Security teams should define clear trust boundaries around the model, external inputs, and extensible functions before production use. Untrusted content should be segregated or sanitized, privileged actions should require explicit approval, and access to backend systems should follow least privilege. Monitoring must continue at runtime so suspicious model behavior or boundary violations can be detected early and investigated quickly.
Drawing Trust Boundaries Around LLM Inputs, Tools, and Actions
Trust boundaries should be explicit, not implied by the model’s ability to “understand” data. The safest operating model is to treat prompts, retrieved content, plugin responses, and model outputs as different trust classes, then constrain what each class can influence. That means separating read-only context from action-capable functions, and preventing untrusted text from flowing directly into privileged decisions.
For teams using retrieval and tool execution together, the boundary usually fails at the handoff between interpretation and action. A model can summarize or classify untrusted content, but it should not be allowed to convert that content into a backend request, command, or policy decision unless the request is independently validated. This is where OWASP Top 10 for Agentic Applications 2026 is especially useful: it frames prompt injection, tool misuse, and privilege abuse as distinct failure modes, not one generic AI risk.
Teams also need to define where external data is allowed to influence the model. Search results, documents, tickets, and API responses should be tagged by source and sensitivity so downstream logic can apply different handling rules. A retrieval source that is acceptable for summarisation may still be inappropriate for triggering an action, especially if the data can be shaped by an untrusted party.
Controls for Segregation, Approval, and Least Privilege
Boundary design becomes operational when the control plane enforces it. Untrusted content should be isolated from privileged functions by default, with sanitisation, allowlisting, and typed interfaces reducing the chance that model-generated text is interpreted as executable instruction. Where the model can request actions, those actions should pass through an approval step or a policy engine that checks scope, sensitivity, and business justification before anything reaches a backend system.
Least privilege matters because plugins and connectors often inherit far more access than they need. Restrict each plugin to the smallest API surface, narrow data scope, and shortest-lived credential set that still supports the use case. If a tool only needs read access to one system, it should not also be able to write, delete, or impersonate broader service functions. Guidance from NIST AI Risk Management Framework and NIST SP 800-207 Zero Trust Architecture aligns well here because both favour explicit verification, reduced implicit trust, and continuous enforcement rather than one-time approval.
When plugins handle authentication material or session context, the access path itself becomes part of the trust boundary. That is why security teams should prefer scoped tokens, short expiry, and revocation-ready designs over broad, durable secrets. For implementation patterns around workloads and services, SPIFFE workload identity specification is a useful reference for strong, bounded service-to-service identity.
Runtime Monitoring, Drift Detection, and Escalation Signals
Trust boundaries are only real if they are observed after deployment. Monitoring should track which external sources were consulted, which tools were invoked, what data crossed the boundary, and whether the model attempted anything outside its normal function. Teams need alerting for boundary violations such as unexpected tool calls, data exfiltration patterns, approval bypass attempts, and model outputs that try to influence higher-privilege actions than the initiating user or workflow intended.
The hard part is that boundary drift often looks like normal productivity until it scales. A model that repeatedly asks for broader context, invokes an unapproved plugin, or converts untrusted input into a system action is signaling that its control envelope is too loose. That is why runtime telemetry, prompt and tool-call logging, and periodic red-team testing need to be part of the operating model, not a post-incident add-on.
Risk and Threat Considerations
LLMs that can read external data and call plugins create a direct route from untrusted content to privileged action. The main risk is not just bad answers, but instruction hijacking, data leakage, and overbroad backend access when the model treats hostile or malformed input as if it were trusted context.
Failure mechanism: An attacker, poisoned source, or compromised plugin response can steer the model into revealing sensitive data, invoking an unsafe tool, or bypassing intended approval flow because the boundary between interpretation and execution is too weak.
Impact: The result can be unauthorized system changes, credential or token exposure, unwanted disclosure from connected systems, or lateral movement through tools that were granted more authority than the use case justified.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF, NIST Zero Trust (SP 800-207) and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | GOVERN — Govern the AI Risk Management Process | LLM deployments need governance over trust boundaries and approvals. |
| MAP — Map Context and Potential AI Impacts | Mapping data sources, plugins, and actions clarifies where trust shifts occur. | |
| MANAGE — Manage AI Risks | Continuous monitoring and boundary enforcement are core AI risk treatments. | |
| Recommendation — Define boundary ownership and require governance checks before enabling tool access. Inventory each external source and plugin against its allowed data and action scope. Enforce runtime monitoring for unsafe tool calls, drift, and boundary violations. | ||
| NIST Zero Trust (SP 800-207) | SC-7 — Boundary Protection | Trust boundaries between model, inputs, and backend systems need explicit protection. |
| AC-6 — Least Privilege | Plugins and backend access should be restricted to minimal required authority. | |
| Recommendation — Place policy checks between external inputs, plugins, and privileged systems. Scope each plugin and credential to the smallest access set that still works. | ||
| CIS Controls v8 | 6 — Access Control Management | Plugin and backend permissions require least-privilege access management. |
| 8 — Audit Log Management | Runtime monitoring and traceability depend on usable logs of tool and data flows. | |
| Recommendation — Review and restrict every connector, token, and backend permission path. Log model prompts, tool calls, approvals, and denied boundary crossings. | ||
| OWASP Agentic AI Top 10 | A2 — Prompt Injection | External data sources can inject instructions that redirect model behaviour. |
| A3 — Tool Misuse | Plugins can turn model intent into unsafe privileged actions if poorly bounded. | |
| A4 — Overprivileged Agents | Plugins and agents often have more access than the workflow requires. | |
| Recommendation — Treat untrusted retrieved content as data only and validate before action. Constrain tools with explicit allowlists, approvals, and scoped execution rights. Reduce tool authority until a compromise cannot reach broader backend systems. | ||
Practitioner Guidance
What to verify: Verify that every plugin has an explicit permission profile, a defined data scope, and a failure mode that defaults to deny when context is missing or ambiguous. If a tool can reach production systems, treat its approval path as a control, not a convenience feature.
Decision rule: If a model action would be harmful when triggered by malicious external content, require deterministic validation outside the model before execution. If the action is reversible and low impact, you can automate more aggressively, but only when logs clearly show who approved it and what data influenced the decision.
Practitioner takeaway: The boundary should be designed so that untrusted data can inform the model, but never inherit authority from the model, because authority must come from policy, scope, and runtime controls, not from language understanding.
Related resources from NHI Mgmt Group
- How should security teams test LLMs that can access tools and external data?
- How should security teams implement AI governance in environments where developers use public LLMs and internal data sources?
- How should security teams handle security data ingestion when AWS logs are spread across Security Lake, CloudTrail, VPC Flow, and third-party sources?
- How should security teams handle vulnerability management when there are multiple global sources for CVE data?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
