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Extend an AI system safely by treating every tool, plugin, connector, and retrieval index as a new privileged interface. Give each extension only the data and actions required for one task, enforce authorization in application code and downstream services rather than in model instructions, and test the entire path from ingestion to final action.
This approach matters because a model can choose a tool using incomplete, manipulated, or simply wrong information. OWASP’s 2025 LLM06: Excessive Agency identifies three recurring causes: excessive functionality, excessive permissions, and excessive autonomy. A secure design limits all three while preserving the capability users actually need.
What changes when you add an extension
An unextended language model mainly produces text from its prompt and learned parameters. A connected model can read a document repository, query a database, call an API, send a message, modify a record, or trigger an operational workflow. The extension therefore changes the system’s authority, not just its feature list.
There are two separate security questions:
- What can the extension reach? This includes systems, records, files, network destinations, and secrets.
- What can the model cause it to do? This includes reading, writing, deleting, transmitting, approving, or initiating an irreversible action.
Model instructions are not an authorization boundary. Prompt injection can appear in a user message, a retrieved document, an email, a web page, or an API response. A model can also misinterpret a legitimate request. If the model is allowed to decide whether an action is permitted, an attacker only needs to influence that decision.
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The UK National Cyber Security Centre’s secure-AI guidance describes the objective as systems that “work without revealing sensitive data to unauthorised parties.” In practice, that means each extension must have an explicit owner, purpose, identity, data boundary, action boundary, and revocation path.
Start with a task and data boundary
Before selecting a plugin or connector, write down the smallest useful task. “Help employees with documents” is too broad to authorize safely. “Answer a signed-in employee’s questions from documents in that employee’s project workspace, without sending content outside the organization” is specific enough to design controls around.
Record these decisions for every use case:
- User outcome: the result the system must produce.
- Data classes: for example, public, internal, confidential, regulated, or secret. Use the organization’s existing classification scheme rather than inventing a universal one.
- Permitted actions: read, search, create, update, delete, send, approve, or execute. Separate read access from write access.
- Authority: the human user, service account, or delegated identity whose permissions should govern access.
- Prohibited flows: destinations, records, or operations that must never be reached.
- Retention and disclosure rules: where prompts, retrieved passages, tool arguments, and outputs may be stored or transmitted.
Write the boundary as a testable statement. For example: “A support agent may retrieve case records assigned to the signed-in agent’s team, may draft a reply, and may not send, close, export, or retrieve records from another team.” This statement becomes the basis for identity checks, tool schemas, tests, and monitoring.
Inventory every integration before enabling it
Maintain an inventory that covers first-party code, open-source packages, hosted APIs, model providers, vector databases, browser or shell tools, and data pipelines. An integration that appears to be read-only may still transmit prompts, metadata, telemetry, or error details to a provider.
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| Inventory field | What to record | Why it matters |
|---|---|---|
| Provider and component | Vendor or project, version, hosting location, and internal owner | Identifies update, support, and assurance responsibilities |
| Functions | Each exposed operation, not just the product name | Prevents a broad package from hiding unnecessary capabilities |
| Identity and credentials | Human delegation, service account, token scope, secret location, and expiration | Shows whose authority is being used and how it can be revoked |
| Reachable systems | Databases, folders, queues, URLs, and network segments | Defines the real blast radius of a compromised or misused tool |
| Data movement | Fields sent, fields returned, logging destinations, retention, and geographic boundary | Exposes privacy, confidentiality, and residency risks |
| Write and transmit powers | Whether it can create, edit, delete, send, publish, or execute | Separates reversible retrieval from high-impact actions |
| Change and shutdown path | Update process, rollback version, owner, and emergency disable switch | Allows rapid response when a provider, model, or connector changes |
NIST’s draft Generative AI profile highlights third-party transparency and risk management; NCSC guidance also covers systems built on other providers’ tools and services. Ask providers for current documentation on data use, isolation, logging, incident notification, subcontractors, and deletion. Treat marketing statements as unverified until they are supported by contractual terms or technical evidence.
Choose an architecture by risk, not fashion
No deployment pattern is automatically safe. Compare viable designs on data exposure, permission granularity, pipeline control, provider assurance, and operational burden.
| Design option | Data exposure | Permission granularity | Pipeline control | Provider assurance | Operational burden |
|---|---|---|---|---|---|
| In-process connector | Data stays close to the application, but connector code shares the application’s trust boundary | Can be precise if functions and credentials are narrowly scoped; broad SDK access is dangerous | Good visibility when the application validates every call | Mostly your responsibility, including dependency and supply-chain review | Lower initial integration work; your team owns patches and failures |
| Brokered tool gateway | Requests pass through a controlled service that can redact or block fields | Strong opportunity for per-user, per-task, and per-action policy | Centralized logging, validation, rate limits, and approvals | Depends on gateway operator and downstream providers | More components to operate, but policy changes are centralized |
| Isolated retrieval service | Only selected passages cross into the model context | Can enforce document- and user-level filters before retrieval results leave the service | Allows separate controls for ingestion, indexing, retrieval, and response handling | Depends on vector-store and embedding-service practices | Requires index maintenance, synchronization, and access-policy testing |
| External provider API | Prompt, retrieved content, and metadata may cross an organizational boundary | Limited by the provider’s identity, tenant, and tool controls | Less direct control over internal processing and telemetry | Requires due diligence, contracts, service-level terms, security evidence, and subprocessor review | Less infrastructure to run; greater dependency and change-management work |
For higher-impact actions, a brokered gateway or isolated service often makes policy and observation easier. That is a design trade-off, not a guarantee. The correct choice depends on the sensitivity of the data, the reversibility of actions, provider commitments, and the team’s ability to operate controls continuously.
Apply least privilege at two levels
Least privilege must constrain both the extension interface and the downstream service account. Removing a function from the model’s tool list is useful, but insufficient if the remaining function can use a credential with broad permissions.
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Narrow the extension interface
- Expose a task-specific function such as
search_assigned_cases, not a generic database query tool. - Use typed arguments with bounds on identifiers, date ranges, result counts, and destination domains.
- Separate read, draft, send, update, delete, and administrative operations into different functions.
- Remove shell execution, arbitrary URL fetching, unrestricted code execution, and open-ended file access unless the task demonstrably requires them.
- Return the minimum fields needed for the next model step. Do not return full records when a status or aggregate is enough.
Constrain downstream credentials
- Use a dedicated identity per integration and environment; do not reuse a human administrator token.
- Grant only the required tables, folders, endpoints, verbs, and records.
- Bind access to the requesting user or service context where the downstream system supports delegation.
- Set short lifetimes, rotate secrets, and provide an immediate revocation mechanism.
- Use network controls to restrict reachable hosts and egress destinations, not just application-level checks.
A tool should fail closed. If a user identity, policy decision, document filter, or destination check is missing, return a denial rather than retrying with a broader credential or alternate data path.
Keep authorization outside the model
Use the model to interpret intent and propose a tool call; use deterministic code and the connected service to decide whether that call is allowed. A practical request path is:
- Authenticate the user and establish a trusted identity context.
- Ask the model for a structured action proposal that matches an allow-listed tool schema.
- Validate argument types, ranges, identifiers, destination, and requested operation in application code.
- Evaluate policy against the user, task, data classification, resource ownership, and current state.
- Require independent confirmation for high-impact or irreversible actions.
- Invoke the downstream service with a narrowly scoped credential and an audit correlation ID.
- Validate the returned data before placing it in the model context or showing it to the user.
Confirmation should be meaningful, not a decorative “Are you sure?” prompt generated by the same model. Show the exact target, records, fields, destination, and consequence. For payments, deletion, publication, permission changes, or external messages, require a human or separate policy service to approve the final operation.
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Use allow lists for tools and destinations. Reject unknown functions, extra arguments, malformed identifiers, and attempts to smuggle instructions inside fields. Treat tool output as untrusted input too: an API response can contain text designed to manipulate the next model step.
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Secure RAG as a complete pipeline
Retrieval-augmented generation does not remove the need for access control. The OWASP RAG Security Cheat Sheet puts it plainly: “RAG does not reduce risk — it redistributes it across the data pipeline, creating new attack surfaces at every stage from ingestion to generation to output.” Protect each stage separately.
1. Ingestion
- Authenticate and authorize the source that submits each document.
- Capture owner, tenant, classification, retention, and allowed audience as metadata.
- Scan files for malware and unsupported active content before processing.
- Reject poisoned, duplicated, stale, or untrusted sources according to documented rules.
- Record deletion and permission changes so the index can be corrected rather than retaining a forgotten copy.
2. Embedding creation
- Send only the approved text and metadata to the embedding service.
- Decide whether the provider may retain inputs; do not assume that an embedding is harmless because it is not readable prose.
- Keep tenant and classification boundaries in the data model so vectors cannot be searched across them accidentally.
- Version embedding models and pipelines; a model change can alter retrieval behavior and require revalidation.
3. Vector storage
- Enforce storage authentication, encryption, backups, and deletion controls.
- Apply tenant, user, group, and document-level filters at query time.
- Prevent direct client access when the application is supposed to mediate retrieval.
- Audit searches, returned identifiers, and administrative exports.
4. Retrieval
- Derive filters from a trusted identity and policy context, not from model-generated text.
- Apply authorization before returning passages, including when a result is found through metadata or hybrid search.
- Limit result count, chunk size, and cumulative context to reduce accidental disclosure.
- Handle denied, empty, stale, and failed retrieval explicitly; never fall back to a broader index silently.
5. Context construction and generation
- Clearly delimit retrieved content from system instructions and user requests.
- Mark source and trust metadata so the model is less likely to treat a document instruction as an administrator command.
- Do not include secrets, unnecessary identifiers, or unrelated passages merely because they improve apparent fluency.
- Use a model context policy that forbids executing instructions found in retrieved material unless a separate, authorized workflow approves them.
6. Output validation
- Check generated citations, identifiers, links, and structured fields against the authorized source set.
- Detect attempts to disclose hidden prompts, credentials, or content outside the requester’s scope.
- Apply business rules before output is sent to another system or presented as a definitive decision.
- For regulated or high-impact responses, route to a qualified reviewer rather than relying on confidence wording.
7. Downstream agent use
- Reauthorize every tool call; do not treat a retrieved passage as permission.
- Carry the original user identity and policy decision through the chain.
- Set hop, time, and action limits so one mistaken response cannot trigger an unbounded workflow.
- Avoid caching highly sensitive retrieved content unless the cache has equivalent access controls, isolation, retention, and deletion behavior.
Review providers and third-party integrations
Adding an external model, embedding API, vector service, browser tool, or monitoring platform creates a provider boundary. NIST’s draft Generative AI profile identifies possible privacy, intellectual-property, and security risks from third-party integrations. Its risk-management examples include due diligence, service-level agreements, software bills of materials, and SSAE reports where applicable; the right evidence depends on the provider and use case.
Ask and document:
- What inputs, outputs, metadata, and telemetry are retained, for how long, and in which jurisdictions?
- Are customer inputs used for training, evaluation, abuse monitoring, or human review?
- How are tenants, encryption keys, administrators, subprocessors, and support access separated?
- Can the provider delete data on demand and prove deletion from active systems and backups?
- What security incident, vulnerability, and material-change notifications are promised?
- Which model, embedding, connector, and dependency versions are covered by the service?
- Can you export data, logs, configurations, and indexes if the service is unavailable or the relationship ends?
Do not treat a certificate, questionnaire, or contract as a substitute for architecture. Verify that the provider’s documented controls match the data flow you actually operate, including debug logs, failed requests, support tickets, and backups.
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Security tests should exercise the complete path, not only the model’s refusal behavior. Use test identities and synthetic sensitive data where possible.
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- Attempt to retrieve another user’s or tenant’s documents through direct search, natural-language requests, alternate identifiers, and malformed filters.
- Place a malicious instruction in a document, email, or API response and check whether it can influence a tool call.
- Try to make a read-only task invoke send, edit, delete, shell, or unrestricted URL functionality.
- Remove or deny a permission and verify that the system fails closed rather than retrying with a service-wide credential.
- Force an empty, stale, timed-out, or corrupted retrieval and confirm that no broader fallback index is used.
- Inspect logs for prompts, retrieved passages, arguments, and outputs that should have been redacted.
- Replay a previous approval with a different user, resource, destination, or changed amount and confirm that authorization is reevaluated.
- Test rate, token, recursion, and time limits so a model cannot create an unbounded chain of calls.
These scenarios are practical tests derived from the access-control, integrity, and pipeline risks identified in OWASP’s RAG guidance. Record expected and actual decisions, evidence, and remediation owners. Retest after changing a model, connector, permission, provider, index, or data source.
Monitor use and prepare for failure
Log enough to reconstruct a decision without creating a second sensitive-data repository. A useful event includes timestamp, authenticated identity, application and model version, tool name, policy decision, resource identifiers, destination, approval record, latency, and outcome. Hash or tokenize content where full payloads are unnecessary, and restrict log access.
Alert on patterns such as:
- Sudden increases in denied retrievals or cross-tenant filter failures.
- New destinations, functions, scopes, or providers appearing without a change record.
- Repeated attempts to access secrets, system prompts, administrative endpoints, or large document ranges.
- Tool calls that do not match the user’s task, approval, or normal volume.
- Changes in provider retention, model version, connector behavior, or index synchronization.
Define an incident playbook before launch: disable the affected tool, revoke credentials, preserve relevant audit records, identify exposed data, notify responsible stakeholders, rebuild or purge compromised indexes and caches, and restore service with a narrowed policy. A kill switch that depends on the same failing model path is not an adequate emergency control.
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Use a secure-development lifecycle
NIST SP 800-218A, finalized in July 2024, adds AI-specific practices to the Secure Software Development Framework for system producers and acquirers. Apply its lifecycle idea to extensions rather than treating security as a one-time review.
- Plan: define the task boundary, data classes, identities, prohibited actions, provider requirements, and success criteria.
- Design: choose the architecture, tool schemas, policy points, approval path, data-flow controls, and failure behavior.
- Implement: build narrow functions, validation, secrets handling, logging, redaction, and tenant-aware retrieval filters.
- Verify: run authorization, prompt-injection, data-leakage, integrity, abuse, and resilience tests with representative identities.
- Release: approve provider evidence, rollback plans, monitoring, incident ownership, and documented residual risk.
- Operate: review alerts, access, logs, provider notices, model changes, index freshness, and user feedback.
- Reassess: repeat the review whenever permissions, connectors, models, providers, or data sources change.
NIST’s Cybersecurity for Open AI Systems project has also described proposed implementation-focused control overlays; material from that project should be treated as development work rather than a final standard. Similarly, initial public draft material from NIST AI 600-1 can inform planning but should not be presented as a final requirement.
A compact approval checklist
Do not approve an extension until the owner can answer yes to each applicable question:
- Is the user outcome narrow and the data boundary written down?
- Is every function documented, necessary, and separately permissioned?
- Are downstream credentials limited to the required user, records, verbs, and destinations?
- Does application or service code enforce authorization independently of model instructions?
- Are high-impact actions independently approved and reversible where possible?
- Are ingestion, embeddings, vector storage, retrieval, context construction, generation, output validation, and downstream calls controlled?
- Have provider retention, training use, subprocessors, incident terms, and exit options been reviewed?
- Can the team observe decisions, detect abuse, revoke access, and disable the extension quickly?
- Have cross-user retrieval, malicious content, denied access, fallback behavior, and replay scenarios been tested?
- Is there an owner and a scheduled review for future model, connector, permission, and data-source changes?
If any answer is no, reduce the extension’s authority or delay deployment until the missing control has an accountable owner. The safest extension is not the one with the most impressive capability; it is the one whose authority is explicit, narrow, observable, and easy to revoke.
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