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A defensible pre-launch threat model treats the deployed AI system—not just its model—as the unit of analysis. Define the intended use and affected people, map data and trust boundaries, identify credible attack and failure scenarios, test controls in a representative environment, and record residual risk, owners, launch conditions, and the person accountable for the decision. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes this work as Govern, Map, Measure, and Manage; OWASP’s generative-AI guidance adds application-specific tests for systems that use language models, retrieval, or tools.
What an AI threat model must cover
An AI threat model is a structured explanation of how a system could be misused, attacked, fail, or cause harm, and how the team will reduce or contain those outcomes. The scope is the production deployment: model behavior is important, but so are identities, prompts, files, retrieval stores, APIs, plugins, software dependencies, human decisions, and automated downstream actions.
NIST AI RMF 1.0, released January 26, 2023, is voluntary guidance. Its four functions—Govern, Map, Measure, and Manage—provide a repeatable lifecycle rather than a mandatory checklist. NIST’s Playbook, completed March 30, 2023, offers suggested actions. NIST says the framework is being revised, so verify its status and edition when you begin a review.
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For generative-AI applications, OWASP’s 2025 LLM application taxonomy is a useful security prompt. It covers prompt injection, sensitive-information disclosure, supply-chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system-prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. These categories are not a universal probability ranking; select the ones that fit your architecture, data, users, and exposure.
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1. Govern: assign ownership and define the decision
Start before drawing a data-flow diagram. A threat model without a decision owner becomes a document nobody can use to stop a launch.
Name the people who can make and challenge the decision
- Business owner: accountable for the value being delivered and the consequences of intended use.
- System owner: responsible for the deployed architecture, configuration, dependencies, and change process.
- Security and AI/ML leads: responsible for threat analysis, technical testing, and remediation advice.
- Domain experts: people who understand the decisions, workflows, and populations affected by outputs.
- Privacy, safety, legal, or compliance reviewers: include them when the data, sector, jurisdiction, or use case requires it.
- Risk acceptor: the named person or group with authority to accept residual risk or block release.
Write the operating rules
Record the intended use, foreseeable but prohibited uses, user groups, affected populations, deployment geography, and the model and application versions under review. Define what requires escalation—for example, a newly discovered path to confidential data, an unapproved tool action, a material change in model behavior, or an incident that exceeds an agreed impact threshold.
Set launch criteria before testing. They should state what evidence is required, which findings are release blockers, who can grant an exception, and when a rollback is mandatory. This prevents a successful schedule or a persuasive demo from silently becoming the risk threshold.
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Create a boundary diagram and an inventory that engineers, security staff, operators, and domain reviewers can inspect together. NIST’s mapping function concerns context and lifecycle; its security guidance frames confidentiality, integrity, and availability across AI systems, data, software, and hardware.
Inventory components and responsibilities
| Area | Record for the threat model | Questions to answer |
|---|---|---|
| People and actors | End users, administrators, operators, reviewers, support staff, external callers, and attackers | Who can invoke the system, change it, see logs, approve actions, or influence inputs? |
| Inputs and data | Prompts, uploaded files, conversations, logs, training or fine-tuning data, retrieval corpora, metadata, and generated outputs | What is confidential, regulated, poisonous, user-controlled, retained, or sent to a supplier? |
| Models and services | Model names and versions, hosting, APIs, libraries, datasets, plugins, vector stores, and other suppliers | Who operates each dependency, how is it updated, and what happens if it is unavailable or compromised? |
| Identity and permissions | Authentication, authorization, service identities, secrets, keys, and network privileges | Which principal is used for each call, and is access enforced outside the model? |
| Tools and actions | Functions the model can call, allowed operations, parameters, downstream systems, and irreversible effects | Can the model send, delete, purchase, publish, execute, or modify anything without confirmation? |
| Interfaces and humans | User interface, APIs, review queues, override controls, and notifications | Where can a person inspect, correct, reject, or appeal an output? |
| Operations | Deployment environment, dependencies, monitoring, rate limits, fallback behavior, recovery, and rollback | How will you detect misuse, contain an incident, restore service, and preserve evidence? |
This inventory is a practical synthesis, not a diagram NIST mandates verbatim. Mark each data flow with its trust level and destination. Show where untrusted text enters, where it is combined with instructions, where authorization is checked, and where output becomes an instruction to another component.
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Draw trust boundaries and privilege transitions
Separate, at minimum, the public or tenant-facing interface, application services, model provider, retrieval layer, tool gateway, privileged business systems, and human approval channel. A boundary crossing should identify the identity used, the data leaving the boundary, the validation performed, and the response if the destination is unavailable. Do not treat a model’s system prompt as an authorization boundary; enforce permissions in the application and destination systems.
3. Turn architecture into threat scenarios
Labels alone do not support a launch decision. For every credible scenario, write six facts:
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- Exposure: the data, function, resource, or decision at stake.
- Enabling condition: the weakness, trust assumption, or configuration that makes it possible.
- Outcome: what the system would disclose, change, execute, or prevent.
- Harmed parties: users, customers, employees, third parties, the organization, or the public.
- Control and test: the safeguard and the evidence that will show whether it works.
Prompt injection and instruction conflicts
Direct prompt injection is an attacker’s attempt to override the application’s intended instructions through user input. Indirect injection hides instructions in content the system retrieves or processes, such as a web page, document, ticket, or email. OWASP describes possible results including disclosure of connected data, unauthorized function access, arbitrary commands in integrated systems, and manipulated decisions. It also notes that foolproof prevention is not established. Model the attack as a way to reduce trust in input, then contain its consequences with authorization, isolation, validation, and monitoring.
Unauthorized retrieval and information disclosure
Test whether a user can cause the system to reveal another user’s records, hidden instructions, secrets, personal data, model details, or sensitive logs. Retrieval authorization must be enforced by the data service or query layer; a model instruction such as “only show documents the user may access” is not sufficient. Include deliberate cross-tenant queries, prompt variations, and attempts to infer the existence or contents of records.
Unsafe output handling
Any output passed to a parser, shell, browser, database, template engine, or workflow is a new trust boundary. A seemingly harmless answer can become an injection when interpreted as code, markup, a query, or an action parameter. Specify an output schema, validate types and allowed values, encode for the destination, reject unexpected fields, and test malformed and adversarial outputs at the receiving component.
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Excessive agency
OWASP defines excessive agency through excess functionality, excess permission, or excess autonomy. An email assistant illustrates the risk: the same model may be useful for drafting messages but dangerous if it can send, delete, forward, or alter records without a constrained tool and human approval. Give each task the narrowest tool set, identity, object scope, and operation set. Require confirmation for consequential or irreversible actions.
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Supply-chain, poisoning, and model attacks
Assess model providers, datasets, libraries, extensions, plugins, and vector stores for provenance, update controls, and compromise paths. Consider data or model poisoning where an attacker can influence training, fine-tuning, evaluation, or retrieval content. NIST’s security overview also identifies evasion, model extraction, membership inference, and availability threats; include them when the deployment’s data, intellectual property, or service exposure makes them relevant.
Misinformation and unsafe decisions
Identify workflows where a confident but wrong answer could cause physical, financial, legal, employment, health, or reputational harm. Define when the system must abstain, show uncertainty, cite its source material, route to a qualified person, or stop because it is outside its knowledge or authority.
Unbounded consumption and availability
Model oversized prompts, repeated tool calls, recursive agents, adversarial files, queue floods, and provider failures. Estimate the resources and cost each request can consume, then test rate limits, timeouts, quotas, circuit breakers, and degraded modes. Include the effect of an attacker intentionally forcing expensive or slow paths.
4. Measure likelihood, impact, and uncertainty
Use a method your organization can explain rather than presenting an uncalibrated score as an objective probability. Rate each scenario using factors such as severity to affected people, exposure, attacker effort, existing safeguards, reversibility, detectability, and uncertainty. Keep security risk distinct from broader quality, fairness, privacy, safety, or operational concerns when that improves clarity, but connect all material findings in the launch decision.
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A practical rating worksheet
| Field | Example entry |
|---|---|
| Scenario | Indirect instructions in an uploaded invoice cause a tool-enabled assistant to disclose another tenant’s invoice data. |
| Impact | Confidentiality breach, contractual notification, and loss of customer trust. |
| Likelihood factors | Untrusted uploads are common; the tool is reachable; exploitation requires no special account. |
| Existing controls | Tenant-scoped retrieval, tool allow-list, output validation, and approval before external transmission. |
| Evidence gap | No test yet covers malicious instructions embedded in a document plus a cross-tenant query. |
| Decision state | Block launch until the test passes or the risk acceptor documents an exception. |
Record confidence and evidence quality separately from severity. A severe scenario with little evidence should not receive a reassuring score merely because no incident has occurred.
5. Test controls in a representative environment
Testing should resemble the intended deployment: the same model and version, retrieval configuration, tool permissions, identity paths, filters, logging, rate limits, and human approval steps. A prompt-only demonstration cannot establish that an application or its downstream systems are safe.
- Freeze the test target. Record the build, model version, prompts or policies, data snapshot, dependencies, feature flags, and permission configuration.
- Build representative workflows. Include normal tasks, edge cases, multi-tenant data, sensitive records, long inputs, malformed files, and expected refusal or escalation paths.
- Run abuse cases. Try direct and indirect prompt injection, hidden instructions in retrieved content, cross-tenant retrieval, system-prompt and secret extraction, unsafe output payloads, unauthorized tool parameters, excessive tool loops, and resource-exhaustion inputs where applicable.
- Test the boundaries. Verify authorization in the application and destination service, not only in model instructions. Confirm that parsers, browsers, databases, and workflow engines reject invalid or dangerous output.
- Exercise human controls. Measure whether reviewers see enough context to make a decision, whether approval can be bypassed, and what happens when a reviewer rejects, times out, or is unavailable.
- Collect evidence. Preserve prompts, inputs, model responses, tool calls, identity context, logs, screenshots, expected results, and observed results. Redact sensitive test data appropriately.
- Re-test after fixes. A mitigation that blocks one string may fail for paraphrases, languages, encodings, document formats, or a different retrieval path. Track regression tests as part of the release artifact.
OWASP’s Gen AI Red Teaming Guide, announced January 22, 2025, treats evaluation as a risk-based exercise spanning model behavior, application integration, and failure modes. Use that breadth when planning a red-team engagement; do not limit it to adversarial prompts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Select controls that reduce likelihood or contain impact
Controls should answer a named scenario and have an owner. Common options include:
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- External authorization: enforce tenant, object, and operation permissions in authoritative services.
- Constrained tools: expose only required functions, fixed destinations, bounded parameters, and explicit allow-lists.
- Human confirmation: require an informed approval before sending, deleting, publishing, transferring funds, changing records, or taking another consequential action.
- Input and output validation: use schemas, size limits, encoding, content checks, and destination-specific escaping.
- Retrieval isolation: apply access filters before content reaches the model; prevent cross-tenant indexing and leakage through citations or summaries.
- Data minimization: remove unnecessary secrets and personal data, limit retention, and control log access.
- Supplier and update controls: verify provenance, pin versions where practical, review changes, and maintain a rollback path.
- Abuse resistance: rate limits, quotas, timeouts, loop limits, concurrency controls, and cost alarms.
- Monitoring and response: log identity, retrieval, tool calls, policy decisions, errors, and approvals; define alert triage, containment, notification, and recovery procedures.
- Safe failure: refuse or route to a person when the system is outside its knowledge, loses an authorization dependency, or cannot validate an action.
These are design choices, not guarantees or a universal mandatory set. Select them according to the scenario’s impact, exposure, reversibility, and operational cost.
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7. Make the launch decision explicit
Before release, create a decision record containing:
- the intended deployment, geography, users, model and application versions, and data classes;
- the highest-priority threat scenarios and affected parties;
- tests performed, evidence retained, known limitations, and unresolved findings;
- each control, its owner, operating conditions, and monitoring signal;
- residual risks, assumptions, and the rationale for accepting them;
- release thresholds, exceptions, escalation contacts, and rollback triggers; and
- the named person or group that accepts residual risk.
NIST’s Generative AI Profile (NIST AI 600-1), published July 26, 2024, frames a deployment expectation this way:
“The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits.”
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That statement is not a universal certification test. Translate it into evidence appropriate to your system: which harms matter, what tolerance has been approved, how safe failure works, and how you know the controls remain effective.
8. Keep the threat model alive after launch
NIST’s lifecycle framing means a pre-launch review should become a maintained artifact. Re-open it when the model, prompts, retrieval corpus, tools, suppliers, permissions, user population, data flows, or usage patterns change. Also review it after incidents, near misses, material performance drift, or a new attack technique.
Monitor the signals that can change risk
- Prompt-injection and policy-violation detections, including source and tenant context.
- Unexpected retrieval results, authorization denials, secret-like output, and cross-tenant access attempts.
- Tool-call volume, rejected parameters, approval bypass attempts, loops, latency, and cost.
- Model, library, dataset, plugin, and provider changes, with version and rollback records.
- User reports, reviewer overrides, harmful outcomes, and near misses.
Define who investigates an alert, how evidence is preserved, and when the system switches to a restricted mode or is rolled back. A control that works only until the next model update is not a completed control.
How NIST and OWASP fit together
| Approach | Best use | Strength | Limitation to plan for |
|---|---|---|---|
| NIST AI RMF and Playbook | Organization-wide governance and lifecycle risk management | Connects accountability, context, measurement, and treatment across design through operation | Voluntary, context-sensitive guidance; it does not prescribe one threat list or score |
| OWASP 2025 LLM application risks | Security coverage for generative-AI applications | Provides concrete prompts for injection, disclosure, output handling, agency, poisoning, supply chain, and availability | Categories still require architecture-specific likelihood, impact, and testing |
| OWASP Gen AI Red Teaming Guide | Adversarial evaluation of model behavior and integrations | Encourages risk-based tests across the model, application, and failure modes | Red-team findings need owners, remediation, and a formal launch decision |
Use NIST to establish who decides and how risk is managed; use OWASP to deepen generative-AI security scenarios and tests. Neither replaces domain expertise, application security review, privacy analysis, or operational readiness.
Quick Recap
Common mistakes that invalidate a pre-launch review
- Reviewing only the model: integrations, identities, retrieval, and downstream interpreters often create the exploitable path.
- Using a checklist as a verdict: a category being marked “reviewed” does not show that a specific scenario is controlled.
- Trusting instructions as permissions: authorization must be enforced by code and destination systems.
- Testing only clean prompts: include hostile documents, paraphrases, malformed data, long context, and tool-call abuse.
- Ignoring human failure: approval that is too frequent, opaque, or easy to bypass becomes decoration.
- Scoring without evidence: an unexplained number can hide uncertainty and create false precision.
- Stopping at launch: model, data, supplier, and usage changes can invalidate assumptions.
- Accepting risk by silence: every residual risk needs an owner, rationale, trigger, and review date.
A compact pre-launch checklist
- Intended and prohibited uses, affected people, geography, and data classes are written down.
- Business, system, security, AI/ML, domain, and relevant privacy or legal owners are named.
- The deployed boundary, trust boundaries, identities, data flows, models, suppliers, tools, and human controls are diagrammed.
- Threat scenarios state trigger, exposure, enabling condition, outcome, harmed party, control, and test.
- Generative-AI scenarios cover only the OWASP categories relevant to the architecture, including injection, disclosure, output handling, agency, and resource abuse where applicable.
- Likelihood, impact, uncertainty, reversibility, and evidence quality are assessed with an explainable method.
- Representative abuse tests run against the configured application and downstream systems.
- Controls have owners, monitoring signals, escalation paths, and rollback criteria.
- Residual risks and exceptions are explicitly accepted by the authorized decision maker.
- The threat model, tests, and decision record have a post-launch review trigger.
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