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A prompt guard or content filter can reduce suspicious instructions, but it is not an authorization boundary. If an agent can send mail, delete records, move money, change configuration, or retrieve sensitive data, deterministic controls outside the model must decide whether each specific operation is allowed.
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Why prompt injection becomes a runtime security problem
Prompt injection is not merely a malicious sentence in a chat. It is a way to influence an agent that has access to tools, credentials, data, or other agents. An attacker can place instructions directly in a user message or hide them in a web page, document, email, database row, tool result, conversation history, tool description, or message from another agent. If the model treats that content as an instruction and the runtime executes the resulting tool call, the attack becomes a real side effect.
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Examples include sending a message to an unintended recipient, deleting a record, exposing a secret, changing an access policy, or using a broad service credential on behalf of an attacker. OWASP classifies this combination of excessive permissions and weak control over actions as LLM06:2025 Excessive Agency. The classification identifies a risk category; it is not a measured attack or prevention rate.
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The security objective is therefore precise: an injected instruction may influence a proposal, but it must not be able to authorize an operation. Authorization belongs to the application, identity system, resource server, or downstream API that performs the side effect.
Map the agent’s real attack surface
Start with an inventory of every input channel, identity, tool, data store, and downstream system. Security controls that cover only the chat box miss indirect injection arriving through content the agent is asked to read.
| Channel | How injection can arrive | Runtime implication |
|---|---|---|
| User input | A message asks the agent to ignore policy, reveal secrets, or perform an unrelated action. | Authenticate the caller and authorize the proposed operation against that caller’s actual scope. |
| Retrieved content | A web page, file, ticket, or knowledge-base record contains instructions aimed at the agent. | Keep retrieved text typed and provenance-labelled as data; do not let it expand permissions. |
| Email and database data | Imported text tells the agent to forward information, change records, or follow a link. | Apply the same validation and authorization as for a direct request. |
| Tool results | An API response includes hostile text or a fabricated next step. | Validate the next call independently; a tool result cannot grant a new capability. |
| Conversation and memory | Old messages or shared memory contain instructions from another user or tenant. | Separate memory, state, and credentials by user, tenant, and agent. |
| Agent peers and tool descriptions | A peer agent or plugin suggests an unsafe operation or changes its advertised behavior. | Review dependencies and mediate every operation at the receiving runtime. |
Google Cloud guidance emphasizes that unattended, agent-only operation depends on the security of the program around the model and is exposed to prompt injection, insecure tool chaining, and naive error handling. Design on the assumption that any content channel can be hostile.
Put complete mediation beside every side effect
The most important design decision is where enforcement happens. Place a policy-enforcing execution layer between the model and each tool, API, file system, message service, or administrative endpoint. Every request must pass through it, including requests produced after retrieval, a tool result, a retry, or an approval.
- Authenticate the initiating principal. Identify the human, service, tenant, and agent instance responsible for the request. Do not rely on the model’s statement of who authorized it.
- Identify the exact operation and target. Distinguish reading one project record from searching an entire database, and creating a draft from sending a message. Resolve the target to a canonical resource before policy evaluation.
- Authorize the operation. Check whether that principal, acting through that agent, may perform the specific operation on the specific target. Enforce the decision in the identity and resource systems that actually control access.
- Validate every argument. Check types, ranges, destinations, ownership, allowed fields, file paths, query scope, and payload size. Reject extra or ambiguous parameters rather than silently accepting them.
- Apply impact controls. Require approval, a time limit, a rate limit, a transaction boundary, or a dry-run mode when the action could be irreversible, financial, administrative, or externally visible.
- Execute with bounded credentials. Use the narrow identity and scope required for this operation, not a shared administrator token.
- Record the outcome. Log the principal, effective scope, tool, target, policy decision, approval reference, and result before allowing follow-up actions.
This is complete mediation: no side-effecting request bypasses the same checks. A model explanation such as “the user asked for this” is evidence for a proposal, never proof of permission. The pattern also reduces confused-deputy risk, in which an agent uses its broader credentials to satisfy an attacker’s request.
Separate read and write capabilities
Expose task-specific operations instead of a generic shell, unrestricted HTTP client, or broad administrative API. A capability such as read_project_record(project_id) is easier to constrain than arbitrary database access. If a workflow needs to prepare an email but not send it, provide a draft operation and keep sending behind a separate gate.
Use distinct scopes and identities for read, write, delete, export, and administration. Bind actions performed for a user to that user’s real identity and resource scope; do not silently substitute a highly privileged shared agent credential.
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Design least-privilege agent identities
Least privilege applies to the agent, its tools, and each downstream resource. Inventory the minimum data and operations needed for the task, then remove everything else. A customer-support agent may need to read one customer’s order and create a draft response; it does not automatically need bulk export, account deletion, shell access, or tenant-wide search.
- Use a dedicated identity for each agent or workflow rather than one credential shared by unrelated tasks.
- Scope permissions to the tenant, project, record, folder, or transaction being handled.
- Separate credentials for development, testing, and production.
- Prefer short-lived, revocable tokens and make revocation cut off downstream access immediately.
- Prevent tools from modifying their own permissions, policies, prompts, or deployment configuration.
- Review permissions whenever a model, tool, data source, plugin, MCP server, or agent connection changes.
OWASP recommends narrow tools and user-context execution because a model cannot safely compensate for an identity that is broader than the task. Permission checks should remain effective even when a request bypasses a keyword filter or arrives through an unexpected channel.
Keep untrusted content separate from policy
Use a typed representation for external content that records its source and limits what the model may do with it. For example, pass a retrieved document as a document_text field with provenance metadata, not as if it were a system instruction. Keep trusted policy, task parameters, and untrusted text in distinct fields and distinct processing stages.
Delimiters and wording such as “treat this as data” improve structure, but they do not enforce security. The tool executor must still check authorization and arguments independently. Do not put secrets in model context unless the task requires them; when a secret is needed, prefer a narrowly scoped operation that returns only the required result.
Isolate memory and state by user, tenant, and agent. A memory entry written by one tenant must not become context for another. Apply the same separation to caches, uploaded files, vector indexes, conversation transcripts, and temporary credentials. Protect data in transit and at rest, and define retention and deletion rules for agent traces.
Use human approval for consequential actions
Approval is valuable when an action is high-impact, irreversible, financial, administrative, or externally visible. It is not a cure for excessive permissions and it does not make an unsafe proposal safe by itself.
What an approval request must contain
- The exact operation, such as “send” or “delete,” not a vague description such as “handle this request.”
- The canonical target, including recipient, account, record, environment, or file.
- All material parameters and payloads, including attachments, amounts, permissions, and affected scope.
- The identity and reason under which the action will execute.
- An expiration time and a unique approval reference.
Bind approval to those exact values. If the target, parameters, identity, or payload changes, invalidate the approval and request a new one. Keep the authorization check in force after approval; an approval token must not become a general-purpose pass.
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An interface that asks only “Proceed?” gives reviewers too little context. Google warns that people may approve destructive suggestions without verifying their safety, so show the complete action and make safe alternatives, cancellation, and escalation obvious. For unattended workflows, define explicit time, scope, and rate limits and provide a system-level pause or stop control that does not depend on the model cooperating.
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Contain the blast radius
Assume that some malicious instruction will eventually reach a model. Containment limits what happens next.
- Isolate execution. Run risky tools in separate processes, containers, accounts, or network segments. Restrict outbound destinations and file-system paths.
- Limit data exposure. Return only fields required for the task; redact secrets and personal data from prompts, logs, and tool responses.
- Bound volume and speed. Apply quotas, concurrency limits, and rate limits to messages, exports, writes, and retries. Monitoring and rate limits reduce damage but do not prevent every excessive-agency attack.
- Use transactional safeguards. Prefer previews, drafts, soft deletes, staged changes, and reversible operations before irreversible commits.
- Protect dependencies. Inventory models, tools, plugins, MCP servers, retrieved sources, and agent-to-agent links. Review changes and do not automatically enable an unreviewed capability.
- Maintain revocation. Disable an identity, token, tool, or workflow quickly and verify that the downstream service honors the revocation.
Make actions observable and interruptible
Logs should let an investigator reconstruct what happened without trusting the model’s narrative. Record the authenticated principal, tenant, agent version, effective permission scope, tool name and version, canonical target, normalized arguments or a protected hash of sensitive payloads, policy decision, approval reference, timestamp, and downstream result. Capture denied attempts as well as successful calls.
Alert on unusual target changes, repeated denials, privilege escalation attempts, export volume, rapid retries, cross-tenant access, tool changes, and actions outside the agent’s normal task profile. Keep logs tamper-resistant and separate from the agent’s writable state. Monitoring supports detection and response; it is not evidence that all injections were caught.
A practical rollout plan
- Inventory the workflow. List every input channel, retrieval step, memory store, tool, identity, permission, downstream system, and possible side effect.
- Write deterministic policy. Define allowed and denied operations by principal, agent, target, data field, environment, and impact level. Put the policy in application or resource authorization code, outside the model.
- Replace broad tools. Split generic shell or API access into small read, draft, update, delete, and administrative capabilities with separate scopes.
- Add complete mediation. Route every call, including retries and tool-chained calls, through authentication, target resolution, authorization, argument validation, and logging.
- Classify impact. Mark actions as read-only, reversible write, irreversible write, financial, administrative, or externally visible. Require exact approval for the higher-impact classes.
- Separate state and secrets. Enforce tenant and user boundaries for memory, caches, files, indexes, and credentials. Remove secrets from context unless essential.
- Instrument a safe environment. Use dummy records and instrumented tool substitutes that cannot send, delete, or alter production data.
- Test in audit mode. Review detections, denials, and false positives on a limited scope before broader enforcement. An alert is not proof that every attack was detected.
- Enable blocking deliberately. Move to blocking only after reviewing legitimate task failures and confirming that revocation, approval, and recovery paths work.
- Retest every change. Repeat the tests after changing the model, prompt, tool, permission, data source, plugin, runtime, or network path.
Test the real boundary, not just the detector
OWASP describes its hand-picked prompt-injection examples as illustrative smoke tests rather than a representative benchmark. Build tests around the actual task and observable authorization outcome.
| Test case | Safe expected result | What to verify |
|---|---|---|
| Direct instruction to ignore policy and export data | Proposal is denied or limited to the caller’s permitted records. | Authorization rejects the export even if the text avoids detector keywords. |
| Retrieved page instructs the agent to send a secret | Retrieved text is treated as data; no send operation is authorized. | Provenance handling and tool-boundary checks work together. |
| Tool result requests a new privileged tool | The follow-up call is denied unless independently allowed. | Tool output cannot grant capability. |
| Approval followed by changed recipient or amount | Original approval is invalidated and a new review is required. | Approval binds to exact parameters and does not replay. |
| Cross-tenant memory or file reference | Access is denied and the event is logged. | Isolation holds across context, cache, storage, and retrieval. |
| Revoked identity during an active workflow | Subsequent downstream calls fail. | Revocation reaches the service that performs the side effect. |
Use harmless payloads, synthetic secrets, and instrumented tools. Test direct and indirect injection through user, retrieval, email, database, conversation, and tool-result channels. Measure authorization decisions, parameter validation, approval binding, logging, and final side effects—not merely whether a classifier fired.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Microsoft Defender for Endpoint runtime protection
Microsoft documents AI agent runtime protection for Windows endpoints as a preview feature. The documentation describes device-level detection of prompt injection and the ability to block or audit supported agent actions before execution.
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- Audit mode: detects and reports while allowing the supported action to continue.
- Block mode: blocks supported actions identified by the protection feature.
- Recommended rollout: start in audit for a limited device group, review detections and legitimate-task impact, then move to block when ready.
This is a platform-specific layer for supported agent interfaces and actions on Windows endpoints. It does not establish universal protection for every framework, operating system, tool, or injection technique. Check the current preview status, compatibility, licensing, and deployment requirements before making it part of a control system. Keep application-level identity, authorization, validation, approval, isolation, and logging in place even when endpoint protection is enabled.
How to compare runtime-security implementations
There is no single product score that proves an agent is secure. Compare an implementation against the dimensions below and document evidence for each one.
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|---|---|
| Enforcement location | Are checks beside every side effect, including chained calls, retries, and downstream APIs? |
| Identity granularity | Can the system distinguish user, tenant, agent, workflow, and effective resource scope? |
| Capability design | Are tools narrow and task-specific, or do they expose shells, open-ended fetches, or unrelated administration? |
| Input coverage | Does the design treat user text, retrieval, email, databases, memory, tool results, and peer agents as untrusted channels? |
| Approval binding | Does approval cover the exact operation, target, parameters, identity, expiration, and replay behavior? |
| Isolation | Are users, tenants, memory, files, credentials, tools, and network destinations separated? |
| Observability and revocation | Can responders reconstruct decisions, stop execution, revoke credentials, and verify that access actually ended? |
| Compatibility | Which operating systems, agent frameworks, interfaces, and actions are supported? |
| Operational cost | What legitimate tasks become slower or require review, and how will false positives be handled? |
Common mistakes and their fixes
Relying on a system prompt as the boundary
Failure: The model is told never to reveal secrets or call a dangerous tool, but the tool accepts any request it receives.
Fix: Enforce identity, authorization, target, and parameter checks in the executor and downstream service.
Giving the agent one administrator credential
Failure: A prompt injection turns a narrowly intended task into broad data access or destructive administration.
Fix: Use dedicated identities, user-bound scopes, separate read and write capabilities, and short-lived revocable tokens.
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Trusting retrieved text or tool output
Failure: A document or API response is treated as a new instruction with authority to call another tool.
Fix: Preserve provenance and type information; require independent policy authorization for every follow-up action.
Approving a vague summary
Failure: A reviewer approves “send the response” without seeing the recipient, attachments, or payload.
Fix: Display the exact operation and target, bind approval to parameters, expire it, and re-review changes.
Testing only whether a detector alerts
Failure: The test passes because a warning appears, even though the unauthorized tool call still succeeds.
Fix: Assert the final authorization decision and side effect using harmless, instrumented tools.
Assuming monitoring prevents abuse
Failure: Logs and rate limits exist, but an agent can still perform unlimited destructive actions before anyone responds.
Fix: Combine observability with narrow permissions, transaction limits, approval, isolation, and a tested stop mechanism.
Operational checklist
- Every side-effecting operation has an authenticated principal and deterministic authorization decision.
- Tools expose the minimum operations and data required for the task.
- User, retrieved, historical, tool-returned, and peer-agent content is treated as untrusted.
- Memory, state, files, indexes, and credentials are isolated by user and tenant.
- High-impact actions show exact targets and parameters and require expiring approval.
- Approval remains tied to authorization and is invalidated when material values change.
- Identities and tokens are revocable, and revocation cuts off downstream access.
- Logs capture identity, scope, tool, target, decision, approval, and outcome.
- Rate limits, quotas, and transaction boundaries limit blast radius.
- Safe pause and stop controls work without model cooperation.
- Direct and indirect injection tests exercise the real input channels and assert final outcomes.
- Changes to models, tools, permissions, data, or runtime configuration trigger retesting.
Putting the design together
Runtime security is a layered control system, not a clever prompt. Keep the model useful for interpreting tasks and proposing actions, while deterministic code and resource authorization decide what may actually happen. Narrow capabilities, user-bound identities, typed untrusted content, exact approvals, isolated state, observability, rate limits, and tested revocation each address a different failure mode. Together they ensure that a malicious instruction can at most produce a rejected or bounded proposal—not an unreviewed side effect.
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