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Use STRIDE to structure threat questions about a system, MITRE ATLAS to explore adversary behaviors aimed at AI-enabled systems, and the OWASP Top 10 for LLM Applications to organize application-specific risks and mitigations. They complement one another; none replaces an architecture-specific threat model, security testing, or accountable owners.
The current OWASP edition as of September 1, 2026, is the 2026 Top 10 for LLM Applications. Use that edition rather than presenting the 2025 list as current, and verify the downloadable 2026 edition before naming or ranking its individual risks.
Why AI applications need more than one threat-modeling lens
AI applications combine familiar software risks with components and behaviors that are easy to miss in a conventional application diagram: model and orchestration services, prompts, retrieved context, vector stores, agent loops, tools, and generated output that may trigger downstream actions. The useful question is not which framework wins, but how to connect broad system analysis to AI-specific adversary behavior and LLM application risks.
These resources answer different questions. STRIDE provides categories for examining threats to system elements and data flows. ATLAS helps identify adversary tactics and techniques relevant to AI-enabled systems. The OWASP LLM Top 10 provides a risk-and-mitigation lens for LLM applications. Their outputs can be cross-mapped, but a mapping alone does not show that a control works or that a listed threat applies to a particular deployment.
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What each resource contributes
| Resource | Primary purpose | Scope and useful output | Important qualification |
|---|---|---|---|
| STRIDE | Structure threat questions using six categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. | General system analysis. Apply the categories to components, identities, trust boundaries, and data flows; record architecture-linked threats and controls. Microsoft’s STRIDE reference describes the categories. | The AI-specific examples in this article are prompts to assess against an actual design, not findings supplied by STRIDE or Microsoft. |
| MITRE ATLAS | Explore adversary tactics and techniques targeting AI-enabled systems. | AI-focused scenario enrichment: use relevant behaviors to add specificity to threats already connected to the system model. OWASP’s threat-modeling directory describes ATLAS as a living knowledge base. | ATLAS is not a complete architecture, a deployment-specific risk ranking, or proof that a listed technique applies. The description here is attributed to OWASP’s directory. |
| OWASP Top 10 for LLM Applications | Organize LLM application risks and mitigations. | Application-risk checklist to map to components, owners, controls, tests, and residual risk. The current edition is the 2026 Top 10, dated September 1, 2026. | The 2026 landing page establishes the edition and describes its updates and mappings, but its readable text does not expose the complete category list. Consult the official downloadable edition before stating exact category names or rankings. |
OWASP says the 2026 edition updates rankings, broadens threat coverage, draws on research grounded in thousands of real-world AI security incidents, and maps risks to MITRE ATLAS, CWE, NIST, and the OWASP Top 10 for Agentic Applications. Its September 1, 2026 announcement also reports 10,000 downloads in the first 48 hours and more than 30,000 LinkedIn members. Those are publication and community figures, not measures of framework effectiveness, security outcomes, or unique active users.
How to combine them in a threat-modeling workflow
The following sequence is a practical synthesis of the resources, not an officially mandated process. Keep the model tied to a specific architecture and document assumptions as well as decisions.
- Draw the system and trust boundaries. Show user entry points, models, orchestration and agent loops, retrieval sources, tools and APIs, sensitive data, human approval points, logging, and downstream systems. Mark untrusted inputs and outputs, including retrieved material and generated content.
- Apply STRIDE to components and flows. For each credible threat, record the affected asset or boundary, an abuse question, potential impact, existing controls, and an accountable owner. For example: can someone spoof a user or tool identity; tamper with retrieved content or a model artifact; dispute or obscure an action in the audit trail; expose sensitive context; exhaust model or tool resources; or exploit prompt injection and excessive permissions to gain authority?
- Enrich relevant scenarios with ATLAS. Look for AI adversary behaviors and red-team demonstrations that fit the architecture. Add only applicable techniques, and note the assumptions behind each mapping; a technique’s presence in a knowledge base is not evidence that the deployment is vulnerable.
- Use the current OWASP LLM edition as a risk and mitigation checklist. Map applicable items from the 2026 edition to architecture components, control and test evidence, risk owners, and residual risk. Confirm its exact taxonomy in the official download before recording category names or rankings.
- Map controls and gaps. Microsoft’s AI defense catalog normalizes defenses against ATLAS, the OWASP 2025 LLM Top 10, and NIST AI RMF. It groups controls across governance and assurance, supply-chain provenance, identity and least privilege, input and retrieval hygiene, model hardening, runtime isolation, output handling, monitoring and forensics, and resource governance. Use those as control families, not as evidence that the catalog already maps to OWASP’s newer 2026 edition.
- Validate and maintain the model. Feed the threat model into design review, testing or red teaming, release decisions, monitoring, and incident response. Revisit it when the architecture, model or provider, tools, data sources, or agent permissions change.
Questions to test in an AI system
STRIDE gives a repeatable structure for asking these questions; it does not answer them without system-specific analysis.
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- Identity and authority: Can an agent, user, or tool impersonate another identity? Do tool credentials and permissions allow only the actions required?
- Integrity: Could an attacker alter prompts, retrieved content, training or model artifacts, tool responses, or approval state?
- Accountability: Can the organization reconstruct what the system received, decided, and did, including relevant approvals and tool calls?
- Confidentiality: Can sensitive data leak through prompts, retrieved context, model output, logs, or downstream consumers?
- Availability and cost: Can repeated or oversized requests exhaust model, tool, or service resources?
- Output and action safety: Could untrusted generated output be interpreted as instructions by a downstream component, or could prompt injection influence a tool-using system beyond its intended authority?
Treat these as investigation prompts, not a claim that any unspecified AI system has these weaknesses. The relevant controls depend on the architecture, data, deployment context, and impact of failure.
Rank #3
Keep edition and evidence boundaries clear
The OWASP page for the 2025 LLM risk set remains accessible, but it is historical once the 2026 edition is current. The 2025 page lists Prompt Injection, Sensitive Information Disclosure, Supply Chain, Data and Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector and Embedding Weaknesses, Misinformation, and Unbounded Consumption. Do not pass those names off as the 2026 ranking or imply that the list stayed unchanged.
Framework cross-mapping is useful for organizing work, but it is not proof of completeness, control effectiveness, or compliance. Preserve the source and version for each mapped item, identify who owns each risk, and record how controls will be tested. No effectiveness comparison among STRIDE, ATLAS, and OWASP is established by the cited materials.
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