Should embeddings and vector indexes be treated as sensitive?

Yes. Embeddings, metadata, vector indexes, backups and similarity-query interfaces should be protected as data assets because they can expose document existence, relationships, restricted themes or information through weak access controls and probing.

When should a RAG system be security tested again?

Retesting should follow material changes to sources, connectors, parsers, chunking, embedding models, indexes, metadata, authorization, prompts, language models, tools, memory, caching or deletion workflows.

Are the 12 controls an official OWASP or NIST checklist?

No. The 12 controls are Think Unlimited Research's practical grouping for threat modeling. They are informed by cited OWASP and NIST resources but are not an OWASP or NIST certification.

Think Unlimited Research · Security Report · 2026

RAG Security Threat Model 2026: 12 Controls for Private Retrieval Systems

Q: What is a RAG security threat model?

A RAG security threat model maps the sources, ingestion pipeline, parsers, embeddings, vector storage, authorization controls, retrieval process, model context, outputs, tools, logs and lifecycle operations that can affect a private AI knowledge system.

Q: Where should access authorization be enforced?

Authorization should be enforced before restricted passages enter the model context. Verified identity, tenant and resource permissions should be applied by deterministic retrieval and storage controls.

A practical framework for protecting the complete path from document ingestion and vector retrieval to model context, generated answers, connected tools and lifecycle evidence. It is written for organizations building private AI knowledge systems in Lebanon and the wider region, without claiming local incident statistics that have not been independently verified.

Review the 12 controls Open the security owner page Explore RAG implementation

Published 4 August 2026

Framework 12 practical controls

Scope Private RAG and AI knowledge systems

Research owner Think Unlimited Research

Research position

RAG security is a system problem, not a prompt problem.

A retrieval-augmented generation application joins several components that were often designed and governed separately: source repositories, synchronization services, parsers, chunking logic, embedding models, vector stores, metadata, identity systems, ranking, prompt assembly, language models, tools, memory, logs and deletion workflows.

Important distinction: the twelve controls below are Think Unlimited Research’s practical grouping. They are informed by the official OWASP and NIST references listed in this report, but they are not presented as an official OWASP checklist, NIST certification or universal compliance standard.

Threat path

Follow the evidence from the source to the final action.

Security validation should trace exactly how content moves through the system and where identity, trust and authority may be lost.

01Sources and connectors

02Parsing and transformation

03Chunks and embeddings

04Retrieval and authorization

05Model context and answer

06Tools, memory and evidence

Control framework

The twelve controls

Each control identifies the security objective and the evidence that an authorized assessment should be able to verify.

Source inventory and trust classification

Every indexed source should have an identified owner, approved purpose, trust level, data classification and synchronization path. Public websites, shared folders, uploaded files, internal drives and customer platforms should not inherit equal trust merely because a connector can reach them.

The threat model should identify who can add, alter, approve or remove information and whether an attacker can introduce content through a weak upload, compromised account, exposed API or overly broad connector.

Evidence: source register, owner, connector identity, approval status, classification, version and ingestion event.

Parser and document-transformation security

Documents may contain hidden text, comments, metadata, annotations, alternate text, embedded objects, malformed structures or instructions that are not obvious in the normal visual view. The security boundary begins before the vector database.

Supported file types should be normalized predictably. Unsupported or ambiguous structures should fail safely, and extracted content should retain enough provenance to explain where each passage originated.

Evidence: parser version, extracted text, rejected elements, source coordinates, transformation logs and controlled adversarial test files.

Identity-aware authorization before retrieval

Authentication at the chat interface does not prove that the retrieval layer is enforcing the same user, role, department, customer or tenant permissions. Authorization must be applied before restricted passages enter the model’s working context.

A broadly privileged service account must not silently replace the original user’s rights. Filters should use trusted identity attributes and fail closed when required metadata is absent, stale or contradictory.

Evidence: authenticated principal, evaluated policy, retrieval query, enforced filters and returned document identifiers.

Tenant and collection isolation

Multi-tenant systems must isolate chunks, embeddings, metadata, namespaces, caches, histories, exports, snapshots, background jobs and administrative tooling. A tenant name inside a prompt is not an enforceable security boundary.

Tenant context should be derived from the authenticated session rather than user-controlled text or request parameters. Global fallbacks and shared indexes require particularly careful testing.

Evidence: tenant-scoped identifiers, storage namespaces, query constraints, cache keys and cross-tenant negative tests.

Vector and embedding protection

Embeddings are numerical representations, but they should still be treated as protected data assets. Weak vector APIs, administrative access, snapshots, backups or repeated similarity probes can reveal document existence, relationships, sensitive themes or information about restricted collections.

OWASP’s vector and embedding guidance identifies risks including unauthorized access, data leakage, cross-context exposure, embedding inversion and data poisoning.

Evidence: vector API permissions, namespace controls, export restrictions, backup security, query telemetry and abnormal-probing alerts.

Indirect prompt-injection resistance

Retrieved material should be treated as untrusted data, not as an authoritative instruction channel. A document can attempt to imitate a system message, suppress citations, redirect an answer, request secrets or influence a connected tool without the user seeing the hidden instruction.

OWASP’s prompt-injection guidance states that RAG and fine-tuning do not fully mitigate prompt injection and includes a scenario where a modified repository document changes model output.

Evidence: controlled poisoned documents, preserved instruction boundaries, model behavior, requested actions and blocked tool calls.

Knowledge poisoning and source integrity

An attacker may aim to change what the application believes rather than extract protected content. Altered policies, procedures, prices, security guidance or product facts can produce persuasive but incorrect answers.

High-impact collections need controlled ownership, approval records, trusted-source weighting, version history and monitoring for unexpected additions, deletions or large semantic changes.

Evidence: source signatures, version history, approval chain, ingestion differences, ranking influence and response changes.

Context minimization and confidential-data control

The model should receive only the passages and metadata needed for the authorized task. Excess context increases exposure to providers, application logs, caches, monitoring systems, developers, support workflows and later model output.

Chunk size, overlap and context assembly should not combine unrelated classifications, versions, projects or tenants. Output filtering can help, but it should not be the primary protection for information the model never needed.

Evidence: final assembled context, passage count, token volume, redaction results, provider settings and data-retention path.

Citation integrity and source provenance

A displayed citation is useful only when it identifies the material that actually influenced the answer. Systems can cite the wrong version, combine multiple sources without explanation or present a trusted title while relying on an untrusted passage.

Important answers should preserve source identity, version, relevant passage, retrieval time and access context so a human reviewer can verify the claim.

Evidence: retrieved passages, generated claims, displayed citations, source versions and claim-to-source comparison.

Tool authority and consequential-action controls

Some RAG applications can send messages, update tickets, change records, generate financial workflows, modify infrastructure or trigger other business operations. Poisoned content must not become an instruction with inherited authority.

The tool layer should independently validate identity, authorization, argument boundaries, destination, limits and the relationship between the user’s request and the proposed action. High-impact operations may require meaningful human approval.

Evidence: selected tool, generated arguments, policy decision, approval record, execution result and rollback capability.

Monitoring, abuse resistance and incident evidence

Security teams need visibility into unusual retrieval, repeated probing, filter failures, cross-tenant attempts, connector changes, bulk exports, indexing anomalies, poisoned-source indicators and tool activity influenced by retrieved content.

Logging must also be minimized so it does not become an uncontrolled duplicate of every private passage. Rate limits, alerting and evidence preservation should support both containment and investigation.

Evidence: user and tenant identity, source and query identifiers, decision records, alert history, action trail and retention rules.

Lifecycle, deletion and regression testing

Removing a source document does not automatically prove that chunks, embeddings, caches, backups, summaries, conversation memory and generated artifacts have been removed. Permission changes and document replacements must propagate throughout the complete system.

Changes to parsers, chunking, embedding models, ranking, metadata, connectors, prompts, models, tools or memory can reopen previously corrected findings. Confirmed security requirements should become repeatable regression tests.

Evidence: deletion request, propagation events, residual searches, backup treatment, test results and controlled retesting records.

Framework mapping

How the research sources support the model

The mapping below describes the relationship without claiming that the twelve-control structure is an official reproduction of either framework.

Reference	Relevant security context	Controls informed
OWASP LLM01:2025 Prompt Injection	Direct and indirect instruction manipulation, including malicious content retrieved from a repository.	02, 06, 10 and 12
OWASP LLM08:2025 Vector and Embedding Weaknesses	Unauthorized access, leakage, cross-context exposure, embedding inversion and poisoning risks.	03, 04, 05, 07 and 11
OWASP data and model poisoning guidance	Manipulation of data sources, training or operational knowledge that can influence system behavior.	01, 02, 07 and 12
NIST AI RMF Generative AI Profile	Cross-sectoral risk management across governance, mapping, measurement and management activities.	All twelve controls

Assessment readiness

Minimum information to prepare before testing

Approved repositories and ingestion sources
User roles, tenants and permission rules
Parser, chunking and embedding configuration
Vector database, namespaces and metadata model
Model providers and retention settings
Connectors, credentials and synchronization ownership
Tools and actions influenced by generated output
Logging, monitoring and incident-response contacts
Deletion, caching, memory and backup behavior
Authorized accounts, environments and operating limits

Direct answers

Frequently asked questions

What is a RAG security threat model?

It maps the sources, ingestion pipeline, parsers, embeddings, vector storage, authorization controls, retrieval process, model context, outputs, tools, logs and lifecycle operations that can affect a private AI knowledge system.

Does RAG prevent prompt injection?

No. Retrieved documents can contain malicious or misleading instructions. The system still needs source governance, instruction separation, constrained tools, authorization and independent validation.

Where should access authorization happen?

Before restricted passages enter the model context. Verified identity, tenant and resource permissions should be applied by deterministic retrieval and storage controls.

Are embeddings and vector indexes sensitive?

They should be treated as protected data assets. Embeddings, metadata, indexes, backups and similarity interfaces can expose document existence, relationships or restricted themes when surrounding controls are weak.

When should the system be retested?

After material changes to sources, connectors, parsers, chunking, embeddings, indexes, metadata, authorization, prompts, models, tools, memory, caching or deletion workflows.

Are these twelve controls an official certification?

No. They are Think Unlimited Research’s practical grouping, informed by the cited OWASP and NIST resources. They are not an OWASP or NIST certification and do not replace legal, regulatory or contractual review.

Primary references

Official sources

Connected authority

Research explains the risks. The security and implementation owners define the operational path.

Use the Cyber page for an authorized RAG security assessment. Use the Wolf page for private knowledge-system design and implementation context.

RAG Security Assessment RAG Systems Lebanon