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The safest way to let an LLM use tools is to deny the model execution authority entirely. Treat the model as an untrusted planner: it interprets a request and emits a typed proposal, while an independent airlock enforces authorization, supplies narrowly scoped credentials, runs the approved operation in an isolated worker, and records what happened. The model must not be able to run shell commands, alter production, send messages, spend money, deploy code, delete records, or use standing credentials.
This design contains both ordinary model mistakes and prompt injection. A deterministic policy gateway, short-lived identities, disposable sandboxes, exact-action approvals, and tamper-resistant audit logs turn an LLM from an operator with broad reach into one component of a controlled change process.
What does “zero execution authority” mean for an LLM?
Zero execution authority means the model can suggest an action but cannot perform it or obtain the permissions needed to perform it. Every side effect must pass through services that are independent of the model and that can reject an action even when the model is persuasive, compromised, or wrong.
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What the model may do
- Classify a request and extract its intended actor, operation, target, parameters, expected effect, and uncertainty.
- Read approved context through read-only retrieval interfaces.
- Produce a dry-run plan, explain assumptions, and ask for missing information.
- Report the result returned by the execution system without altering the audit record.
What the model may never do directly
- Run a shell, SQL, cloud, deployment, or administrative command.
- Read or store standing secrets, private keys, session cookies, or production tokens.
- Choose a destination, recipient, amount, or permission change outside a policy decision.
- Bypass approval by rephrasing an action, invoking a different tool, or delegating to another agent.
The term “cognitive airlock OS” is an engineering synthesis rather than an established product category. Its useful meaning is an operating model in which cognition and execution are separated by enforceable boundaries.
Why is giving an LLM broad tool access dangerous?
Broad tool access turns an ambiguous prediction into a real side effect. OWASP defines excessive agency as “the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM.”
Prompt injection does not have to appear in the user’s message. A retrieved web page, ticket, document, source file, log line, tool description, or tool result can contain instructions that the model treats as authoritative. Once the model can invoke powerful tools, a malicious instruction can become data exfiltration, a destructive command, an unauthorized deployment, or a message sent to the wrong party.
Failure modes an airlock must assume
- Indirect prompt injection: untrusted content instructs the model to ignore the user’s goal or reveal data.
- Excessive agency: an otherwise plausible plan performs more actions than the user intended.
- Confused deputy: the model uses a privileged service identity for a less-trusted requester.
- Credential exposure: a secret appears in context, logs, a prompt, or a tool response.
- Tool poisoning: a tool’s name, schema, description, or output is manipulated to induce an unsafe call.
- Replay and looping: an old approval is reused, or an agent repeats an action until a limit is reached.
- Cascading failure: multiple agents amplify one mistaken instruction.
- Data exfiltration: sensitive content is sent to an unapproved external destination.
There is no authoritative prevalence statistic for prompt-injection incidents or agent failures that can justify a universal probability. The absence of a reliable rate is a reason to engineer for containment, not a reason to grant standing access.
How should a cognitive airlock be structured?
Use separate boundaries for planning, policy, credentials, execution, people, and audit. Each boundary should have its own identity and failure behavior; the model should not be able to collapse them into one tool call.
| Boundary | Responsibility | Required control |
|---|---|---|
| Planner | Translate intent into a typed proposal | Schema validation; no side effects; untrusted input treated as data |
| Policy | Decide whether the exact action is allowed | Deny by default; deterministic checks for identity, resource, environment, data class, spend, time, rate, and reversibility |
| Credential | Provide authority for one approved operation | Short-lived, task-scoped token; secrets stay outside model context |
| Execution | Perform the approved operation | Disposable worker; restricted filesystem and network; no ambient production credentials |
| Human | Approve high-impact or irreversible actions | Exact-action review showing target, parameters, effect, and expiry |
| Audit | Preserve evidence of decisions and outcomes | Central logging outside the agent’s control, with tamper resistance and retention rules |
What belongs in a typed proposal?
Require a machine-validated object rather than free-form prose. A minimal proposal contains an actor, operation, target, parameters, expected effect, uncertainty, requested environment, and an idempotency key. Reject unknown fields when they could hide an extra instruction, and reject unknown operations rather than trying to infer them.
{"actor":"support-bot","operation":"rotate_api_key","target":"staging/payment-service","parameters":{"key_id":"k-184"},"expected_effect":"old key disabled; replacement issued","environment":"staging","uncertainty":["rotation may restart two workers"],"idempotency_key":"8f2..."}The schema is not authorization. It only makes the request explicit enough for an independent policy engine to evaluate.
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How do you enforce authorization outside the model?
Put the authorization decision in a gateway that the model cannot modify, disable, or bypass. The gateway receives the typed proposal, resolves the requester’s identity and resource ownership, evaluates policy, and returns allow, deny, or approval-required.
The Department of Energy GEAR guidance captures the principle precisely: “A prompt such as ‘never delete files’ is not an access control.” A system prompt can guide behavior, but only an external enforcement point can make an operation impossible when the model ignores the instruction.
Checks a policy gateway should make
- Identity: who requested the action, which application is acting, and whether delegation is permitted.
- Resource: exact account, project, record, repository, host, or dataset.
- Environment: development, test, staging, or production.
- Data class: public, internal, confidential, regulated, or secret.
- Operation and parameters: verb, arguments, recipient, amount, and destination.
- Time and rate: expiry, frequency, concurrency, and cumulative quota.
- Reversibility: whether a transaction can be rolled back and how long rollback remains possible.
- Budget and blast radius: spend ceilings, record counts, hosts, regions, and dependency impact.
Policy should start from deny and allow explicitly, as OWASP recommends. A new tool, argument, environment, or destination must not become usable merely because it was added to a schema.
How should credentials be issued without exposing secrets to the LLM?
Keep secrets in a vault or identity service that the model cannot read. After policy approval, mint a short-lived credential bound to the approved operation, target, audience, and expiry; revoke it when the worker finishes or the approval expires.
Read and write identities
Use separate identities for retrieval and mutation. A read-only identity should not be able to escalate by calling a write endpoint, and a write identity should be limited to the smallest resource set and action needed. Never give an agent a general-purpose administrator token “for convenience.”
Bind tokens to the approved action
A token for “update one staging record” should fail if presented to production, a different record, a different endpoint, or a second operation. Include a nonce or idempotency key to prevent replay, and make the token lifetime shorter than the approval window when practical.
How do you sandbox an autonomous agent safely?
Execute approved work in a disposable, network-restricted sandbox or worker that starts without ambient production credentials. The worker receives only the approved inputs and the temporary token, then is destroyed or reset after completion.
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Filesystem controls
- Mount a minimal, read-only base image and an ephemeral working directory.
- Expose only the files required for the approved operation.
- Block access to host sockets, credential files, metadata services, and other agents’ workspaces.
- Capture a diff or artifact manifest before teardown.
Network controls
- Allow-list exact service names, ports, and destinations.
- Block arbitrary outbound internet access and DNS rebinding paths.
- Inspect uploads and downloads for secrets and prohibited data classes.
- Enforce byte, connection, and duration limits.
Process and resource controls
- Run as a non-root user with a read-only kernel and minimal capabilities.
- Set CPU, memory, process-count, timeout, and output-size limits.
- Terminate on policy violation, repeated failure, or a kill-switch signal.
- Prevent the worker from starting a new privileged worker or changing its own policy.
Sandboxing limits damage after approval; it does not replace authorization. A sandbox with unrestricted network access or a production token still has a large blast radius.
When should a human approve an AI-proposed action?
Require independent, exact-action approval for operations whose impact is high, irreversible, externally visible, or difficult to detect after the fact. Approval must show the resolved target and parameters, not just a vague summary such as “the agent wants to fix production.”
Actions that normally require approval
- Deployments, migrations, rollbacks, and changes to production configuration.
- Deletion of records, files, accounts, infrastructure, or backups.
- Sending external messages, publishing content, or changing customer-visible data.
- Purchases, refunds, transfers, or commitments above a defined amount.
- Permission, identity, firewall, routing, or security-policy changes.
- Connections to a new external network destination or data-sharing recipient.
The approval record should include the proposal hash, approver identity, timestamp, expiry, policy version, and the exact action authorized. If any material field changes, invalidate the approval and require a new decision. Separate the person who proposes an action from the person who approves it for sensitive workflows.
What should an independent audit system record?
Record enough information to reconstruct the decision without trusting the agent’s own narrative. Send logs to a central system the model and execution worker cannot alter.
- Original request and authenticated requester.
- Model identifier, prompt or context references, and proposal hash.
- Tool name, complete arguments, target resolution, and output.
- Policy version, every decision rule result, and denial reason.
- Credential scope, issuer, issue time, expiry, and revocation state.
- Approval identity, exact approved parameters, and expiry.
- Worker image or code version, sandbox limits, network decisions, and resource use.
- Result, output digest, side-effect diff, rollback attempt, and final status.
Redact secrets before storage, but do not discard the fields needed to prove what was authorized. Alert on denied-action spikes, repeated near-misses, token reuse, unusual destinations, and actions outside normal rate or budget envelopes.
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How do you implement zero execution authority incrementally?
Build the controls in stages so that each stage reduces risk without requiring a complete platform rewrite.
- Start read-only. Permit retrieval and analysis only, and add a dry-run mode that computes changes without applying them.
- Define typed actions. Create a finite action catalog with strict schemas, explicit targets, and idempotency requirements. Deny unknown operations.
- Install the policy gateway. Evaluate identity, resource, environment, data class, spend, time, rate, and reversibility outside the model.
- Separate service identities. Create distinct read and write principals, each limited to specific resources and operations.
- Issue temporary credentials. Mint a token only after approval, bind it to the proposal, and expire it quickly.
- Move execution into disposable workers. Restrict filesystem, network, process capabilities, and resource use; remove ambient credentials.
- Add exact-action approvals. Gate production, deletion, external communication, purchases, permission changes, and other high-impact actions.
- Centralize evidence and recovery. Add immutable or append-only audit storage, rate and budget limits, rollback procedures, kill switches, and tested recovery drills.
- Test direct and indirect injection. Place hostile instructions in pages, tickets, documents, code, logs, tool descriptions, and tool results. Verify that the proposal is either denied or held for the correct approval.
Which designs should you compare before choosing an airlock?
Compare designs by where authority lives, how finely capabilities are scoped, and how much damage remains possible after a mistake. A design that is fast for developers but grants a broad token usually has a larger blast radius than a slightly slower design with short-lived, task-bound credentials.
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| Comparison axis | Questions to ask | Safer direction |
|---|---|---|
| Authority boundary | Can the model invoke the real API or only submit a proposal? | Independent gateway owns the decision |
| Capability granularity | Is access one endpoint and resource, or an administrator role? | One typed operation at a time |
| Data scope | Can retrieval expose unrelated confidential records? | Purpose-limited, filtered context |
| Isolation strength | Does execution share a host, filesystem, or network with production? | Disposable, restricted worker |
| Credential lifetime | Can a leaked token be reused tomorrow? | Short expiry, audience and nonce binding |
| Approval rules | Is approval for a category or the exact action? | Exact parameters with expiry |
| Reversibility | Can the change be undone and verified? | Transactional change and tested rollback |
| Observability | Can an operator reconstruct every decision? | Central logs outside agent control |
| Rate and budget | What stops loops or runaway spend? | Hard quotas, concurrency and time limits |
| Latency and ergonomics | Will safeguards be bypassed because they are painful? | Fast dry runs and clear denial messages without weakening policy |
| Blast radius | What is the worst case if model and tool both misbehave? | Small resource, time and financial boundaries |
What common approaches fail, and how can you repair them?
“The system prompt says not to delete anything.”
A prompt is guidance, not an access control. Remove delete capability from the identity, require an explicit policy rule and approval, and make the execution service reject deletion even if the model requests it.
“A content filter will catch prompt injection.”
Filters can miss novel wording and encoded instructions, and they do not decide whether a target or recipient is authorized. Treat all retrieved content as untrusted data, then enforce authorization at the tool and execution boundaries.
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A generic shell or administrator API hides the real operation from policy and makes auditing difficult. Replace it with narrow, typed actions that expose target, arguments, cost, and reversibility.
“Approval once is enough for a workflow.”
Long-lived approval becomes unsafe when targets, parameters, code, or environment change. Hash the exact proposal, expire approval, and restart review after any material change.
“The agent can write its own logs.”
An agent-controlled log cannot establish what really happened. Emit events from the gateway, credential issuer, worker supervisor, and approval service into a separate audit system.
What evidence supports this architecture?
OWASP’s excessive-agency guidance identifies unexpected, ambiguous, or manipulated model output as a path to damaging actions and recommends starting from deny and allowing capabilities explicitly. DOE GEAR states that a natural-language instruction such as “never delete files” is not an access control. Together, these controls support separating model planning from authorization and execution; the airlock terminology is the engineering pattern used to apply them.
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How do you know the airlock is working?
Success is not that the model always produces sensible plans. Success is that an unsafe plan cannot create an unauthorized side effect, and that a safe plan leaves complete evidence and a recoverable result.
- Inject hostile instructions through every untrusted input channel and verify denial or correct approval routing.
- Attempt parameter substitution, target escalation, token replay, cross-environment use, and approval reuse.
- Kill workers mid-operation and confirm idempotency, rollback, and alerting behavior.
- Exceed rate, spend, output, network, and time limits and verify hard stops.
- Remove or corrupt audit connectivity and confirm execution fails closed for actions that require evidence.
- Exercise the emergency kill switch and measure how quickly all active credentials and workers are stopped.
Review these tests after every new tool, model, identity, data source, or policy change. A new integration is a new authority path and must receive the same adversarial treatment as the original system.
Frequently Asked Questions
Can an LLM safely use tools at all?
Yes, when tools expose proposals or tightly bounded operations rather than unrestricted execution. The model should receive filtered context, while a separate gateway validates identity, target, parameters, quota, and approval before any side effect.
How can I stop an agent from exfiltrating data through a tool result?
Classify data before it reaches the model, minimize the fields returned, and enforce destination allow-lists at the network and policy layers. Scan outbound payloads and log the decision independently; do not rely on the model to recognize secrets in its own context.
What is the difference between a sandbox and least privilege for AI agents?
Least privilege limits what an identity is authorized to do; a sandbox limits what the running process can reach if it misbehaves. Use both: a restricted worker with a broad token is still dangerous, and a narrow token in an unrestricted environment can still expose sensitive local data.
Should every AI action require a person to click approve?
No. Low-impact, reversible, well-scoped actions can be automatically allowed by deterministic policy. Reserve human review for production changes, deletion, external communication, purchases, permission changes, and other actions whose consequences or recipients make an automated mistake costly.
What happens if the policy service or audit system is unavailable?
Fail closed for mutations, credential issuance, and other side effects that require a policy decision or evidence. Keep read-only analysis and dry-run work available only if it cannot access protected data or create a hidden execution path.
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