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Secure an AI agent as an untrusted, task-specific process—not as a coworker with your account. Start with no permissions, issue a separate short-lived identity, allow only the named tools and resources needed for one task, run code inside a constrained sandbox, and require an independent human approval before any action that is difficult to reverse. Authorization must be enforced by infrastructure outside the model, while centralized logging and a kill switch provide detection and recovery.
What a secure agent configuration must accomplish
A tool-using agent can read files, execute programs, call APIs, send messages, and alter systems. Its natural-language plan is not an authorization boundary: a prompt injection, compromised retrieval document, model error, or malicious tool result can cause it to request an action you never intended. Treat every model output as an untrusted request that must pass external policy checks.
A practical control model has six parts:
- Attributable identity: a service account or bot identity unique to the agent or workload, linked to the human who started the session.
- Task-scoped permissions: an empty default allowlist expanded only for the current task, resource, operation, and time window.
- External enforcement: a policy decision point, API gateway, service mesh, or tool-execution proxy that can deny requests without asking the model to police itself.
- Isolation: filesystem, process, network, package, resource, and syscall limits around the agent and any code it runs.
- Independent approval: a human checkpoint at irreversible or high-impact boundaries.
- Evidence and recovery: tamper-resistant logs, anomaly alerts, credential revocation, pause controls, and rollback procedures.
This is the “least agency” approach described in OWASP guidance: grant the minimum ability to act, for the shortest practical period, and make every grant revocable.
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Give the agent its own identity
Create a service account, workload identity, or bot principal for each agent or workload. Do not run it under a developer’s personal account. Bind every request to both the initiating human and the agent identity, so an audit can answer who asked, which agent acted, and what it attempted.
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Separate identities by environment and purpose. A research agent might have read-only access to a documentation index; a deployment agent should not inherit that identity or its credentials. Keep development, staging, and production principals distinct so a test session cannot silently reach production.
Start with an empty allowlist
Deny by default. For each task, name the permitted tool, operation, resource, and expiry. “Read the issue tracker” is narrower than “access the internet”; “read these three repository paths” is narrower than “read the workspace.” Include an explicit end time or session limit, then remove the grant when the task ends.
| Control dimension | Safer setting | Why it matters |
|---|---|---|
| Tools | Only the named search, file-read, or API tools required for the task | Limits the number of paths a prompt injection can exploit |
| Operations | Read operations first; add one write operation only when justified | Prevents a read-only task from becoming a modification task |
| Resources | Specific repositories, records, directories, or API objects | Stops broad discovery and bulk collection |
| Time | Short-lived grant with an automatic expiry | Reduces the window for stolen tokens or runaway loops |
| Environment | Separate identities and policies for development, staging, and production | Prevents accidental cross-environment changes |
Default-deny policies should explicitly block secrets such as .env files, credential stores, SSH keys, and token caches. Also deny unrestricted shells, arbitrary package installation, unrestricted network egress, production writes, and permission changes unless a separate reviewed policy allows a specific exception.
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Use read-only API scopes and filesystem mounts wherever the task permits. If a write is necessary, expose the smallest operation—for example, “create a draft” rather than “publish,” or “update this record” rather than “edit any record.” Pair a write with a narrow resource selector and an approval requirement. A capability that can be revoked and whose result can be rolled back is safer than a broad, permanent privilege.
Enforce authorization outside the model
Put a policy decision point between the agent and every tool or sensitive resource. The enforcement layer should evaluate the human principal, agent identity, tool, requested operation, target resource, session context, and grant expiry. It then returns allow, deny, or approval-required. The model should never be able to change that policy, mint a broader token, or select a different enforcement path.
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An API gateway can check scopes before forwarding a request. A service mesh can enforce identity and destination rules for network calls. A tool proxy can validate command arguments, redact parameters, and require approval for selected operations. For local tools, a broker process can expose only approved functions instead of giving the model a general shell.
Keep policy evaluation deterministic and observable. Record the policy version and decision with each call. If a tool is unavailable, fail closed rather than silently falling back to an unrestricted alternative. Never treat a model statement such as “the user approved this” as evidence of approval; the approval must come from an independent control channel tied to one specific action.
Sandbox code and tool execution
NIST’s 2025 AI profile advises that arbitrary agent code execution should generally be curtailed, sandboxed, placed under approval and monitoring, or disallowed entirely. Apply that principle even when generated code appears harmless: dependencies, build scripts, and data files can contain side effects that are invisible in a short snippet.
Constrain the filesystem
Mount only the working directory or a task-specific copy. Make source and reference data read-only when possible, and provide a separate, disposable output directory for generated files. Block access to operating-system configuration, home-directory credential locations, device files, and other users’ data. Destroy the workspace after the session unless retention is explicitly required.
Constrain processes and system calls
Run the agent in an isolated process or container with a non-privileged user. Apply CPU, memory, wall-clock, and process-count limits to stop fork bombs and runaway loops. Restrict system calls to those required by the approved tools. Do not expose a host daemon socket or a privileged container escape path.
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Constrain network and packages
Default to no outbound network. If the task needs an API, allow only the documented destination and method through an egress proxy. A request to a new host must be denied or sent to human approval. Pin approved package sources and versions, or provide a prebuilt environment; arbitrary package installation should be treated as a high-risk capability because installation scripts can execute code.
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If the agent can answer through a narrow API, do not give it a shell. When execution is essential, use a disposable sandbox, monitor the process and network, and require approval for exceptional commands. Disable execution completely for agents that only classify, summarize, or retrieve information.
Put human approval at irreversible boundaries
Approval is most valuable where harm is hard to undo. OWASP specifically identifies pushing code, merging, deploying, deleting data, sending messages, spending money, changing permissions, and connecting to a new network destination as points where a human decision should remain.
| Action class | Default treatment | Approval details to show |
|---|---|---|
| Low-risk search or read | Allowlist after policy review | Tool, resource, query scope, and expiry |
| File or record write | Require approval unless the operation is narrowly pre-approved and reversible | Exact target, proposed diff, and expected effect |
| Code execution | Sandbox, monitor, and approve exceptional execution; disable when unnecessary | Command, arguments, working directory, image or runtime, and resource limits |
| External message or publication | Require an independent human approval | Recipients, content, attachments, and sending identity |
| Deletion, deployment, or financial action | Require approval for every operation | Objects or environment affected, amount or change set, and rollback plan |
| Permission change or new network destination | Deny by default; require explicit approval | New principal or destination, requested scope, and duration |
The reviewer must see the exact command and arguments, not a model-generated summary. Bind approval to one action, target, and parameter set; changing any of those should invalidate the approval. Use a separate approval channel or role so the agent cannot approve its own request. Set an expiration on pending approvals and deny if the context changes.
Handle credentials as temporary capabilities
Issue short-lived, task-scoped tokens through a secrets manager or an OIDC-style workload flow. Keep read-only and write identities separate. Rotate and revoke promptly when a session ends, a policy changes, or an anomaly appears. Never place long-lived production credentials in prompts, retrieved documents, source files, or a general agent environment.
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Deliver credentials only to the broker or tool that needs them, rather than exposing secret values to the model. Redact tokens from command output and logs. Test revocation: a kill switch should invalidate the agent’s credentials or pause its execution quickly enough to limit an incident.
Log and monitor outside the agent’s control
Send structured events to a central logging system that the agent cannot alter. For every tool call, capture the tool name, arguments (with secrets removed), agent identity, initiating user, session identifier, timestamp, policy decision, result, and resulting file or configuration diff. Record denials and approval requests as well as successful calls; denied activity often reveals probing or prompt injection.
Alert on credential-file access, bulk reads, unexpected destinations, attempts to invoke a new tool, changes to instruction or policy files, repeated denials, unusual retries, and sudden increases in tool-chain length or cost. Retain logs according to your organization’s policy and protect their integrity so an investigator can rely on the sequence of events.
Control retries, cost, and recursive behavior
Cap the number of retries, total spend, recursion depth, and tool-chain length for each session. A model that keeps retrying a denied request can create a denial-of-service condition or amplify an unintended action. Stop execution when the context changes materially, when an approval expires, or when the agent requests a capability outside its task grant. Requiring a fresh session for a new objective is safer than allowing an old authorization to accumulate privileges.
Implement the controls in a deployment sequence
- Define the task: write down the human requester, objective, permitted resources, allowed operations, expected outputs, and expiry.
- Create the identity: assign a dedicated agent principal and link it to the initiating human and environment.
- Build the policy: start with an empty allowlist; add named tools and read-only operations first, then document each exception.
- Insert enforcement: route all tool and API calls through a policy decision point, gateway, mesh, or broker that can deny independently of the model.
- Prepare the sandbox: isolate the process, mount only required paths, disable unnecessary system calls, restrict egress, and set CPU, memory, time, and process limits.
- Issue credentials: mint short-lived tokens for the approved scope, keep production write credentials separate, and verify automatic expiry.
- Configure approvals: mark writes, execution, external messages, spending, deletion, deployment, permission changes, and new destinations as approval-required.
- Test observability and recovery: confirm that logs contain identity, user, arguments, decisions, results, and diffs; then test the kill switch and rollback path.
- Run adversarial tests: use prompt-injection attempts, malicious tool output, path traversal, unauthorized destinations, and expired approvals to verify that infrastructure—not model obedience—blocks them.
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Replace the broad mount with a task-specific read-only mount and an explicit path allowlist. Block hidden credential files and verify that symlinks cannot escape the allowed directory.
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A prompt says approval was granted
Ignore the statement and check the external approval record. Require a matching action hash or equivalent binding to the exact command, target, and arguments.
The sandbox still reaches the internet
Disable default egress and route permitted calls through a destination allowlist. Log DNS and connection attempts, including denied destinations, and require approval for a new host.
Logs contain secrets
Redact at the broker before events leave the agent boundary. Use structured fields so tokens, cookies, private keys, and authorization headers can be detected and removed consistently.
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Approvals create fatigue
Do not approve broad categories such as “all repository writes.” Allowlist low-risk, reversible operations with narrow scopes, while retaining per-action approval for irreversible effects. Review the allowlist whenever tools, prompts, memory, retrieval sources, or models change.
How to compare agent-security architectures
When evaluating an implementation, compare the controls rather than the model’s claims about safety:
- Scope granularity: can policy limit a tool, operation, resource, user, and time window independently?
- Enforcement location: is authorization decided by infrastructure, or merely described in a prompt?
- Isolation strength: which filesystem, network, process, package, resource, and syscall boundaries are actually enforced?
- Approval quality: does the reviewer see exact commands and arguments, and is approval bound to one action?
- Auditability: are logs complete, centrally retained, tamper-resistant, and free of secret values?
- Revocation and recovery: can credentials be revoked, sessions paused, and changes rolled back quickly?
- Operational friction: can safe, low-risk actions be allowlisted without training users to approve everything automatically?
Re-test after any change to prompts, tools, memory, retrieval, model or provider, sandbox settings, or approval policy. Security is a property of the whole execution path, not of the model in isolation.
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