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What an AI agent security design must control
An agent combines a language model with instructions, memory, retrieval, tools, and an execution loop. That combination creates several paths for data to leave your control or for an attacker to influence an action. Security therefore has to cover more than the model prompt. It must include the agent’s identity, every data source, every tool boundary, downstream authorization, runtime isolation, approvals, audit records, and emergency shutdown.
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Microsoft’s guidance summarizes one immediate risk: “Even in a private chat session, credentials in prompts are a security risk and may appear in logs.” NIST describes agent hijacking as a failure to separate trusted internal instructions from untrusted external data. Use those two ideas as design requirements: secrets never belong in conversational context, and content an agent reads must not be allowed to redefine what the agent is authorized to do.
1. Keep credentials and secrets out of the agent’s context
Do not paste API keys, passwords, private certificates, database connection strings, bearer tokens, or recovery codes into a prompt, chat history, memory record, uploaded file, or unrestricted agent configuration. A model may quote, summarize, store, or send that content to another tool, and logs may retain it long after the session ends.
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Use a separate identity for every agent
- Create a distinct service identity for each agent or deployment boundary rather than sharing a human account or a broad application credential.
- Separate development, test, and production identities. A test agent must not be able to reach production systems simply because both environments use the same configuration.
- Separate read-only identities from identities that can create, modify, or delete records. A read-only reporting agent should never inherit write permissions.
Prefer short-lived, scoped access
Issue tokens for a specific task, resource, and time window. Scope a token to the exact API, tenant, project, table, repository, or directory required. Rotate credentials on a defined schedule and revoke them immediately when an agent, integration, or operator is no longer trusted. If a task needs elevated access, obtain it just for that action instead of embedding a permanently privileged secret.
Prevent accidental exposure
- Keep secrets in a managed secret store or identity broker, not in prompts, chat transcripts, shell history, or files that the agent can freely read.
- Redact secrets before sending telemetry, traces, error reports, or model context.
- Return opaque handles or narrowly filtered results to the model instead of raw credentials or full connection details.
- Define who can retrieve, rotate, and revoke each secret, and log those administrative events.
2. Map every data flow and classify sensitivity
Make a data-flow inventory before granting tools. Record what enters the system, where it is stored, what is sent to a model, and which component can receive the result.
| Flow or store | Questions to answer | Minimum control |
|---|---|---|
| User input | Can it contain personal, financial, confidential, or credential data? | Validate, minimize, and redact before model submission. |
| Chat history and memory | How long is it retained, and can another user or tenant retrieve it? | Use retention limits, access controls, and tenant isolation. |
| Retrieval results | Which repositories can be searched, and can documents contain instructions? | Filter by the initiating user’s authorization and treat text as untrusted data. |
| Model request and response | What sensitive fields leave your boundary, and where are prompts logged? | Send only necessary fields and apply redaction and provider controls. |
| Tool arguments | Can a generated value select a path, query, recipient, amount, or command? | Validate against a typed schema, allowlist, and policy decision. |
| Tool responses | Can returned content contain malicious instructions or secrets? | Constrain output, scan sensitive fields, and keep it separate from trusted instructions. |
| Logs and traces | Do records include prompts, tokens, payloads, or personal data? | Minimize, redact, restrict access, and set retention periods. |
Classify data before it reaches a model or tool. Minimize personally identifiable information, financial records, credentials, and confidential business data. Microsoft specifically advises against using real customer names, email addresses, or usage data as example inputs. Substitute synthetic or masked values when testing prompts and workflows.
3. Treat external content as hostile input
Web pages, email messages, uploaded files, retrieved documents, user text, tool responses, and outputs from another agent can all contain instructions designed to redirect behavior. A malicious document might tell an agent to ignore its system rules, reveal a secret, or call an unrelated endpoint. The agent must not gain authority merely because text appears in a high-priority-looking location.
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Separate instructions from data
- Keep system and developer instructions outside user-controlled or retrieved content.
- Label retrieved text and tool output as data, not commands.
- Do not concatenate untrusted text into policy statements or executable templates.
- Require the policy layer to approve an action independently of the model’s explanation.
Microsoft Agent Safety states: “Treat LLM-provided arguments as untrusted input, similar to user input in a web API.” Apply that rule to every generated argument, including a URL, file path, SQL fragment, recipient address, shell command, quantity, or permission name.
Validate before execution
- Parse the model’s proposed call against a strict schema with explicit types and allowed fields.
- Reject unknown fields, malformed values, excessive lengths, and ambiguous encodings.
- Check the requested target, operation, and scope against the initiating user’s authorization.
- Apply allowlists for commands, domains, directories, tables, recipients, and resource identifiers.
- Send only the validated, policy-approved call to the downstream service.
4. Enforce least privilege at the tool boundary
Start with deny-by-default. An agent should receive only the tools and permissions required for its stated job, not a general-purpose account. Authorization must bind together the initiating user or agent, the action, the target, and the permitted scope. Checking only whether “the agent” is allowed is too coarse.
| Agent task | Allowed capability | Explicitly blocked capability |
|---|---|---|
| Read a project report | Read specific report records in the user’s project | Write, delete, export unrelated projects, or alter permissions |
| Draft an email | Read approved context and create a draft | Send, add recipients, or attach unapproved files without confirmation |
| Analyze files | Read an allowlisted directory in a sandbox | Read secrets, system directories, or arbitrary network shares |
| Update a ticket | Change approved fields on tickets the user can access | Bulk updates, deletion, assignment outside policy, or privilege changes |
Use separate tools for separate risk levels. A “preview” tool can calculate a change without applying it; an execution tool can apply only the reviewed result. Keep database permissions aligned with the operation: read-only extensions must not inherit UPDATE, INSERT, or DELETE rights.
5. Require fresh approval for consequential actions
Human confirmation is appropriate when an action sends a message, spends money, makes a purchase, deletes data, deploys code, changes permissions, performs a bulk operation, or crosses a trust boundary. The approval should happen immediately before execution and describe the actual target and effect, not a vague request to “continue.”
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Design an approval prompt that can be audited
- Show the initiating user, tool name, operation, target, scope, and material parameters.
- Show recipients, amount, record count, or affected environment when those values apply.
- Identify whether the action is reversible and what recovery path exists.
- Expire an approval if the arguments, target, or context changes.
- Record who approved, when, what was displayed, and what actually executed.
Use stronger review for sensitive data access, production changes, permission grants, and actions that affect many records. A model’s confidence score or natural-language justification is not a substitute for authorization.
6. Sandbox execution and cap the blast radius
Code execution and tools that can reach networks or operating-system resources deserve isolation. Run untrusted code in a sandbox with a restricted filesystem, no access to host secrets, and only the network egress destinations required for the task. Keep production credentials and production data outside the sandbox unless a separately approved operation needs them.
- Allowlist outbound domains, ports, and protocols; deny arbitrary internet access by default.
- Limit CPU, memory, wall-clock time, process creation, file size, and storage.
- Cap loop iterations, agent steps, query volume, batch size, and spend to contain runaway behavior.
- Use quotas and circuit breakers that stop execution when a threshold is reached.
- Separate tenants and prevent one agent’s memory, files, or network identity from being reused by another.
These limits reduce impact when a prompt injection succeeds, a tool misbehaves, or the model repeats an action. They do not replace authorization; they are a second containment layer.
7. Log enough to reconstruct every decision
Audit records should let an operator answer: what input arrived, which instructions and retrieved records were used, what the model proposed, which policy checks ran, who approved, what tool executed, what changed, and what failed. Log each tool call, its validated arguments, the authorization result, approval events, state changes, and errors.
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Protect logs as sensitive data. Redact credentials and unnecessary personal information, restrict access, detect tampering, and define retention periods. Storing every raw prompt forever can create a second data-exposure problem. Record hashes, identifiers, and selected fields where full payloads are not needed for investigation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Build revocation and recovery before launch
Document the owner of each agent, its permitted data and tools, its dependencies, and the exact shutdown procedure. An incident plan should be executable by someone other than the original developer.
- Disable the agent identity or feature flag to stop new work.
- Revoke active tokens and rotate any credential that may have been exposed.
- Remove stale permissions, network routes, webhooks, and scheduled jobs.
- Preserve relevant audit records while preventing further sensitive logging.
- Identify affected records, recipients, systems, and tenants.
- Review the triggering input, tool arguments, approvals, and downstream responses.
- Restore only after permissions, prompts, validators, and monitoring have been corrected.
Review permissions and logs regularly, not only after an incident. Test that revocation actually blocks calls, that expired approvals cannot be replayed, and that a disabled agent cannot obtain a new token through another integration.
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Implementation sequence for a new agent
- Define the job and trust boundaries. Write down the user, data sources, tools, environments, and actions the agent must perform.
- Classify the data. Mark personal, financial, credential, confidential, and public fields; remove anything unnecessary from model context.
- Create the identity. Issue a dedicated, short-lived identity and separate read-only and write-capable roles.
- Implement deny-by-default tools. Allowlist exact operations, resources, paths, destinations, and limits.
- Add typed validation and downstream authorization. Treat every model-generated argument as hostile input and recheck permissions at the service that performs the action.
- Add approval gates. Require fresh, auditable confirmation for irreversible, high-impact, bulk, sensitive, or cross-boundary actions.
- Sandbox and cap execution. Restrict filesystem and network access and enforce step, time, volume, and spend limits.
- Instrument and test. Capture policy decisions and state changes, then test prompt injection, malformed arguments, excessive loops, cross-tenant access, and failed calls.
- Exercise shutdown. Revoke credentials, disable the agent, investigate a simulated suspicious call, and verify recovery procedures.
How to compare agent designs or platforms
Use the same questions for an internal build, a hosted service, or a framework. A polished chat interface does not demonstrate that the underlying agent is safe.
| Comparison axis | Evidence to request |
|---|---|
| Credential isolation and revocation | Per-agent identities, short-lived tokens, rotation workflow, immediate invalidation, and secret redaction. |
| Per-tool and per-action authorization | Typed schemas, resource-level policies, user-context checks, and downstream enforcement. |
| Data minimization and tenant isolation | Retention controls, memory boundaries, filtering, encryption, and cross-tenant access tests. |
| Prompt-injection resistance | Instruction/data separation, retrieval labeling, adversarial tests, and refusal or escalation behavior. |
| Sandbox and egress controls | Filesystem isolation, network allowlists, quotas, execution limits, and production separation. |
| Approval experience | Fresh confirmation, complete action details, expiry, replay protection, and approval audit records. |
| Observability | Tool-call logs, policy outcomes, state changes, redaction, retention, export, and tamper detection. |
| Operational cost and latency | Added review steps, policy checks, sandbox startup, logging volume, and expected failure handling. |
Pre-launch checklist
- No prompt, memory record, log, test fixture, or configuration contains a long-lived secret.
- Each agent has a distinct identity, short-lived credentials, documented owner, and revocation path.
- Data flows are inventoried, classified, minimized, and isolated by tenant and environment.
- Retrieved content and tool responses are treated as untrusted data, never as policy.
- Tool arguments are schema-validated, allowlisted, and authorized again by the downstream service.
- Write, delete, payment, messaging, deployment, permission, and bulk operations require fresh approval.
- Execution is sandboxed, network egress is restricted, and loops, steps, queries, time, and spend are capped.
- Logs capture calls, decisions, approvals, changes, and failures without retaining unnecessary secrets or personal data.
- Teams have tested token rotation, emergency disablement, stale-permission removal, and incident investigation.
Common failures and corrective actions
The agent can read everything because it is “internal”
Internal status does not make a data source trustworthy or a broad identity safe. Replace the shared account with per-agent, per-resource permissions and filter retrieval by the initiating user’s access.
A confirmation button appears, but the action changed
Check whether the tool executed before approval, whether the approved parameters were later mutated, and whether approval can be replayed. Move enforcement into the execution service, bind approval to an immutable action record, and expire it when any material field changes.
Logs are useful but contain secrets
Redact before ingestion, reduce payload capture to the fields needed for investigation, restrict log readers, and rotate any credential that has already appeared in telemetry.
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Inspect the entire path, including summarizers, memory writers, and tool responses. Label untrusted content at every handoff and require an independent policy decision before any side effect.
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