An AI threat model identifies what the system must protect, who might attack it, which components can be abused, and what impact failure would have. It covers data, prompts, models, tools, and downstream actions rather than only the model itself. Defenders should map trust boundaries, abuse cases, and controls for each stage.
What an AI threat model is actually mapping
An AI threat model is not just a checklist for model abuse. It maps the assets, trust boundaries, and failure paths around the whole AI system, including training data, prompts, model outputs, tools, orchestration, and any downstream action the system can trigger. The practical question is not only “can the model be tricked?” but “what can an attacker reach if the model, its inputs, or its tool access is abused?”
The most useful way to think about it is as a system view of exposure. A model can leak data, follow malicious instructions, call unsafe tools, or produce outputs that are later trusted by humans or other systems. That means the threat model has to include the surrounding application, identity, permissions, memory, retrieval, and action layers, not only the core model.
A solid AI threat model also distinguishes between what is directly user-controlled and what is trusted by design. That distinction matters because many AI failures happen when untrusted content is treated as if it were internal guidance, when outputs are treated as facts without validation, or when the system is allowed to act with more authority than the task requires.
How teams build the model in practice
The usual starting point is scoping. Define the use case, the data the system can see, the actions it can take, and the environments it touches. Then map trust boundaries: where inputs enter, where prompts are assembled, where external data is retrieved, where tools are invoked, and where outputs become decisions or actions. That mapping makes hidden assumptions visible.
Next, enumerate abuse cases for each layer. For example, a prompt may be manipulated, a retrieval source may be poisoned, a tool call may be redirected, or an output may contain unsafe instructions that another system executes. A good threat model asks what each component trusts, what it should never trust, and what happens if those assumptions fail.
At this stage, the model becomes more valuable when it is concrete. Instead of describing “prompt injection risk” in the abstract, identify the exact prompt sources, the tool permissions, the approval gates, and the monitoring that should catch misuse. Threat Modelling AI Agents is a useful reference point when the system includes agentic behaviour, because it turns trust boundaries and identity maps into a working method.
Controls should then be attached to the specific abuse paths, not to AI in general. That usually means data filtering, prompt hardening, output validation, tool allowlisting, permission scoping, logging, rate limits, review workflows, and safe failure modes. If the system can take action, the question is not only whether it is accurate, but whether it is authorised to act and whether its actions are reversible.
Why the threat surface is broader than the model itself
The biggest mistake is to treat the model as the only thing worth defending. In reality, AI systems often fail through the edges: data ingestion, retrieval, tool calls, plugins, APIs, embedded credentials, and downstream automations. The model may be the point of influence, but the real damage often comes from what it can reach after influence is achieved.
That is why AI threat modelling must consider both confidentiality and operational impact. A compromised prompt flow can expose sensitive data. A compromised tool path can alter records, send messages, or execute transactions. A compromised output path can mislead a human operator or feed bad instructions into another workflow. In agentic systems, the blast radius grows when the system can chain actions without enough supervision.
Threat modelling is also where adversarial technique knowledge becomes useful. Techniques such as prompt injection, tool misuse, memory poisoning, and context manipulation are not just research terms; they describe realistic ways attackers abuse the structure of AI systems. For a broader adversarial technique view, MITRE ATLAS adversarial AI threat matrix helps teams connect observed behaviour to known attack patterns.
For organisations deploying agents, the threat model should expand again to include autonomy and delegated authority. If an agent can retrieve, decide, and act, then identity, privilege, and tool access become part of the security boundary. CSA MAESTRO agentic AI threat modeling framework is relevant where orchestration, multi-agent interactions, and outcome risk need to be assessed together.
Risk and Threat Considerations
AI threat models fail when teams underestimate how quickly a small manipulation can become a system-level incident. The risk is not just incorrect output, but unauthorized access, data exposure, unsafe action, and persistence through trusted workflows. The more the system can retrieve, delegate, or execute, the more important it is to model abuse paths rather than only accuracy failures.
Failure mechanism: Attackers exploit untrusted inputs, poisoned retrieval sources, weak output controls, or overly broad tool permissions to influence decisions or trigger actions beyond the intended scope.
Impact: The result can be data leakage, fraudulent actions, business process disruption, lateral movement into connected systems, or a chain of bad decisions that is hard to trace back to the original abuse.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5 and OWASP ASVS set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI threat models must cover misuse of delegated authority and tool access. |
| Recommendation — Constrain agent privileges and validate every high-impact action before execution. | ||
| MITRE ATT&CK | T1059 — Command and Scripting Interpreter | AI outputs or tool chains can drive execution paths that mirror attacker abuse patterns. |
| Recommendation — Map AI-driven execution paths to ATT&CK and detect suspicious automation behaviors. | ||
| NIST SP 800-53 Rev 5 | SA-11 — Developer Testing and Evaluation | Threat modelling AI systems requires structured security testing of abuse paths and controls. |
| AU-2 — Event Logging | AI systems need audit evidence for prompt, tool, and action paths used in the threat model. | |
| Recommendation — Test AI abuse cases against the implemented controls before production release. Log prompt, retrieval, tool, and action events needed to investigate AI misuse. | ||
| OWASP ASVS | V15 — Secure Coding and Architecture | AI threat modeling depends on secure architecture decisions around trust boundaries and outputs. |
| Recommendation — Design AI workflows so untrusted inputs cannot directly drive sensitive actions. | ||
Practitioner Guidance
What to verify: Confirm that the model’s trust boundaries are drawn around real data flows and real action paths, not just around the model endpoint. If a component can change records, send messages, approve work, or call external services, it belongs in scope.
Decision rule: If the AI system can do more than answer questions, treat tool permissions, output handling, and human approval as first-class threat-model items. If it can only generate text, focus more heavily on prompt, data, and output abuse. If it can act, model the blast radius of those actions before expanding autonomy.
What practitioners underestimate: The hardest failures are often indirect. A model does not need to be “hacked” in the classic sense to create material risk, it only needs to be placed inside a workflow that trusts its inputs or acts on its outputs too readily.
Practitioner takeaway: A useful AI threat model is system-centric, not model-centric, and it should end with specific controls tied to concrete abuse paths rather than generic AI security language.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Free weekly newsletter
Subscribe to the NHI & AI Identity Journal
The latest on NHI and Agentic AI security – articles, research, breaches, news and events every week.
Bonus 33% off our NHI Course when you subscribe.
Reviewed and updated by the NHIMG editorial team on October 17, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org