The reliable way to stop a runaway AI agent is to make every consequential action pass through independent controls. Give the agent a unique, short-lived identity with only the tools, data, and operations needed for its current task; recheck authorization immediately before each tool call; require fresh human approval for irreversible, high-impact, sensitive, or externally visible actions; enforce hard limits on steps, retries, time, tokens, rate, and cost; isolate code and network access; and keep a tested pause, revocation, and shutdown path.
A plan generated by a model is not an authorization decision. Approval at the start of a session is not approval for a later delete, payment, message, deployment, or permission change. The execution layer must independently validate the exact actor, action, target, parameters, data classification, and approval state before anything runs.
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What a runaway AI agent looks like
An agent has run away when its execution continues beyond the intended task, scope, or risk level. The cause may be a recursive tool loop, a prompt-injection instruction hidden in a document, a broad service credential, a faulty retry policy, or a tool that can make changes without a separate authorization check. The model does not need malicious intent for the result to be dangerous: a misunderstood instruction can send data, alter infrastructure, spend money, or delete records.
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Stopping that behavior does not mean trying to make the model perfectly obedient. It means ensuring that a bad proposal cannot become a high-impact action without controls outside the model. The agent may suggest an operation, but a policy service or execution component decides whether the operation is in scope, whether the identity has the required privilege, whether approval is required, and whether the run has exceeded its limits.
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Start with an action-risk matrix
Before assigning permissions, inventory what each agent can do and classify every operation. Judge an action by more than whether it is technically a read or a write. Reversibility, data sensitivity, financial impact, external visibility, production blast radius, and the ability to affect permissions all matter.
| Action class | Examples | Default control | Approval treatment |
|---|---|---|---|
| Low-risk inspection | Read public documentation, inspect non-sensitive status, calculate a result without changing a system | Allow only the required read tool, data source, rate, and time budget | Policy decision may be automatic when the target and data class are within scope |
| Restricted read | Query private customer, employee, financial, or security data | Limit fields, records, tenant, purpose, and retention; log the query | Require a gate when policy or data classification marks the access as sensitive |
| Reversible internal change | Create a draft, open a ticket, update a non-production record | Constrain the exact resource and allowed parameters; provide an undo path | Approval depends on sensitivity and blast radius; do not rely on a broad session approval |
| Externally visible action | Send an email or message, publish content, contact a customer, or submit a form | Show the destination, content, attachments, and identity that will send it | Require a fresh, action-specific human decision |
| Irreversible or high-impact change | Delete data, make a payment, deploy to production, change security configuration, or alter roles and permissions | Default deny until the exact operation is reviewed; use narrow parameters and an expiry | Require explicit approval immediately before execution |
OWASP guidance recommends restricting an agent to the minimum tool set and requiring approval for security-relevant configuration, permission, and infrastructure changes. A useful policy rule is deny by default: an operation is permitted only when the matrix has an explicit allow decision for that agent, task, resource, and environment.
Give every agent a distinct, temporary identity
A shared service account makes accountability and containment difficult. If several agents use one credential, the policy layer cannot reliably distinguish which run requested an action, and revoking one run can disrupt every other run. It also creates confused-deputy risk: an agent may be tricked into using the authority of the application or user who started it.
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For each deployed agent, maintain an identity record containing:
- A unique agent identifier and the human or team that owns it.
- The business purpose and approved workflow.
- The environment in which it may operate, such as a test tenant or a production service.
- The allowed tools, operations, resources, data classifications, and destinations.
- The initiating user or workload identity for each run.
- An expiration time and the process that renews or retires the identity.
Microsoft identity guidance describes the goal this way: every user, agent, workload, plugin, and tool receives a verified identity, explicit authorization, and only the rights required. Treat that as a per-agent, per-tool, per-resource, and per-task requirement, not as a one-time application setting.
Prefer just-in-time credentials
Use short-lived credentials issued for a particular run or action whenever the platform allows it. Bind the credential to the initiating user, agent identifier, tool, operation, target resource, and data classification. A credential that can be used for unrelated tasks or remains valid after a run ends increases the blast radius of both a model error and a stolen token.
Keep credential issuance separate from the model. The model can request an operation, but it must not mint broader credentials, edit its own policy, or decide that a denied scope is acceptable. Permission changes should be treated as high-risk operations and sent through the same approval and audit path as other security-sensitive actions.
Reauthorize every tool call at the execution boundary
Authorization must be checked at the point where a tool is about to run. Checking only when a session starts leaves a gap: the agent may later change the target, expand the parameters, follow hostile instructions in retrieved content, or chain several individually small actions into a damaging result.
- Receive the proposed call. Capture the initiating user, agent identity, workflow, tool name, operation, target, parameters, data classification, and correlation ID.
- Canonicalize the request. Resolve aliases, redirects, resource identifiers, destinations, and parameter defaults before evaluating policy. The value that is approved must be the value that will execute.
- Check scope and privilege independently. A policy service verifies the identity, environment, resource, operation, data class, and current run budget. Tool output, retrieved documents, web pages, email, and other agents are inputs, not policy instructions.
- Evaluate the approval rule. If the action is in a class requiring human review, pause the run and create an approval request containing the exact operation and its expected impact.
- Bind any approval to the request. An approval should expire and be usable only for the same agent, tool, operation, target, parameters, and relevant data scope. If any material value changes, create a new request.
- Execute through a constrained adapter. The adapter enforces the approved parameters, network destination, data fields, and time limit rather than passing unrestricted model output to the underlying system.
- Write the result to the audit trail. Record the authorization result, approval decision, execution outcome, and any follow-up calls under the same correlation ID.
proposed_call = agent.propose(tool, operation, target, parameters)
request = canonicalize(proposed_call)
policy = policy_service.evaluate(identity, user, request, run_state)
if policy.denied:
log(request, policy, decision="denied")
stop_or_quarantine(run)
elif policy.requires_approval:
approval = approval_service.wait_for_fresh_decision(request, expires=policy.expiry)
if not approval.matches(request):
stop_or_quarantine(run)
else:
approval = None
execution_adapter.run(request, identity, approval)
log(request, policy, approval, outcome)
This separation is central to OWASP’s agent-security guidance: the agent can propose an action, but an independent policy or execution component must validate scope, privilege, and approval state before execution.
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Put approval gates where risk becomes real
An approval gate should interrupt execution immediately before the consequential operation, not merely approve a plan at the beginning of a conversation. The reviewer needs enough context to make a decision without reconstructing the agent’s entire chain of thought.
Show the complete proposed operation
The approval screen or request should identify:
- The agent, initiating user, owner, environment, and workflow.
- The tool and operation that will run.
- The exact target, destination, records, files, or infrastructure objects.
- Parameters, data fields, attachments, commands, and estimated scope.
- Why the operation is needed and what the expected impact is.
- Whether the operation is reversible, and how it can be undone.
- When the approval expires and whether the request changed since it was created.
Do not hide material values behind a generic label such as “continue automation.” A person who approves “send message” should see the recipients and content; a person who approves “change infrastructure” should see the resource, environment, and proposed settings.
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Fresh approval is appropriate for writes that affect sensitive data, deletes, payments, external communications, production changes, role or permission changes, security configuration, and any operation that cannot be reliably reversed. A prior approval can remain valid only for the exact bounded action it describes and only until its stated expiry. If the agent changes the target, amount, destination, parameters, or data scope, treat it as a new action.
Make rejection safe
A rejection should stop the specific operation and prevent the agent from repeatedly reissuing it. After repeated denials, tool errors, or attempts to vary parameters around the gate, quarantine the run and alert the owner. Do not let the model reinterpret a rejection as a request to search for another credential, tool, or route.
Install hard brakes for runaway loops
Every run needs finite budgets. Limits should be enforced by the orchestration and execution layers, not suggested to the model as a behavioral preference.
| Limit | What it bounds | When to stop or quarantine |
|---|---|---|
| Plan steps | Total tool actions or workflow transitions in one run | When the maximum is reached, require a new run or human review |
| Recursion and chain depth | Nested calls, sub-agents, and agent-to-agent delegation | Stop before an unbounded chain can form |
| Retries | Repeated attempts after errors, timeouts, or denials | Quarantine after the configured retry ceiling, especially when parameters change |
| Token and context budget | Model consumption and the amount of retrieved or generated content | End or pause the run before a growing context causes uncontrolled work |
| Elapsed time | How long a run may remain active or hold credentials | Expire the run and its temporary authorization |
| Request rate | Calls to a tool, endpoint, tenant, or external destination | Throttle or stop bursts and unusual repetition |
| Cost and spend | Paid model calls, transactions, or resource creation | Deny further activity at the budget boundary and notify the owner |
OWASP identifies chain-depth, retry, token, and cost limits as controls against recursive tool abuse and runaway loops. Choose ceilings for each workflow rather than one universal value: a reporting agent may need many read calls, while a payment or deployment agent should have a much smaller action budget. The limits must also cover delegated agents and background jobs, not only the visible chat session.
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Contain code, data, memory, and network egress
Sandbox code execution
Run generated code in an isolated environment with no implicit access to host files, production credentials, or unrestricted processes. Give the sandbox only the files, libraries, CPU, memory, and execution time required for the task. A code tool that can reach the host or invoke arbitrary commands can bypass otherwise careful application-level approval.
Restrict outbound destinations
Allow network egress only to destinations required by the approved workflow. Enforce the allowlist outside the model, and recheck redirects, resolved hosts, ports, and upload targets. A request to fetch a page is not permission to send private data to every endpoint that page mentions. Unexpected destinations, unusual upload volume, or a sudden change in host should stop or quarantine the run.
Isolate memory and retrieval
Separate memory by user, tenant, environment, and purpose. Apply access control and encryption to memory stores, and define retention rather than keeping every interaction indefinitely. A memory record should not silently become a permanent credential or a cross-tenant source of instructions.
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Treat web pages, documents, emails, retrieved text, tool responses, and messages from other agents as untrusted input. They may contain prompt-injection text that attempts to override policy, reveal secrets, or persuade the agent to widen its permissions. The policy engine must ignore such instructions unless an authorized human and system policy explicitly approve the resulting action.
Make the action chain auditable
Logs must allow an investigator to reconstruct what happened without relying on the model’s narrative. Record at least:
- Agent identity, owner, initiating user, workflow, environment, and credential or token identifier.
- Timestamp, correlation ID, parent run, delegated agent, and sequence number.
- Tool, operation, canonical target, parameters, data classification, and destination.
- Relevant input and output references, with secrets and sensitive values redacted according to policy.
- Authorization result, policy version, approval request, approver, decision, expiry, and reason.
- Execution result, error, retry count, rate-limit event, budget state, and shutdown or quarantine event.
Protect these logs from alteration by the agent and make them available to the people responsible for incident response. Correlation IDs should follow delegated calls so that a seemingly harmless sub-agent action can be connected to the initiating request.
Monitor for signals of drift or abuse
Alert on unusual destinations, expanded parameters, access to a new data class, privilege changes, repeated denials, repeated tool errors, attempts to call unavailable tools, unusual query patterns, and activity outside the normal schedule or environment. A single signal may be benign; a cluster of signals should trigger throttling, quarantine, or human review.
Monitoring is not a substitute for a gate. It is a detection and response layer that helps identify a policy gap or a compromised credential after the preventive checks have done their work.
Build a real pause and shutdown path
The stop mechanism must operate below the agent process. If the model can ignore a pause message, the pause is not a control.
Design the control points
- Global pause: prevent new runs and block execution adapters for the affected agent, tenant, environment, or workflow.
- Run cancellation: terminate the current orchestration and revoke its temporary authorization.
- Credential revocation: invalidate access tokens, rotate secrets, and disable service identities when compromise is suspected.
- Queue and job cancellation: remove pending tool calls and stop background workers, scheduled tasks, and delegated agents.
- Network containment: apply the egress block or isolate the sandbox if data exfiltration or hostile destinations are suspected.
- Recovery and restart: preserve logs, assess changes, correct the policy, and restart only with a new bounded run.
Use an incident runbook
- Invoke the system-level pause for the smallest scope that contains the risk; use a broader pause if the boundary is uncertain.
- Cancel active runs and invalidate their short-lived credentials and approval tokens.
- Block affected destinations and preserve the complete action log, including failed and denied attempts.
- Identify changes already made, data accessed or sent, and downstream jobs started by the agent.
- Rotate or revoke any credential that may have been exposed, and review permission changes.
- Fix the triggering policy, tool adapter, prompt, connector, or workflow before resuming.
- Restart in a restricted environment with a new correlation ID and heightened monitoring.
Test this procedure before production. A shutdown path that has never been exercised may fail because a worker ignores cancellation, a token remains valid, a queue continues dispatching, or a delegated agent is outside the pause boundary.
A practical implementation sequence
Teams can implement the controls in an order that reduces immediate exposure while building toward stronger governance.
- Inventory the estate. List every model, agent, tool, plugin, connector, memory store, data source, queue, scheduled job, and execution environment. Include hidden integrations and delegated agents.
- Assign ownership. Give each agent a named owner, purpose, environment, unique identity, and expiration. Remove abandoned identities and credentials.
- Default-deny access. Start with no tools or data, then add only the operations required by a documented task. Scope access to specific resources and fields.
- Define the risk matrix. Mark read, write, delete, external, financial, production, sensitive-data, and permission-changing operations, along with reversibility and blast radius.
- Insert the policy service. Put an independent authorization check immediately before every tool action. Ensure the tool adapter cannot be called through an ungoverned path.
- Add approval workflows. Build an approval request that displays the exact target, parameters, data, impact, expiry, and identity. Bind the decision to the canonical request.
- Set run budgets. Enforce step, recursion, retry, token, time, rate, and cost ceilings, including delegated work and background activity.
- Contain execution. Sandbox code, isolate memory, encrypt and control access to stored context, and restrict outbound network destinations.
- Instrument and alert. Capture complete action context and create alerts for denials, bypass attempts, privilege changes, unusual destinations, and abnormal query patterns.
- Rehearse recovery. Test pause, cancellation, credential revocation, token invalidation, rotation, decommissioning, and restart. Document who can invoke each control.
- Review on every material change. Reassess permissions and gates whenever prompts, tools, memory, models, connectors, or workflows change materially.
Common designs that fail
One approval at session start
Why it fails: The approved plan can evolve, retrieved content can alter the next step, and later calls may have different targets or impact.
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Fix: Reauthorize each tool call and require a fresh decision for every high-impact or irreversible operation.
A broad service account behind a friendly interface
Why it fails: The interface may look constrained while the underlying credential can read, write, delete, or administer unrelated resources.
Fix: Issue task-scoped identities and permissions tied to the exact agent, user, tool, resource, and data class.
The model is allowed to approve itself
Why it fails: A prompt, tool response, or hostile document can persuade the model that a denied action is safe or already approved.
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Fix: Keep policy and approval state in an independent service, and accept approval only when it matches the canonical request and has not expired.
Client-side confirmation without server enforcement
Why it fails: Another code path, background worker, or direct connector can bypass the user interface.
Fix: Enforce authorization in the execution service or tool adapter immediately before the operation.
Unlimited retries and delegation
Why it fails: A transient error can turn into a high-volume loop, while sub-agents multiply calls outside the visible conversation.
Fix: Bound retries, recursion, chain depth, rate, time, tokens, steps, and cost, and include delegated agents in the same budgets and logs.
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Logging only the final answer
Why it fails: A final response does not show which records were read, which parameters changed, which policy decisions were made, or whether a tool failed repeatedly.
Fix: Log the complete action chain, authorization and approval state, inputs and outputs, and execution outcomes with correlation IDs.
A kill switch that only sends a message to the model
Why it fails: The model may be stuck, disconnected, or already holding a valid credential and queued work.
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How to evaluate an agent-control design
When comparing implementations, assess the controls rather than the model’s claims about safety.
| Evaluation axis | Questions to ask |
|---|---|
| Identity and permission granularity | Does every agent have a verifiable owner, purpose, environment, expiration, and task-scoped permission set? |
| Per-action authorization | Is scope and privilege checked immediately before each tool call, including delegated and background calls? |
| Approval coverage and UX | Does the reviewer see the exact target, parameters, data, impact, identity, and expiry? Are deletes, sends, payments, deployments, and permission changes gated? |
| Runaway-loop controls | Are chain depth, retries, steps, tokens, time, rate, and cost enforced outside the model? |
| Sandbox and egress isolation | Can generated code reach the host, production credentials, arbitrary processes, or unapproved destinations? |
| Audit completeness | Can an investigator reconstruct every proposal, policy decision, approval, execution result, and delegated call? |
| Pause and revocation reliability | Can operators stop active and queued work, revoke credentials, invalidate tokens, and contain network access quickly? |
| Lifecycle ownership | Who renews or retires the identity, reviews permissions, responds to alerts, and approves material workflow changes? |
Keep controls aligned as the system changes
Agent permissions are never finished because the surrounding system changes. A new connector may expose a different data class; a prompt revision may cause more tool calls; a model update may alter parameter formatting; a memory feature may retain information longer; and a workflow change may turn a reversible draft into an external send.
Set a review trigger for every material change to prompts, models, tools, plugins, connectors, memory stores, destinations, data sources, or workflows. Re-run the action-risk matrix, verify that the execution adapter still enforces canonical parameters, test approval expiry, and exercise the shutdown path. Retire identities and credentials when an agent is decommissioned rather than leaving dormant access available.
The governing principle is simple: let the agent reason within a narrow workspace, but make the system—not the agent—the final authority over tools, data, side effects, and continued execution.
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