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The dependable way to secure an AI agent is to enforce authorization and input validation outside the model, at every tool and side-effect boundary. Treat prompts, retrieved documents, web pages, email, memory, tool results, tool descriptions, and messages from other agents as untrusted data. Give the agent only narrowly scoped identities and capabilities, require informed approval for consequential actions, isolate state, log what happened, and test the controls against direct and indirect prompt injection. A system prompt or content filter can improve behavior, but neither is an authorization boundary.
Why runtime security matters
Prompt injection becomes a security incident when an agent can turn hostile instructions into an external action. An attacker may write directly in a chat prompt, hide instructions in a retrieved file, place them on a web page, include them in an email or database record, return them from a tool, or send them through another agent. If the model treats that text as an instruction, it may call a tool to send a message, delete data, expose a secret, change an account, or make an administrative request.
The important boundary is therefore not the model’s answer. It is the point where an action can affect a system outside the model. OWASP classifies the underlying design problem as LLM06:2025 Excessive Agency. The classification is a taxonomy label, not a measured attack or detection rate; there is no reliable percentage reduction that can be claimed for the controls in this guide.
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Google Cloud’s guidance describes an agent-only operating mode as taking action without waiting for approval. In that mode, security depends on the program around the model and is exposed to prompt injection, unsafe tool chaining, and poor error handling. Autonomous operation can be useful, but it requires stronger deterministic controls rather than greater trust in the model.
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Start with a precise threat model
Document what the agent can read, what it can call, whose identity it uses, and which systems can be changed. Include normal and exceptional paths: retries, background jobs, delegated tasks, agent-to-agent messages, cached results, and administrator tools. For each possible side effect, identify the principal, operation, target, parameters, approval requirement, and rollback or stop mechanism.
Classify every input channel as untrusted
- User prompts and uploaded files.
- Retrieved pages, documents, tickets, email, and chat history.
- Database records and previous agent memory.
- Tool results, error messages, and tool metadata.
- Tool descriptions, plugins, MCP servers, and agent-to-agent messages.
Keep provenance visible to the application. A retrieved paragraph should arrive as data with an origin and trust classification, not as an instruction that is indistinguishable from system policy. Delimiters and wording such as “treat this as reference text” help the model separate roles, but the executor must still validate the action independently.
Map the side effects
List actions such as reading a record, creating a draft, sending an external message, changing permissions, deleting an object, moving money, or running an administrative command. Record whether each action is reversible, externally visible, financial, privacy-sensitive, or capable of affecting many resources. This map determines the required scope, approval, rate limit, and monitoring.
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Put enforcement beside the side effect
Every operation needs complete mediation: the execution layer authenticates the initiating principal, checks authorization for the specific operation and target, validates arguments, and rejects anything outside policy before the downstream request is made. The model’s explanation that an action is safe or allowed is not authorization.
- Authenticate the caller and agent. Establish which user, service, or delegated identity initiated the task. Do not rely on a name supplied in model-generated text.
- Resolve the effective scope. Determine the resources and operations permitted to that identity for this request, tenant, project, and time window.
- Authorize the exact operation. Check the verb, target, and relevant fields against deterministic policy and the resource system that will perform the action.
- Validate parameters. Enforce types, allowed values, size limits, resource ownership, destination restrictions, and any business rules. Reject missing, extra, or ambiguous arguments.
- Execute with the narrow credential. The downstream service should receive only the authority needed for this operation, not a broad administrator token.
- Record the decision and outcome. Log the principal, effective scope, tool, target, decision, result, and correlation identifier.
Apply these checks on every retry, asynchronous job, and internal service hop. Revoking a user or agent identity must actually remove downstream access; stopping the model loop alone is insufficient if queued work can still run.
Design small, task-specific capabilities
Capability design limits what a compromised or confused agent can do. Prefer a narrow operation such as “read this project record” or “create a draft in this mailbox” over a generic shell, unrestricted HTTP client, open-ended file fetcher, or broad administrative API. Separate read and write operations and use distinct scopes when the task does not require both.
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| Capability choice | Safer design | Why it matters |
|---|---|---|
| Data access | Expose one resource type and permitted fields | Limits disclosure and prevents unrelated queries |
| Writes | Separate draft, submit, update, and delete operations | Allows different approval and rollback rules |
| Network access | Allowlisted destinations and methods | Reduces exfiltration and server-side request abuse |
| Administration | Dedicated, time-limited administrative capability | Avoids handing routine tasks an administrator credential |
| Execution | Typed parameters and fixed actions instead of a general shell | Prevents arbitrary command construction |
Bind actions performed for a user to that user’s actual identity and scope. Silently substituting a shared, more privileged agent credential creates a confused-deputy risk: the agent can be tricked into using authority the user never had.
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Pass untrusted material in typed fields that preserve provenance, such as retrieved_text, email_body, or tool_result. Keep trusted policy, authorization decisions, and approval records outside the model context when possible. Do not place secrets in context unless the task genuinely requires them; redact or tokenize sensitive values before retrieval and logging.
Instruction hierarchy is useful for reducing accidental confusion, but it is not a security mechanism. An attacker can place text that looks like a higher-priority instruction in a document or tool response. The model may summarize or reason about that text, while the policy engine independently decides whether any proposed action is permitted.
Protect memory and multi-tenant state
Isolate conversation history, long-term memory, cached tool results, files, and embeddings by user, tenant, and agent. Verify ownership on reads as well as writes. A prompt injection that contaminates shared memory can affect later sessions even after the original message is gone. Treat memory restoration, summarization, and replay as data-ingestion paths subject to the same validation as live input.
Use approval for consequential actions
Require meaningful human approval for high-impact, irreversible, financial, administrative, privacy-sensitive, or externally visible operations. The approval record should bind to the exact operation, target, and parameters. If any of those values change, invalidate the approval and request a new decision.
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What an approval screen must show
- The identity on whose behalf the action will run.
- The exact tool and operation.
- The target resource, destination, and affected scope.
- The complete parameter values, including attachments or recipients.
- The expected external effect and whether it is reversible.
- The expiration time and a unique action identifier.
A prompt that asks only “Proceed?” or repeats the agent’s short description gives the reviewer too little information. Google cautions that people can approve destructive suggestions without independently checking safety. Approval therefore supplements, rather than replaces, least privilege, parameter validation, and downstream authorization.
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After approval, run the authorization check again immediately before execution. Prevent replay by binding the approval to a single action identifier, expiry, and parameter digest. Provide a system-level pause or stop path that does not depend on the model choosing to cooperate. For unattended workflows, add explicit limits on scope, duration, action count, and request rate.
Contain tools, dependencies, and data
Models, plugins, MCP servers, retrieval systems, tool packages, and agent connections are all part of the security boundary. Inventory them, review changes, and do not automatically enable an unreviewed tool. A malicious or compromised dependency can return hostile instructions, alter data, or abuse the authority assigned to the agent.
Isolation controls
- Run agents with separate identities and resource scopes rather than a shared administrator account.
- Separate tenants, users, projects, memory stores, and temporary files.
- Protect data in transit and at rest, and minimize secrets exposed to the model.
- Use network and destination restrictions for outbound calls.
- Keep a reliable credential-revocation and emergency-stop mechanism.
Isolation and rate limiting reduce the blast radius and make abuse easier to detect; they do not prove that prompt injection is impossible. Monitor downstream actions, not only model text, because a benign-looking response can precede a harmful API call.
Build useful auditability and response
At minimum, log the initiating identity, agent identity, tenant or project, effective permission scope, input provenance, tool name and version, target, parameter validation result, approval identifier, policy decision, downstream status, and timestamps. Protect logs from tampering and avoid recording raw secrets. Correlate planning, approval, execution, retries, and resulting changes so an investigator can reconstruct the chain.
Alert on denied operations, unusual destinations, privilege escalation attempts, repeated validation failures, unexpected tool sequences, high-volume reads, and actions outside normal time or scope. Rate limits can constrain excessive agency and provide a signal for investigation. Define a response runbook: pause the agent, revoke credentials, isolate affected state, preserve evidence, assess data exposure, and safely replay only after authorization is restored.
Test the real runtime boundary
Security tests must exercise the channels and tools the production agent actually uses. Use harmless records, synthetic secrets, and instrumented tool substitutes so tests cannot send real messages or delete real data.
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- Direct injection: place conflicting instructions in the user message and verify that unauthorized tool calls are rejected.
- Indirect injection: put hostile text in a retrieved page, file, email, database row, memory entry, tool result, and agent message.
- Boundary bypass: vary wording, encoding, language, nesting, and delivery channel to ensure the decision does not depend on a keyword detector.
- Authorization cases: test wrong user, wrong tenant, out-of-scope target, invalid parameter, expired identity, and revoked credential.
- Approval cases: change a target or parameter after approval, replay an approval, let it expire, and submit a denied action.
- Operational cases: force retries, timeouts, partial failures, queueing, and emergency stops; confirm that no unapproved side effect proceeds.
Measure the observable outcome—allowed, denied, approved, executed, or stopped—not merely whether a detector raised an alert. OWASP’s hand-picked injection examples are illustrative smoke tests, not a representative benchmark of application traffic. Build a test set from the actual task, allowed scope, and failure consequences, then rerun it whenever models, tools, permissions, data sources, or runtime settings change.
A staged rollout that reduces risk
- Inventory: record every data source, tool, identity, permission, downstream system, and side effect.
- Define policy: express allowed and denied operations, targets, parameters, scopes, approval rules, and limits in deterministic code or an authorization service.
- Instrument: add complete logs, correlation IDs, alerts, revocation, and a stop path before enabling autonomy.
- Use synthetic tests: run direct and indirect injection tests with harmless data and instrumented tools.
- Start in audit: review detections, denials, false positives, and unexpected tool paths while actions remain observable.
- Enforce narrowly: move high-risk operations to blocking and approval gates first; expand only after evidence supports the change.
- Retest continuously: repeat tests after any change to the model, prompt orchestration, tool schema, identity, retrieval source, or deployment configuration.
An alert indicates that a rule fired; it does not establish that all attacks were detected. Conversely, a blocked example does not demonstrate universal protection. Keep these distinctions in operational reports.
Microsoft Defender for Endpoint runtime protection
Microsoft documents AI agent runtime protection for Windows endpoints as a preview feature. The documentation describes device-level prompt-injection detection and the ability to block or audit supported agent actions before execution.
| Mode | Documented behavior | Practical use |
|---|---|---|
| Audit | Detects and reports while allowing the supported action to continue | Limited-device observation, tuning, and impact assessment |
| Block | Blocks supported actions identified by the protection | Enforcement after detections and compatibility are understood |
Microsoft recommends starting in audit on a limited device group, reviewing detections, and then moving to block when ready. This is a platform-specific defense for supported interfaces and actions. The documentation does not establish universal protection for every agent framework, operating system, tool, or injection technique. Confirm current preview status, compatibility, licensing, and deployment requirements before making it part of a security guarantee, and retain independent identity, authorization, and parameter checks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare runtime-security implementations
When evaluating an architecture or product, compare the controls that determine whether a model mistake can become a real side effect. The available guidance supports these decision dimensions, but it does not provide a comparative product benchmark.
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|---|---|
| Enforcement location | Are checks performed at every side-effecting boundary, including retries and asynchronous workers? |
| Identity and scope | Can each user, tenant, agent, and operation receive a distinct least-privilege scope? |
| Input coverage | Are prompts, retrieval, tool results, memory, and agent-to-agent messages handled as untrusted channels? |
| Approval binding | Does approval cover the exact target and parameters, expire, and resist replay? |
| Isolation | Are state, tools, credentials, and sensitive data separated across users and tenants? |
| Audit and revocation | Can responders reconstruct actions and immediately cut off downstream access? |
| Compatibility | Which operating systems, agent frameworks, tools, and action types are actually supported? |
| Operational effect | What legitimate tasks are delayed or denied, and how are exceptions reviewed? |
Common designs that fail
Relying on a system prompt
A system instruction can tell the model not to follow retrieved commands, but hostile text can still influence planning. Keep the instruction for behavioral guidance and enforce the permission decision in application or downstream code.
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Using a content filter as the authorization layer
Keyword and classifier detections can miss paraphrases, encoded text, or attacks delivered through a tool result. A missed alert must not become a successful write; deterministic authorization must reject the operation.
Giving an agent a broad service account
Shared administrator credentials turn a small prompt-injection flaw into a large compromise. Use user-context execution where appropriate, separate read and write scopes, and grant only the resources required by the task.
Adding approval without showing details
Human review is ineffective when the reviewer sees only a vague summary. Present the exact operation, target, parameters, identity, and effect, then recheck policy after approval.
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Conversation text does not show which credential reached which resource. Log the downstream decision and outcome, protect the records, and make them usable for incident reconstruction.
Testing only obvious prompt attacks
A few examples can show that a detector works on those examples, not that the runtime is secure. Test indirect channels, changed parameters, replay, retries, and denied authorization outcomes with safe substitutes.
Runtime security checklist
- Every tool call authenticates the initiating principal.
- Authorization checks the exact operation, target, and parameters.
- Downstream services enforce the same decision instead of trusting model output.
- Tools are narrow, typed, and separated by read, draft, write, and administrative scope.
- User and tenant context is preserved; privileged shared credentials are not substituted silently.
- All content sources are labeled and treated as untrusted data.
- Memory, files, caches, and embeddings are isolated by user, tenant, and agent.
- High-impact actions require detailed, expiring, non-replayable approval where appropriate.
- Scope, duration, action count, and rate limits constrain unattended work.
- Identity revocation and an independent stop path cut off queued and downstream work.
- Logs capture identity, scope, tool, target, decision, approval, and outcome without exposing secrets.
- Direct and indirect injection tests run after changes to models, tools, permissions, or data sources.
The practical bottom line
Prompt injection cannot be solved by asking the model to behave or by placing a filter in front of it. Secure an AI agent as an application with a narrow authority budget: authenticate every request, authorize every side effect, validate every argument, isolate every user’s state, require informed approval for consequential actions, observe downstream behavior, and maintain a tested stop and revocation path. These controls contain failures and make abuse harder to turn into damage; they do not justify claiming that every injection will be detected or prevented.
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