work |
| Run tests or build tools | Allowlisted commands in an isolated environment, with only the required dependencies | Unrestricted shell access, privileged commands, or arbitrary software installation |
| Publish or deploy a change | Agent may prepare the proposed action and show the exact command and arguments | Independent approval and the final irreversible or high-impact action |
The table is a starting point, not a universal role definition: a particular task may need less access, and any additional access should have a specific purpose. Keep production credentials out of development and review environments unless they are essential and separately controlled.
Use an isolated development container, disposable virtual machine, or hosted isolation boundary for code execution. Check what the boundary actually covers: shell commands, file-access tools, and MCP servers may not all be confined in the same way. A sandbox should not be assumed to block network access or protect resources it can still reach.
Where should authorization decisions be enforced?
Enforce allow and deny decisions in the operating system, application, API gateway, scheduler, database, or policy engine—not only in the prompt. If an approval service or reviewer is unavailable, fail closed: pause the action rather than treating the missing decision as permission.
A prompt can guide the agent to ask before acting, but it cannot reliably prevent a tool call when the tool itself accepts the request. External authorization lets the system reject a prohibited operation even if the model misunderstands its instructions or is influenced by untrusted content. OpenAI’s cybersecurity guidance likewise calls for denying unauthorized actions, pausing ambiguous or high-risk changes for human approval, enforcing independent filesystem and network boundaries, keeping audit logs, and failing closed when review is unavailable.
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Apply policy at each boundary where a change can occur. For instance, filesystem permissions can restrict writes, repository settings can govern pushes and merges, and application or API permissions can limit changes to connected services. A policy that blocks a command in one interface is incomplete if another tool can invoke the same capability.
Which actions should require a human approval gate?
Require an independent human decision before actions that are irreversible, high-impact, or difficult to audit and undo. The reviewer should see the exact proposed action, including the command and its arguments, and should not be the agent approving its own work.
- Pushes, merges, releases, and deployments
- Deletion, overwriting, bulk edits, or other destructive changes
- Changes to permissions, identities, or security policy
- Spending money or initiating a financial commitment
- Sending messages or other communications on someone’s behalf
- Installing software, resetting systems, or changing their configuration
- Connecting to a new network destination or expanding access to a new data source
Make approval meaningful: show what resource will change, what the agent intends to do, and the exact command or operation. Do not treat a vague confirmation such as “continue?” as informed authorization. If the action differs from what was reviewed, require a new decision.
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Can prompt injection or a poisoned tool bypass the rules?
Untrusted content can try to manipulate an agent into taking actions its operator did not intend. Treat content the agent reads—including issues, pull requests, web pages, logs, dependency files, instruction files, and tool descriptions—as data, not as a source of authority.
Prompt injection is not solved by adding another sentence to the prompt. The most dependable defense is to keep the agent’s permissions narrow and enforce them independently, so that manipulation cannot grant capabilities the agent does not already have. If a malicious instruction asks for a file deletion, the external policy should still deny it unless that action has been explicitly authorized.
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Review and pin MCP servers and instruction files rather than accepting changes to them automatically. Limit what tools can do and what destinations they can contact. These precautions reduce the chance that a compromised or misleading tool changes the rules or expands the agent’s reach.
How do sandboxing and network restrictions limit damage?
Run the agent in a dev container, disposable virtual machine, or hosted isolation boundary with only the workspace and resources needed for the task. Restrict outbound connections to an allowlist, and verify which execution paths the sandbox actually contains.
A workspace sandbox can reduce the impact of an accidental edit, but it is not automatically a complete boundary. The agent may have separate file tools, connected services, or MCP servers; those paths need their own access controls. Likewise, isolating shell commands does not prove that an agent cannot make a network request from another tool.
- Do not mount an entire home directory when a task needs only one workspace.
- Keep production secrets out of the environment and avoid exposing long-lived credentials.
- Allow network access only to destinations needed for the task; review requests to add a destination.
- Pin and review tool and dependency versions so changes are deliberate rather than silently introduced.
- Check that the boundary applies to every tool the agent can invoke, not just its primary execution environment.
Isolation contains risk; it does not replace authorization. The agent still needs explicit permissions, and high-impact actions still need a review gate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you log and monitor?
Keep an audit trail outside the agent’s control that connects each action to an identity, user, session, approval, and resulting change. Record the tool call and arguments, the resource accessed, the network destination where relevant, and the resulting diff, while avoiding logging secret values.
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- Attempts to read credential files or access unexpected secrets
- Connections to destinations outside the approved network list
- Unusually broad or bulk reads of data
- Changes in effective permissions or unexpected privilege increases
- Edits to instruction files, tool definitions, or other policy-relevant configuration
- Attempts to disable monitoring or delete audit records
Audit logs help establish what happened; they do not themselves prevent a change. Pair them with enforcement and approvals so that detection is not the only line of defense.
How should you roll out these controls?
Build the controls in stages, beginning with an inventory and a read-only trial. Add narrowly scoped write access only for a demonstrated need, then test approval, isolation, monitoring, and revocation before relying on the workflow.
- Inventory the workflow. List every agent, model, tool, plugin, data source, owner, and downstream permission. Include connected systems and indirect routes to modify data.
- Write an action policy. Classify actions as read, write, destructive, network, or administrative. State what is allowed, what requires human approval, and what is denied.
- Start read-only. Use a narrow workspace and no production credentials. Verify that the agent can complete safe tasks without access beyond that scope.
- Allowlist necessary actions. Add only the files, commands, tools, data, and destinations the task requires. Keep high-impact categories behind an independent approval gate.
- Isolate execution. Use a suitable sandbox, restrict egress, and pin and review tool and dependency versions. Check coverage for shell, file tools, and MCP servers.
- Centralize logs and alerts. Send audit records to a system outside the agent’s control and configure alerts for suspicious access, permission drift, unexpected destinations, and logging tampering.
- Exercise revocation and recovery. Test disabling the agent, rotating credentials, invalidating tokens, and removing stale permissions. Confirm that the process works before an incident.
- Re-review after changes. Reassess permissions and boundaries when the workflow, tools, data scope, or execution environment changes.
How do the main safeguards compare?
No single safeguard covers every failure mode. The useful comparison is what each control actually enforces and what remains exposed if it fails.
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|---|---|---|
| Prompt instruction | Communicates intended behavior to the model | External authorization, because instructions alone do not block tool actions |
| Task-scoped allowlist | Limits the files, commands, tools, data, and destinations available | Review of downstream permissions and approval for consequential actions |
| Workspace sandbox | Contains activity within a defined environment | Controls for tools, services, or network paths outside that boundary |
| Human approval | Provides a separate decision before designated high-impact actions | Least privilege, logging, and enforcement for actions that do not require approval |
| External audit logging | Creates evidence for investigation and anomaly detection | Prevention; logs should be paired with access controls and alerts |
| Short-lived credentials and revocation | Limits how long access persists and supports containment | Testing that every token and downstream permission can actually be disabled |
Operational overhead rises as access is divided into narrower scopes and more actions require review. That cost is usually most important for workflows that touch sensitive data or systems where a mistaken change is expensive; the policy should match the task’s risk instead of granting broad access for convenience.
Frequently Asked Questions
What should I do if an AI agent has already changed files without permission?
Pause the workflow and disable its access before making further changes. Preserve the audit trail, inspect the affected files and resulting diff, then restore from a known-good version or follow your normal recovery process; if credentials may have been exposed, rotate them and invalidate related tokens.
Should an AI agent use my personal account to access work tools?
Prefer a dedicated identity tied to a named owner and task. A personal account can carry unrelated permissions and makes it harder to distinguish the agent’s activity from yours during review or revocation.
Do approval gates make an agent safe to use with production data?
No. Approval addresses selected actions, but it does not eliminate unnecessary access or contain every mistake. Keep production permissions out of the workflow unless they are essential, and apply external authorization, isolation, and logging as well.
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What is the difference between an agent’s audit log and a change history?
An audit log should show the identity, session, tool call, arguments, resource, and approval associated with activity; a change history shows the resulting state or diff. Keeping both makes it easier to connect an action to its outcome without putting secret values in the record.
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