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Threat-model the deployed AI system, not just its model. Before launch, define the system’s purpose and affected people, map data and trust boundaries, identify credible abuse scenarios, test controls in a deployment-like environment, and record residual risk, owners, launch conditions, and the person accountable for accepting the decision. NIST’s voluntary AI Risk Management Framework (AI RMF) provides a useful lifecycle structure—Govern, Map, Measure, and Manage—while OWASP’s generative-AI guidance supplies security-specific scenarios such as prompt injection, unsafe output handling, excessive agency, and information disclosure.
What an AI threat model covers
An AI threat model is a decision record about how a particular deployment could be misused, fail, be attacked, or cause harm—and what evidence shows that controls reduce those risks. The unit of analysis is the complete system in its intended context: model behavior, application code, data stores, retrieval layer, external services, identities, tools, interfaces, human decisions, and downstream actions.
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A model-only review can miss a serious vulnerability introduced by the surrounding software. A language model may refuse a malicious request in isolation, for example, yet an application can still leak another user’s records if authorization is absent from the retrieval service. Likewise, a helpful agent can become dangerous when its service identity can send messages, alter records, or run commands without a confirmation step.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThreat-modeling should begin before production but continue through design, development, deployment, use, evaluation, and change management. NIST released AI RMF 1.0 on January 26, 2023, describes it as voluntary, and says the framework is being revised. Its Playbook, released in a complete version on March 30, 2023, offers suggested actions rather than a mandatory checklist. Use those materials to organize work, then adapt the evidence and risk tolerance to your system, sector, jurisdiction, and affected populations.
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Govern: establish ownership and a launch decision rule
Do not start with a list of attack names. Start by deciding who is accountable and what “safe enough to launch” means for this deployment.
Name the decision owners
Assign named people or teams for the following responsibilities. One person may hold several roles in a small organization, but the duties still need to be explicit.
| Responsibility | Questions to answer |
|---|---|
| Business owner | What user or business outcome justifies deploying the system, and what harms would make that outcome unacceptable? |
| System owner | Which version, environment, interfaces, suppliers, and operational teams are in scope? |
| Security lead | Which attack paths, identities, secrets, dependencies, monitoring, and incident procedures require review? |
| AI or ML lead | How was the model selected, adapted, evaluated, and bounded? What are its known limitations? |
| Domain experts | What errors or decisions are consequential in the real workflow, and when is human judgment required? |
| Privacy, safety, or legal reviewers | Which data uses, populations, decisions, or jurisdictions create additional obligations or unacceptable impacts? |
| Risk approver | Who can accept residual risk, impose conditions, delay launch, or require escalation? |
Define intended and prohibited use
Write the intended users, tasks, data, geography, operating hours, and decision role in concrete terms. State prohibited uses and foreseeable uses that are outside the design. Include affected people who never directly interact with the system, such as customers whose records are summarized or employees evaluated using generated recommendations.
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Set decision rules before testing. Specify which findings block launch, which require a compensating control, and which can be accepted with an owner and deadline. Define escalation events such as a confirmed disclosure, an unauthorized tool action, a material change to the model or data source, or evidence that users are relying on the system beyond its stated role.
Choose a risk vocabulary
Use terms the organization can apply consistently: impact on people and operations, likelihood or exposure, attacker effort, reversibility, existing safeguards, uncertainty, and detectability. A qualitative rating is acceptable if the reasoning is recorded. Do not present an uncalibrated numerical score as an objective probability.
Map the AI system as deployed
Create a boundary diagram that engineers, security staff, domain experts, and operators can inspect together. Draw data flows, trust boundaries, identities, and actions rather than showing only a model box.
Inventory components and flows
| Component or flow | Record in the threat model |
|---|---|
| People and actors | End users, administrators, operators, developers, service accounts, external callers, and potential attackers. |
| Inputs | Prompts, forms, uploaded files, API requests, images, audio, retrieved documents, and automated events. |
| Data stores | Training or fine-tuning data, retrieval corpora, vector stores, user records, logs, traces, caches, backups, and generated outputs. |
| Models | Model name and version, routing logic, fine-tuned variants, moderation models, embedding models, and fallback models. |
| Suppliers and dependencies | Hosting services, model APIs, libraries, datasets, plugins, extensions, browsers, databases, and monitoring providers. |
| Identity and secrets | Authentication methods, authorization checks, service identities, tokens, keys, network permissions, and secret storage. |
| Tools and functions | Every callable operation, accepted parameters, data scope, side effects, rate limit, and approval requirement. |
| Interfaces and humans | User interface, API, administrator console, human review, override points, notifications, and audit views. |
| Downstream actions | Messages sent, records changed, code executed, decisions recommended, transactions initiated, or content published. |
| Operations | Deployment environment, dependencies, monitoring, fallback behavior, recovery paths, rollback process, and change approvals. |
Mark where untrusted content enters, where data crosses tenants or privilege levels, where an external service receives information, and where model output becomes an instruction to another component. Those boundaries are where a plausible scenario becomes a testable attack path.
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Describe lifecycle and change points
Record how data is collected, transformed, retained, and deleted; how models and prompts are versioned; and how retrieval indexes, tools, and policies are updated. A threat model that omits a nightly index rebuild, a vendor model update, or a human escalation queue will become inaccurate as soon as the system changes.
Write threat scenarios instead of only labels
For each relevant boundary, write a scenario that another team could reproduce. Use this structure:
- Trigger: who or what supplies the request, document, dependency, or event?
- Exposure: what data, function, identity, or resource is reachable?
- Enabling condition: which missing check, excessive permission, design assumption, or dependency makes the path possible?
- Consequence: what does the system disclose, change, execute, recommend, or make unavailable?
- Impact: who or what is harmed, and is the effect reversible?
- Control and test: which safeguard should reduce likelihood or impact, and what evidence will demonstrate it?
Prompt injection and indirect instructions
A malicious user can place instructions in a prompt, but an attacker can also place them in a document, web page, email, image, or retrieved record that the system later reads. If the model can call tools or access privileged context, the injected instruction may lead to data disclosure, unauthorized function access, arbitrary commands in connected systems, or manipulated decisions.
Separate instructions from data where possible, label untrusted content, constrain the tools available in each workflow, and enforce authorization outside the model. Test direct and indirect injection against realistic documents and multi-step conversations. OWASP notes that foolproof prevention is unclear; the defensible objective is to reduce the chance and limit the impact through layered controls, then measure residual risk.
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Test whether one user can retrieve another user’s confidential records through normal queries, crafted prompts, alternate identifiers, or a poisoned document. Authorization must be enforced by the retrieval and data services, not inferred from model instructions. Check logs, caches, embeddings, backups, and error messages as well as the visible answer.
Improper output handling
Generated text becomes dangerous when a parser, shell, browser, database, template engine, or workflow treats it as trusted instructions. Identify every sink that receives model output. Use strict schemas, escaping, allowlists, sandboxing, and independent validation where appropriate. A prompt telling the model to “return safe JSON” is not a substitute for validation at the component that consumes the result.
Excessive agency
OWASP describes excessive agency as unnecessary functionality, permissions, or autonomy. An email assistant illustrates the issue: read-only access and a draft-only workflow have a smaller blast radius than unrestricted mailbox access and automatic sending. Give each tool the narrowest data and operation scope, separate read and write identities, limit chained calls, and require human confirmation for consequential or irreversible actions.
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Supply-chain vulnerabilities and poisoning
List the provenance and update path for models, datasets, libraries, plugins, extensions, and hosted services. Consider a compromised dependency, an unsuitable model, poisoned training or retrieval data, a malicious extension, or an unreviewed vendor change. Define review, pinning, integrity checks, update approval, rollback, and monitoring requirements.
Unbounded consumption and availability
Abusive prompts, oversized files, recursive tool calls, or expensive model routes can exhaust compute, hit provider quotas, degrade service, or create unexpected cost. Test limits on input size, recursion, concurrency, tokens, retries, and tool duration. Establish rate limits, budgets, circuit breakers, and a degraded or queue-based mode for overload.
Machine-learning security threats
Depending on the deployment, include evasion, model extraction, membership inference, and availability attacks alongside conventional confidentiality, integrity, and availability risks. The relevant threat depends on the model, data, exposure, and attacker capability; do not add a category merely because it appears on a generic checklist.
False or misleading output
Generative systems can produce incorrect, outdated, or confidently stated content. Identify decisions where an error could cause physical, financial, legal, employment, or reputational harm. Define source display, confidence or uncertainty treatment, human review, refusal behavior, and a safe fallback when the system lacks adequate knowledge.
Rate risks in the actual context
Assess each scenario using a method the organization can explain. Consider:
- Severity and number of affected people or systems.
- Exposure: who can reach the function, data, or interface, and how often?
- Attacker effort, required access, and likely incentives.
- Existing safeguards, their independence, and their tested effectiveness.
- Reversibility: can the action be undone, and how quickly?
- Detectability and time to contain or recover.
- Uncertainty about model behavior, data quality, supplier changes, or operational use.
Keep security risks distinct from broader reliability, fairness, privacy, safety, and operational risks when that separation improves accountability. Bring them together in the launch decision so a system does not pass because a security score looks low while a foreseeable domain harm remains unaddressed.
| Rating question | Evidence to record |
|---|---|
| How bad is the consequence? | Affected people, systems, data classes, financial or operational effect, and reversibility. |
| How reachable is the path? | Authentication, network exposure, user population, required privileges, and attack steps. |
| How dependable is the control? | Design owner, test result, known failure modes, and whether a second control limits impact. |
| What remains unknown? | Untested workflows, uncertain supplier behavior, sparse data, or assumptions about user behavior. |
| What decision follows? | Block, remediate before launch, launch with conditions, or accept with a named owner and review date. |
Measure: test controls before deployment
Testing should resemble the intended environment: the same model and configuration, retrieval permissions, tool wrappers, identity flows, logging, rate limits, and fallback behavior. A clean model sandbox cannot demonstrate that the deployed application is safe.
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Build a representative test set
- Select normal workflows covering each user role, data class, tool, and downstream action.
- Add abuse cases derived from your threat scenarios, including malformed inputs, oversized content, hostile documents, repeated requests, and attempts to cross tenant or role boundaries.
- Define expected behavior for each case: answer, refusal, escalation, redaction, no-op, or safe fallback.
- Capture model version, prompt and policy versions, retrieved context, tool calls, identity, outputs, latency, errors, and control decisions.
- Repeat tests after changes to the model, prompt, data, retrieval index, tool, supplier, or deployment configuration.
Test an LLM application for prompt injection
Use both direct and indirect cases. Ask a user to override system instructions; place conflicting instructions in retrieved documents; hide instructions in long content or markup; and combine injection with requests for privileged data or tool actions. Verify that:
- Untrusted content is not treated as a policy or authorization source.
- Retrieval returns only records the caller is allowed to see.
- Tool calls are authorized independently of the model’s text.
- High-impact actions pause for confirmation or human review.
- Attempts are logged without storing unnecessary sensitive content.
- Rate limits and circuit breakers contain repeated or recursive attempts.
Do not claim that a passing prompt suite proves injection is eliminated. Assess the observed attack success, the impact when a test succeeds, and whether independent controls prevent the harmful action.
Test output handling and downstream behavior
Send boundary values, malformed structured output, escape characters, hostile markup, code-like strings, and unexpected types to every consumer of model output. Confirm that parsers reject invalid schemas, browsers escape content, databases use parameterized queries, and command execution is either prohibited or isolated. Test failure paths, not only successful responses.
Test permissions and agent autonomy
For every tool, attempt calls with missing, expired, or wrong-tenant credentials; alter parameters; chain benign calls into a harmful sequence; and repeat actions after a timeout. Confirm that the application—not the model—enforces scope, approval, transaction limits, and idempotency. Exercise rollback and human cancellation.
Use risk-based red teaming
A red-team exercise can combine model behavior, application integration, data access, tools, human workflows, and operational failure modes. OWASP announced its Generative AI Red Teaming Guide on January 22, 2025. Scope the exercise to credible attack paths and consequential outcomes rather than collecting impressive but irrelevant jailbreaks. Record attack preconditions, reproducibility, impact, detection, containment, and remediation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Manage: select controls that reduce likelihood or impact
Controls should map to a scenario and have an owner and an evidence source. The following are practical options, not universal requirements.
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| Scenario | Possible controls | Useful evidence |
|---|---|---|
| Prompt injection reaches privileged context | Separate instructions from data, restrict context, isolate tools, external authorization, confirmation for high-impact actions. | Injection test results, denied tool-call logs, authorization traces. |
| Cross-user disclosure | Tenant-aware retrieval filters, row-level authorization, data minimization, redaction, retention limits. | Negative access tests, query audit logs, deletion verification. |
| Unsafe downstream output | Strict schemas, escaping, allowlists, sandboxing, independent validation, no direct shell or database interpretation. | Parser-failure tests, sandbox logs, code-review evidence. |
| Excessive agency | Least-privilege identities, narrow tools, read-only defaults, transaction limits, human approval, rollback. | Permission review, approval records, simulated reversals. |
| Supply-chain change | Supplier assessment, dependency pinning, integrity checks, update approval, provenance records, rollback plan. | Inventory, version lockfile, change tickets, recovery test. |
| Resource exhaustion | Input and token limits, concurrency and cost budgets, rate limits, timeouts, circuit breakers, degraded mode. | Load and abuse tests, quota alerts, service-level dashboards. |
| Incorrect or uncertain advice | Grounding, source display, domain review, abstention, escalation, clear user limits, safe fallback. | Representative accuracy and refusal tests, reviewer sign-off, incident trends. |
Set launch gates and document residual risk
Before approving deployment, create a launch record containing:
- System name, intended use, environment, model and configuration versions, and deployment date.
- Users, affected populations, data classes, suppliers, tools, permissions, and downstream actions in scope.
- Top threat scenarios, their ratings, control owners, and test evidence.
- Known limitations, untested cases, assumptions, and monitoring gaps.
- Residual risks, risk tolerance, compensating controls, review dates, and the person accepting each risk.
- Escalation, incident response, rollback, shutdown, and user-notification triggers.
NIST’s Generative AI Profile, NIST AI 600-1, published July 26, 2024, describes 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.” Treat that as a context-sensitive decision standard, not a universal certificate. Define what “demonstrated,” “risk tolerance,” and “fail safely” mean for your system.
Examples of conditions that can block launch
- Authorization is enforced only through prompts or model behavior.
- A tool can perform an irreversible action without an independent approval or transaction limit.
- Retrieved content can cross tenants, roles, or data classifications.
- Model output reaches a code, command, browser, or database sink without validation.
- Critical failure modes have no owner, monitoring signal, rollback path, or response time.
- Testing used a different model, permissions, data, or tool configuration than production.
Monitor after launch and revisit the model
Threat modeling is a maintained artifact, not a one-time gate. Monitor changes in model behavior, prompts, retrieval data, tools, suppliers, user populations, attack patterns, incidents, latency, cost, and fallback frequency. Review sampled interactions under an approved privacy process, and retain enough audit information to reconstruct consequential decisions without collecting unnecessary personal data.
Trigger a new review when the model or provider changes, a new tool or data source is connected, permissions expand, the user population changes, a new jurisdiction or data class enters scope, incidents reveal an unmodeled path, or monitoring shows users relying on the system beyond its intended role. Re-run focused tests and update owners, controls, and launch conditions rather than simply appending a new risk label.
How NIST and OWASP guidance fit together
These resources solve different parts of the problem and are most useful together.
| Resource | Primary contribution | How to use it |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary lifecycle structure: Govern, Map, Measure, and Manage; context-sensitive risk management. | Organize ownership, mapping, measurement, decisions, and ongoing review. Check NIST’s revision status before calling it the latest edition. |
| NIST AI RMF Playbook | Suggested actions for the four functions; complete version released March 30, 2023. | Turn the functions into activities and evidence without treating suggestions as mandatory controls. |
| NIST AI 600-1 Generative AI Profile | Generative-AI lifecycle considerations and deployment language about safety, residual risk, and safe failure; published July 26, 2024. | Use it to define context-specific evidence and limits for generative systems. |
| NIST security and resilience guidance | Confidentiality, integrity, and availability framing plus machine-learning concerns such as evasion, extraction, membership inference, and availability attacks. | Ensure conventional security and infrastructure risks are not lost behind model-specific terminology. |
| OWASP 2025 LLM application risks | Application-focused prompts including prompt injection, sensitive information disclosure, supply-chain vulnerabilities, poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. | Select categories that match the architecture and exposure; do not treat the list as a universal likelihood ranking. |
| OWASP Gen AI Red Teaming Guide | Risk-based evaluation spanning model behavior and system integration; announcement dated January 22, 2025. | Plan adversarial exercises around credible attack paths, impact, detection, and recovery. |
NIST’s framework is primarily an organizing and governance structure. OWASP’s taxonomy and red-team guidance add security-specific attack prompts and testing ideas for generative-AI applications. Neither replaces system-specific analysis, authorization design, domain review, or an accountable launch decision.
A compact threat-register template
Use one row per scenario and keep links to the underlying test artifacts in your normal governance system.
| Field | What to enter |
|---|---|
| Scenario ID and title | A short, unambiguous name such as “Indirect document instruction triggers outbound message.” |
| Asset and boundary | Data, tool, identity, interface, or downstream system involved. |
| Trigger and preconditions | Actor, input, access level, supplier state, and required sequence. |
| Consequence and affected parties | Disclosure, alteration, action, outage, misleading decision, or other harm. |
| Likelihood or exposure rationale | Reachability, attacker effort, frequency, and uncertainty. |
| Impact and reversibility | Severity, scope, recovery time, and whether harm can be undone. |
| Controls and owners | Preventive, detective, and recovery safeguards with accountable teams. |
| Test and result | Environment, version, test case, observed behavior, limitations, and date. |
| Residual risk and decision | Remaining exposure, launch condition, approver, escalation trigger, and review date. |
A defensible launch is therefore not a claim that an AI system is risk-free. It is a documented decision showing that the deployed architecture was understood, credible failure and attack paths were tested, controls were assigned and evidenced, remaining risk fits the organization’s tolerance, and the system can be stopped or recovered when assumptions fail.
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