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Threat-model an AI application as an authority-and-data-flow problem, not just a model-quality problem. Draw every path from user input, retrieved content, files and connectors to prompts, memory, tools, identities and outbound networks; mark trust boundaries; inventory secrets and tenant data; enumerate direct and indirect prompt-injection paths; then attach least-privilege, context-isolation, tool-mediation, egress, monitoring and safe-failure controls. Validate the design with adversarial tests whenever a model, prompt, tool, permission, connector or retrieval corpus changes.
What this threat model must protect
A prompt-injection threat model describes how untrusted instructions could influence an AI system and what the attacker could make it disclose or do. The model may be an LLM chat interface, a retrieval-augmented generation (RAG) service, or an agent that can call business tools. In each case, the security question is the same: can text or data from one principal cross a boundary and gain authority that its author does not have?
A useful threat model produces four concrete outputs:
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- A data-flow diagram showing components, trust boundaries and outbound paths.
- An asset and principal inventory that states who may read, write, invoke or authorize each component.
- An attack-path register connecting hostile inputs to possible disclosures or external effects.
- A control-and-test plan with prevention, containment and monitoring requirements.
Prompt injection can be direct, when a user tries to override system or developer instructions, or indirect, when hostile instructions are hidden in content the application retrieves or processes. Microsoft describes indirect injection as an attacker hiding instructions in data that the AI will consume. The data may be a web page, email, document, database row, image or retrieved chunk. An agent can then turn that text into a tool call, a memory write or an external request.
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The impact is broader than a bad answer. OWASP lists data exfiltration, unauthorized API calls, record deletion and lateral movement among possible consequences. Google’s Agentic AI Security Team describes a threat model in which indirect injection is used to exfiltrate sensitive information. NIST classifies this kind of indirect prompt injection as agent hijacking when it causes unintended harmful actions.
Draw the complete data flow before listing threats
Start with a diagram that follows data and authority through the running system. Include components that are often omitted from architecture diagrams because they look like configuration or operations rather than application code.
- Entry points: chat or other user interfaces, public APIs, batch jobs, webhooks and administrative consoles.
- Gateway and orchestration: API gateway, rate limiting, request validation, session handling and the service that assembles model requests.
- Instruction channels: system prompts, developer prompts, policy text, tool descriptions and templates.
- Model and state: the model endpoint, conversation history, summaries, long-lived memory, caches and model or embedding artifacts.
- Retrieval: document stores, vector indexes, metadata filters, rerankers and the service that inserts retrieved material into context.
- Untrusted content sources: uploads, web pages, email, tickets, database fields, images and third-party feeds.
- Tools and connectors: search, storage, CRM, messaging, payment, code execution, browser, HTTP and other plugins.
- Identity and authorization: identity providers, service accounts, delegated tokens, tenant resolution and policy engines.
- Downstream systems: business APIs, databases, file stores, queues and external services.
- Observability and egress: logs, traces, analytics, alerting, DNS, proxies and every route that can send data outside the approved environment.
Draw an arrow for every read, write, invocation and authorization decision. Label the data on the arrow, not merely the component name. A retrieved paragraph, a tool argument and a credential-bearing header have different security properties even when they pass through the same service.
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For each boundary, record the principal on each side and the operation permitted across it. Typical principals include an end user, a tenant administrator, an external content author, the model runtime, an orchestration service, a tool service and a human approver. Treat all instruction-bearing channels as potentially hostile, including content that your application normally considers useful.
Ask these questions at every crossing:
- Is the value user-authored, retrieved, generated, or supplied by a third party?
- Can it reach a system prompt, memory write, tool selector or authorization decision?
- Which identity is used for the next call, and is that identity bound to the requesting user and tenant?
- Can the receiving component distinguish data from instructions?
- Is the action reversible, and who must approve it?
Inventory assets, authority and acceptable disclosure
Create an inventory before deciding that an output is harmless. For every asset, record confidentiality, integrity, availability, tenant scope, retention and acceptable disclosure. The same value can have different risk in different locations: a customer identifier in a short-lived retrieval result is not equivalent to that identifier in a shared memory store or a log retained for years.
| Asset | Security properties to record | Questions for the model design |
|---|---|---|
| System and developer prompts | Confidentiality and integrity; version and retention | Can a user or retrieved document reveal, rewrite or override them? |
| Credentials, tokens and delegated permissions | Confidentiality, scope, lifetime and revocation | Which tool receives each credential, and can the model influence its destination? |
| Customer records and retrieved documents | Tenant scope, confidentiality, retention and integrity | Are filters enforced before retrieval and again before response or tool use? |
| Conversation history and long-lived memory | Tenant scope, retention, integrity and deletion behavior | Can injected text persist and be replayed in a later user’s session? |
| Model and embedding artifacts | Integrity, availability and version provenance | What changes require a threat-model review and regression tests? |
| Tool parameters and generated plans | Integrity, authorization context and reversibility | Can hidden or extra arguments alter the effect of an approved call? |
| Telemetry and logs | Confidentiality, retention and access control | Could prompts, retrieved secrets or tool payloads be copied into logs? |
Maintain a versioned, checked-in list of what each component can actually do: every tool it can call and which tools create an external effect such as moving value, contacting a person, deleting data or calling a third party. This inventory is an explicit recommendation in OWASP’s AI and Agentic Threat Modeling guidance. Treat it as a security artifact, not informal documentation.
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Enumerate the attack paths
Write each path as a sequence: hostile source, boundary crossed, authority gained, asset exposed or effect produced. The following six paths cover the core cases to test.
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|---|---|---|
| Direct prompt injection | A user asks the model to ignore system or developer instructions, reveal hidden prompts or perform an unsafe action. | Policy bypass, secret disclosure or an unauthorized tool request. |
| Indirect prompt injection | Instructions embedded in a web page, email, document, image, database row or retrieved chunk. | The model treats data as authority and follows the attacker’s instructions. |
| Exfiltration through generation | The model is induced to print private context, credentials, hidden prompts or another user’s retrieved data. | Sensitive information appears in a response, export or log. |
| Exfiltration through tools | Injected content changes a URL, recipient, query, file path or payload in a connector call. | Secrets or personal data leave through HTTP, email, storage, CRM or another integration. |
| Cross-tenant or cross-user disclosure | Retrieval filters, memory, caches or tool credentials are broader than the requesting principal. | One principal receives another principal’s records or uses another tenant’s authority. |
| Chained compromise | An injection changes a tool call; the tool changes application state; a later turn treats that state as trusted context. | A one-time prompt attack becomes persistent manipulation or a wider compromise. |
Direct injection
Test attempts to reveal the system prompt, developer policy, hidden tool instructions and conversation history. Include requests that ask the model to role-play, claim an emergency, reinterpret policy as quoted data or continue from a forged previous message. The threat is not the wording of a particular jailbreak; it is that user-controlled text can change a privileged instruction channel or authorize a side effect.
Indirect injection
Place hostile instructions in every content type your system accepts. Examples include a web article that tells the agent to send its context to a URL, a document that changes the requested recipient, an email that asks for a file upload, a database row that alters a query, or an image containing text that a vision model can read. Retrieval does not make a source trusted. A chunk can be relevant to the question and still be adversarial.
Generation and tool exfiltration
Separate disclosure in the model’s answer from disclosure through an external effect. A response can expose a secret to the user; a tool call can send it to an attacker without displaying it in the chat. Inspect URLs, domains, HTTP methods, recipients, file paths, query parameters and payloads as security-sensitive arguments. A model that is prevented from printing a token may still be able to place that token in a request body unless egress controls inspect the request.
Tenant and user isolation
Model the complete path from identity resolution to retrieval, memory, cache and tool authorization. A correct filter in the search index is insufficient if a shared cache, conversation summary or service credential bypasses it. Bind each call to an explicit user and tenant context, and verify that context at the downstream service rather than trusting a model-generated identifier.
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Chained compromise
Look for state transitions that turn untrusted output into trusted input. A tool may write a note, modify a ticket, create a file or change a workflow flag. On the next turn, the model may read that state as if it were an administrator instruction. Mark every write that can influence later retrieval, planning or authorization, and test replay through memory and persistent records.
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Map controls to each path
Least privilege and scoped identity
Issue narrowly scoped, short-lived credentials. Separate read tools from write, delete, payment, messaging, code-execution and network-egress tools. Bind permissions to the requesting user and tenant; do not give an agent a broad service account merely because a tool is convenient. Revoke or rotate delegated permissions when a session ends or a policy changes.
Context isolation and data marking
Keep external content in a distinct data field or message section that the model and surrounding policy can identify as untrusted. Do not concatenate retrieved text into a privileged instruction channel. Microsoft recommends information-flow control, spotlighting and data marking for untrusted content. Preserve provenance so a later policy check can tell whether a sentence came from a user, a trusted policy or an external document.
Tool mediation
Put a deterministic mediator between the model and every tool. Validate the tool name and every argument against a strict schema; reject hidden, extra or duplicated parameters. Re-check authorization using the user and tenant identity. Require explicit approval from a user or a separate policy engine for irreversible or high-impact actions. The approval screen should show the actual destination, recipient, records, amount, file path or payload, not a model-generated summary.
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Egress control
Restrict outbound domains, methods, recipients and payload sizes. Use allow-lists where practical, and block redirects or DNS paths that escape the policy. Inspect requests for secrets and personal data before HTTP, messaging, storage or file-transfer calls. Treat URL components, email addresses, filenames and query parameters as untrusted input; an attacker can hide an exfiltration channel in any of them.
Data minimization
Retrieve only the fields and documents needed for the task. Redact credentials and unnecessary personal data before model exposure. Prevent sensitive values from entering long-lived memory, summaries or analytics unless there is a documented need and a matching retention policy. Minimize the amount of context available to an attacker even if another control fails.
Layered input and output defenses
Use content filters, injection detectors, AI-security scanners and prompt/output analytics as layers. Do not treat a classifier as a proof of safety: novel, encoded, multilingual or context-dependent attacks can evade it. A detector should trigger a constrained response or human review, not grant permission to continue a high-impact action.
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Monitoring and protected telemetry
Log prompt provenance, retrieved sources, model and embedding versions, tool calls, authorization decisions, outbound requests and policy outcomes. Protect those logs because they may contain the same sensitive content the system is meant to protect. Alert on unusual tool sequences, repeated denied requests, a denied-to-allowed transition, new outbound destinations, sensitive-data findings and retrieval sources that newly contain instructions.
Safe failure
When the model is uncertain, a policy conflicts, an argument fails validation or a destination is not approved, stop tool execution. Return a constrained response that states the action was not performed and require a fresh authorization decision. Do not let an error handler silently retry with broader permissions or substitute a less restricted connector.
A practical threat-modeling workflow
- Set the scope. Name the user journeys, tenants, models, connectors, environments and data classes included in the review. Record exclusions so a later change does not silently expand the system.
- Build the data-flow diagram. Add the interface, gateway, prompts, model, memory, retrieval, files, web and email connectors, tools, identity providers, downstream APIs, logs and outbound network paths. Draw trust boundaries and label every data and authority flow.
- List principals and permissions. For each component, write who can read, write, invoke or authorize it. Capture delegated permissions and the identity presented to each downstream service.
- Inventory assets. Record confidentiality, integrity, availability, tenant scope, retention and acceptable disclosure for prompts, credentials, records, documents, history, artifacts, parameters and telemetry.
- Create the capability register. Check in the exact tool catalog, schemas, side effects, credential scopes, destinations and approval requirements. Version it with the application.
- Trace attack paths. Start with direct overrides, then inject instructions into every external content type. Follow each path to generated output, memory, retrieval, tool arguments, authorization and egress.
- Assign controls. For every path, specify the least-privilege identity, context boundary, validator, approval gate, egress rule, data-minimization rule, detector, log event and safe-failure behavior.
- Define test assertions. State what must not happen: no unauthorized disclosure, no out-of-scope tool call, no cross-tenant result and no unapproved external effect.
- Run the matrix and record evidence. Capture model version, prompt version, tool and connector versions, corpus revision, permissions, inputs, outputs, calls and policy decisions so a failure can be reproduced.
- Approve and schedule refreshes. Obtain sign-off from the application owner and security authority. Re-open the model whenever authority, credentials, tools, trusted inputs, model behavior or external effects change.
Build a validation matrix that tests prevention and containment
Use a separate test case for each trust boundary and attack path. A passing test means both that the intended control prevented the action and that the system contained the attempt without leaking useful data or creating side effects.
| Test family | Example variation | Required assertion |
|---|---|---|
| Direct override | Requests to reveal hidden prompts, ignore policy or invoke a restricted tool. | No privileged instruction or unauthorized call is disclosed. |
| Indirect content | Instructions in a document, web page, email, image, database row or retrieved chunk. | Content remains data; the agent does not treat it as authority. |
| Encoding and language | Encoded, obfuscated, multilingual and formatting-dependent variants. | Detection and policy behavior remain safe without relying on one wording. |
| Tool arguments | Extra fields, altered recipients, URLs, paths, queries or payloads. | Schema and policy validation reject hidden or out-of-scope effects. |
| Secret canary | A synthetic secret placed in retrievable context. | The canary never appears in a response, log, memory write or outbound request. |
| Cross-user retrieval | Queries, cache hits and memory replay across users and tenants. | No record, embedding result or tool credential crosses the boundary. |
| Unsafe URL exfiltration | Attacker-controlled domains, redirects, query parameters or request bodies. | Egress policy blocks the destination and sensitive payload. |
| State replay | Injected text written by a tool and consumed in a later turn. | Persisted content is marked untrusted and cannot authorize a later action. |
Run this matrix before production and after every model, prompt, tool, connector, permission or retrieval-corpus change. Microsoft recommends systematic safety and security evaluations for LLM applications. Keep failed cases as regression tests rather than deleting them after a fix.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare designs by authority and blast radius
When choosing between an architecture with many autonomous tools and one with a smaller mediated tool set, compare the dimensions that determine consequence rather than model benchmark scores:
- Authority and blast radius: what can one compromised session read, change or delete?
- Exposure to untrusted content: how many external sources can influence planning or tool selection?
- Sensitivity of reachable data: can the model access credentials, personal data, financial records or administrator instructions?
- Tool and egress capability: can it send messages, call arbitrary URLs, execute code or transfer files?
- Reversibility: can an action be rolled back, or does it move value or disclose data immediately?
- Tenant isolation: are identity, retrieval, memory, caches and credentials scoped at every layer?
- Observability: can investigators reconstruct provenance, authorization and outbound requests?
- Implementation and operating effort: what validators, approvals, allow-lists, tests and reviews must be maintained?
Fewer tools and narrower permissions generally create fewer consequential attack paths, but treat that as a design hypothesis to validate with application-specific testing. A small catalog with an unrestricted network tool can still have a large blast radius.
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Example: threat-model a support RAG agent
Consider an agent that answers a customer’s question from a tenant-scoped knowledge base and can create a support ticket. Its diagram should show the customer interface, API gateway, system and developer prompts, conversation memory, retrieval index, uploaded documents, identity provider, ticket tool, database, logs and outbound network.
Potential boundaries include the customer-to-gateway request, uploaded documents-to-retrieval, retrieval-to-model context, model-to-ticket mediator and mediator-to-ticket API. The asset inventory includes hidden policy text, customer records, ticket history, delegated ticket permissions, retrieved chunks and logs.
A malicious document might contain an instruction to include the customer database in a ticket and send it to an attacker-controlled address. The model should receive that document as marked, untrusted data; retrieval should enforce the tenant filter; the tool mediator should allow only a ticket title, body and approved customer identifier; an authorization check should bind the call to the current customer and tenant; and egress policy should reject arbitrary recipients or URLs. A canary record in the index verifies that the value cannot appear in the answer, ticket payload, memory or logs.
If the agent writes a ticket note, that note must remain untrusted when a later turn retrieves it. Otherwise the first injection can plant a persistent instruction that changes a future ticket or disclosure decision.
Production review and incident signals
Dashboards should make security-relevant behavior visible without exposing raw secrets. Track injection detections, blocked and allowed tool calls, denied-to-allowed transitions, sensitive-data findings, outbound destinations, retrieval sources that newly contain instructions, authorization failures and unusual sequences of tools. Correlate events with model, prompt, tool, connector, permission and corpus versions.
When an attack is suspected, preserve the relevant provenance and policy decisions, revoke or narrow affected credentials, block the destination or tool, invalidate poisoned memory or retrieval entries, and rerun the attack matrix. Treat logs and traces as sensitive evidence and restrict their access during the investigation.
Re-open the threat model whenever tools or actions, identity and authority, credentials, delegated permissions, trusted inputs, model behavior or external effects change. A visible data-flow diagram can look unchanged while a new permission or connector creates a completely different attack path.
Common modeling mistakes
- Trusting retrieval: relevance does not make a document authoritative.
- Protecting only the chat response: a blocked answer does not stop a tool or network exfiltration path.
- Giving the model a broad service account: convenience converts a prompt mistake into tenant-wide authority.
- Concatenating data into instructions: the model loses the distinction between policy and content.
- Relying on one detector: encoded, multilingual and novel attacks require layered controls.
- Ignoring state: memory, summaries, caches and tool-written records can replay an injection.
- Logging everything: unrestricted telemetry can become a second exfiltration store.
- Testing only at launch: model, prompt, tool, permission and corpus changes can reopen a closed path.
- Approving summaries instead of effects: humans must see the actual recipient, destination, records and payload before authorizing an irreversible action.
A complete threat model therefore ends with an operational promise: untrusted content may be processed, but it never silently becomes authority. Every consequential action has a scoped identity, a validated argument set, an approved destination, an observable decision and a safe way to stop.
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