MITRE ATLAS vs OWASP LLM Top 10: Which to Use

Which should drive your MITRE ATLAS vs OWASP LLM Top 10 decision

Use MITRE ATLAS to plan what you test and the OWASP LLM Top 10 to explain what you found. ATLAS is an adversary technique knowledge base, so it answers whether your test covered the ways an attacker could reach and act on an AI system. The OWASP list ranks application risks in terms a product owner, risk committee or customer can act on. Choosing only one usually leaves a gap: an ATLAS-only plan can produce a report that engineering reads as a technique catalog, and an OWASP-only plan can miss adversary paths that no LLM01 to LLM10 category names directly, such as reconnaissance or discovery of agent runtime capabilities.

Using ATLAS alone is the wrong choice when the audience is mostly governance or procurement, because the technique IDs add detail without a severity story. Using the OWASP list alone is the wrong choice when the system exposes services, agent tools or runtime components that are not application logic, since the 2026 list does not replace a technique-level view. Before writing the scope, confirm with your test provider which ATLAS release and which OWASP 2026 edition their checklists map to, and ask how each finding will carry both a technique reference and an OWASP category.

Use both when engineering and governance share one engagement. A practical pattern is to build the test plan from ATLAS tactics, then tag each executed scenario with the OWASP LLM category it tests, so LLM03 Excessive Agency or LLM08 Hidden Context Exposure appear beside the technique IDs they exercised. Ask for that mapping in the statement of work and in the final report template. Where the OWASP 2026 mappings to NIST, ATLAS or CWE are used as a cross-reference, confirm the exact version used so the traceability survives the next reassessment.

How the two frameworks differ on structure, cadence and purpose

This table compares MITRE ATLAS and the OWASP Top 10 for LLM Applications on what each contains, how it is updated, how it maps to other structures and what it is for. Read it to decide which framework drives test planning and which one shapes the report that the business reads.

Factor	MITRE ATLAS	OWASP Top 10 for LLM Applications 2026
What it is	Public knowledge base of adversary tactics and techniques aimed at AI systems	Ranked list of ten risk categories for LLM applications
Size and structure	v2026.09 data release: 16 tactics, 120 techniques, 88 sub-techniques, 40 mitigations, 73 case studies	Ten entries, LLM01 Prompt Injection through LLM10 Improper Output Handling
Update cadence	Versioned data releases; v2026.09 dated 15 September 2026	Edition published August 2026; confirm the next edition date with OWASP GenAI
Mapping to other structures	Technique IDs (for example AML.T0051.001) that map to adversary behavior	Mappings expanded to NIST, MITRE ATLAS and CWE in the 2026 edition
Primary use in test planning	Choosing which adversary techniques to emulate and which attack paths to cover	Setting the risk categories a test of an LLM application should exercise
Primary use in reporting	Tagging findings with technique IDs so detection and response teams can trace them	Naming and ranking findings by risk category for management and developers
Who typically reads the output	Red team leads, detection engineers and threat intelligence analysts	Application owners, developers, product security and risk committees

What each approach finds and misses

MITRE ATLAS and the OWASP Top 10 for LLM Applications answer different questions about an AI system. One catalogs how adversaries operate, the other ranks what goes wrong in the application, and a test plan usually needs both views to be complete.

Exposed AI services and attack entry

ATLAS v2026.09 added Misconfigured or Publicly Exposed AI Services (AML.T0132), which describes how an adversary finds and reaches an AI service that should not be reachable. Test planning should check which model endpoints, vector stores and agent interfaces sit on public addresses or weak authentication. The OWASP list does not name this attack path directly, so teams relying only on it can miss the exposure.

Agent runtime discovery

Discover AI Agent Runtime Capabilities (AML.T0133) describes an adversary learning what an agent can run, which tools it can call and what permissions it holds once inside. For a test of an agent, this technique explains why tool inventories and permission scopes need review. Confirm with your team which tools an agent can reach before scoping the test.

Excessive agency as a business risk

OWASP moved Excessive Agency up to LLM03 in the 2026 edition, which signals that giving a model too much capability is a leading application risk. Testers should check whether the model can trigger actions such as sending messages, changing records or issuing payments without a human approval step. This is an application-level risk ranking, so it maps to business impact more directly than a technique list does.

Hidden context exposure

The 2026 OWASP list adds Hidden Context Exposure at LLM08, covering information a model holds in system prompts, retrieved documents or tool outputs that users should not see. Test it by checking what the application reveals when prompted to repeat, summarize or reason about its hidden context. ATLAS covers related extraction techniques, but the OWASP entry frames the risk in terms a product owner can act on.

Granularity that can overwhelm ranking

ATLAS breaks adversary behavior into 16 tactics, 120 techniques and 88 sub-techniques. That depth helps detection and emulation design, but a risk register built directly from it can become a long list with no clear priority. Use the technique catalog to plan coverage, then group results into a short set of business risks before reporting to leadership.

No step-by-step attacker path

The OWASP list describes what goes wrong, such as prompt injection (LLM01) or improper output handling (LLM10), but it does not enumerate how an attacker moves from one step to the next. A tester writing an attack plan will need technique-level detail for each step, so ATLAS or a similar catalog fills that gap. Confirm which steps a test must prove before choosing the reporting vocabulary.

How the 2026 LLM Top 10 maps to ATLAS techniques

The 2026 edition of the OWASP Top 10 for LLM Applications, published in August 2026, changes the list in three ways that affect any mapping built on the earlier numbering. Hidden Context Exposure is new at LLM08, Excessive Agency moves up to LLM03, and the official mappings now reference NIST, MITRE ATLAS and CWE. Before you map anything, check whether your test plan, finding IDs and report templates still use the older LLM numbers. Renumber them first, or a reviewer will read a finding against the wrong risk.

Use the official mappings as a starting point and verify each pair against the OWASP entry and the ATLAS technique page for the version you cite. A single OWASP risk often covers several ATLAS techniques, and one technique can support more than one risk, so expect one-to-many relationships rather than a clean one-to-one match. Record the ATLAS data release beside every mapping. The current release is v2026.09, dated 15 September 2026, and a later release can change descriptions, so a dated citation keeps your report reproducible. For each mapping, ask what evidence your tester captured that supports it.

Two ATLAS updates in v2026.09 matter for test design: LLM Prompt Injection: Indirect (AML.T0051.001) and LLM Jailbreak (AML.T0054). Indirect injection applies when an application processes documents, web pages or tool outputs that an attacker can influence, so the test should plant instructions in those inputs and observe what the system does. Jailbreak targets the model's own safety behavior. Keep these as separate test cases with separate evidence, and reread any older findings that cite either technique against the current ATLAS descriptions before you reuse them.

Using ATLAS for test planning and the OWASP list for reporting

Use ATLAS as the adversary side of the plan. Its techniques describe how an attacker would act against an AI system, so each test scenario should trace back to one or more technique IDs. The planning output is a scenario list in which every scenario names the ATLAS tactic and technique it exercises, the assets it touches and the expected detection signal. Several ATLAS entries are new in the v2026.09 data release, including Misconfigured or Publicly Exposed AI Services (AML.T0132) and Discover AI Agent Runtime Capabilities (AML.T0133), so confirm your inventory covers them.

Scenario mapping: Write each test case against a specific technique, such as indirect prompt injection (AML.T0051.001), rather than a general theme.
Coverage checks: Compare the techniques your scenarios cover against the tactics relevant to your architecture, and record the gaps you accepted.
Detection validation: For each scenario, state what logs, alerts or guardrail events should fire, and test whether they do.
Scope decisions: Use technique applicability to decide which components, such as retrieval stores or agent tools, enter scope.
Re-planning: Recheck mappings when ATLAS publishes a new data release, since technique sets change between versions.

Check with your test provider that their methodology records ATLAS IDs per finding, and ask how they document techniques they attempted but did not reach.

What drives the cost and effort of an AI security test

The price and effort of an AI security test follow from what is in scope, how deep the technique coverage must go, and who will read the results. Each card below is a lever that moves the quote, and each one is worth fixing in writing before a vendor or partner prices the work.

System scope: agents, tools and data

An LLM feature that only returns text is a smaller test than one that calls tools, writes to systems of record or reads from retrieval stores. Each agent, tool integration and data source adds ATLAS techniques to consider and OWASP LLM risks such as Excessive Agency (LLM03) to exercise. Ask the provider to list every agent, tool, connector and data store in the written scope, and to state which ones are excluded.

Number of AI components

Each model, embedding pipeline, vector store, prompt template and guardrail layer is a separate attack surface with its own configuration. A single application that uses three models and two retrieval sources takes more setup and more test cases than one model behind one endpoint. Ask whether the quote prices per component or per application, and what happens when a component is added mid-engagement.

Depth of ATLAS technique coverage

Testing a short list of OWASP LLM categories is cheaper than mapping findings across the ATLAS tactics and techniques that apply to your system. Full coverage means test cases for each relevant technique, evidence per case and a rationale for techniques marked not applicable. Ask the provider which ATLAS techniques they will cover, which they will exclude, and how exclusions are documented.

Black box or architecture access

Black-box testing from an external viewpoint takes longer to reach the same depth, because testers must infer prompts, tool schemas and system instructions. Access to architecture diagrams, system prompts, tool definitions and logs usually shortens discovery and widens coverage. Ask what access the quote assumes, who provides it, and whether test accounts with defined roles are included.

Reporting audience and format

A report for engineers needs reproducible prompts, request traces and fix guidance. A report for a risk committee or regulator needs mapping to ATLAS and OWASP identifiers, severity rationale and a remediation tracker. Producing both usually means more writing and review time. Ask for the sample report format, the number of review rounds and whether an executive summary and technical appendix are both included.

Retest and change control

AI systems change often through prompt edits, model version updates and new tools, so a finding fixed in one release can return in the next. Retest effort depends on how many findings are open, how many components changed and whether the provider runs regression cases. Ask whether retests are included, how many rounds are priced, and who pays when a retest is needed because of a model update.

How to plan one test against both frameworks

This sequence lets one engagement use MITRE ATLAS for adversary technique selection and the OWASP Top 10 for LLM Applications for risk coverage, so the same test produces findings that map to both. It follows the NIST SP 800-115 approach of combining techniques to reach an in-depth assessment at an acceptable level of risk.

Define the system and its trust boundaries
The client's architecture owner and the test lead document the AI system as a set of components: the model or models, the prompt and system instructions, retrieval sources, tools and plugins, data stores, the web and API layers that front them, and the identities each one uses. Mark every trust boundary, such as where user input meets the model, where model output reaches a downstream interpreter, and where an agent calls an external service. This step is complete when the client signs off on a diagram and a component inventory that the rest of the plan references by name.
Map OWASP LLM risks to in-scope components
The test lead walks each component against the ten entries in the OWASP Top 10 for LLM Applications 2026, from LLM01 Prompt Injection through LLM10 Improper Output Handling, and records which entries apply and why. Entries such as LLM03 Excessive Agency and LLM04 Supply Chain often depend on configuration and third-party dependencies, so the client's platform and procurement owners should confirm them early. The output is a risk-to-component matrix in which every in-scope component either has at least one mapped risk or carries a written reason for none.
Select ATLAS techniques for each risk
For each mapped risk, the test lead pulls candidate techniques from the MITRE ATLAS v2026.09 data release, which lists 16 tactics and 120 techniques, and records the technique IDs that describe how an adversary would reach that risk. Prompt injection, for example, maps to techniques such as LLM Prompt Injection: Indirect (AML.T0051.001) and LLM Jailbreak (AML.T0054), while exposure of AI services may point to AML.T0132. Where ATLAS has no close technique for a risk, the lead notes the gap rather than forcing a match. The output is a test catalog that pairs each risk with its technique IDs and the component under test.
Set rules of engagement
The client and the test lead agree on written rules of engagement, using the template in Appendix B of NIST SP 800-115 as a starting point. The document covers permitted attack types per component, test accounts and data, handling of any sensitive information the tests may expose, restrictions on production traffic and tool execution, escalation contacts, and stop conditions. Ask the provider to state which ATLAS techniques involve live exploitation and which only involve analysis, because that split determines the approvals needed. The step closes when both parties sign the rules and the environment (staging or production) is confirmed in writing.
Run the test in combined passes
Testers run the catalog in passes that combine techniques, as NIST SP 800-115 recommends for in-depth assessment: a review of configuration and tool permissions, then manual adversarial prompt work against each mapped risk, then testing of the web, API and data layers that surround the model. Each test records the ATLAS technique ID, the OWASP risk, the component, the input, the observed behavior and the evidence. Automated tools may generate volume, but a tester reviews results by hand, since a tool run alone does not establish the coverage a risk requires. The step closes when every catalog entry has a recorded result of pass, fail, or not applicable with a reason.
Validate findings and assign severity
The test lead reproduces each failed test from its recorded input and confirms that the behavior is caused by the system rather than by test noise. Each confirmed finding receives a severity agreed with the client, and the client's owners confirm whether a compensating control already exists. Ask the provider how it scores findings and whether it uses a published scoring system, since that choice affects how results compare across tests. The output is a validated finding list with reproduction steps that the client's engineers can rerun.
Produce a report that links findings to both lists
The final report presents each finding with two cross-references: its OWASP LLM entry, which explains the risk for the business and development teams, and its ATLAS technique IDs, which describe the adversary behavior for detection and red team teams. The report also lists the mapped risks with no failing tests, so the client can see what was checked and not only what failed. Ask the provider for a sample report structure before signing, and confirm that the identifiers match the versions named in the agreed scope. This step is complete when the client's security and AI owners accept the report and the cross-reference table is retained for the next test cycle.

Questions buyers ask before hiring help with AI security testing

Does any regulation require MITRE ATLAS or the OWASP LLM Top 10?

The sources supplied for this page do not show that either framework is named in a regulation, so do not assume one is required. Both are voluntary references: ATLAS is a public knowledge base of adversary techniques against AI systems, and the OWASP list is a ranked set of application risks. Regulations and supervisors set their own expectations for AI testing and risk management, and the EU AI Act is one example that customers and auditors often raise. Ask your regulator, your legal team and any customer contract which references they expect, and get the answer in writing before you choose a framework to report against.

How often do MITRE ATLAS and the OWASP LLM Top 10 change?

ATLAS publishes versioned data releases, and the most recent one in the supplied facts is v2026.09, dated 15 September 2026. It added techniques such as Misconfigured or Publicly Exposed AI Services (AML.T0132) and Discover AI Agent Runtime Capabilities (AML.T0133), and updated LLM Prompt Injection: Indirect (AML.T0051.001) and LLM Jailbreak (AML.T0054). The OWASP Top 10 for LLM Applications 2026 was published in August 2026. For test plans, pin the framework version in the scope document and record it in every report, so that a later release does not silently change what was tested. Confirm the current release cadence directly with each project.

Can our team use these frameworks without outside help?

Yes, both are public and free to read, so an internal team can map its own AI features to ATLAS techniques and to the OWASP risks without a vendor. The harder part is judgment: deciding which techniques apply to your architecture, writing test cases that a reviewer can reproduce, and separating a true exposure from a model quirk. Teams with existing application security or red team skills usually build the first version in house and bring in outside help for independent testing, for tooling gaps, or when they need a second opinion before release.

What do our internal staff need to contribute to an AI security test?

Expect to supply several roles for the engagement to produce useful results:

An owner who can approve scope, data handling rules and test accounts
Engineers who can explain the architecture, including model hosting, retrieval sources, tool connections and logging
Someone who can grant access to a non-production environment that mirrors production as closely as practical
A reviewer who can judge business impact and decide what to fix

Also confirm early which tools the tester may use against your systems and whether third-party model providers allow the testing you plan. Missing access and unclear rules are the most common causes of delay.

What drives the time and cost of an AI security test?

Cost follows scope, and no researched figure is supplied here for either framework. The main drivers are the number of AI features and integrations, whether the system calls tools or acts with delegated identity, how many environments and user roles are covered, and how much of the work is manual versus automated. One-time costs include scoping, test setup and the first report. Recurring costs come from retesting after model or prompt changes and from keeping the test plan aligned with each new framework version. Ask any provider to price these items separately.

How do we request a quote from a written scope?

Start with a written scope that names the systems, environments, user roles, data types, integrations and agents in play, and state which framework you want the findings mapped to. Add the rules of engagement, the testing window, the people who approve access and the deliverables you expect, such as a findings register with ATLAS technique IDs and OWASP risk mapping. DeployOpen works from a written scope that defines scope, deliverables and timelines before work starts, and it does not publish a price list, so a quote follows from that document. Send the same scope to every bidder so the quotes can be compared fairly.

Should we test against ATLAS techniques or the OWASP risks first?

Use the OWASP LLM Top 10 to decide what to test and ATLAS to describe how an attacker would carry it out. The OWASP list is organized around application risks such as LLM01 Prompt Injection and LLM03 Excessive Agency, which suits a test plan built around your product. ATLAS is organized around adversary tactics and techniques, which suits detection engineering, threat modeling and reporting to security teams. Many teams start with the OWASP categories for scope and then tag each finding with the matching ATLAS technique ID so both audiences can read the result.

Keeping test plans current as ATLAS and the OWASP list change

Tie your review cadence to each release, not to the calendar. The two sources move on different schedules. The OWASP Top 10 for LLM Applications 2026 edition was published in August 2026, and the MITRE ATLAS v2026.09 data release followed on 15 September 2026. When either one publishes a new edition, reopen the test plan and check three things: which risk categories changed, which ATLAS techniques were added or updated, and whether your scenario library still maps to the current IDs. The 2026 OWASP edition moved Excessive Agency up to LLM03 and added Hidden Context Exposure at LLM08, so a plan written against the older numbering needs its coverage matrix rebuilt, not just renumbered. Confirm the release notes and any changelog for each source before you schedule the review, because the exact content of each change is best read from the source itself.

Pin the edition in every finding and every report. A finding that says only "prompt injection" or "LLM01" becomes ambiguous within a year. Record the OWASP list edition (for example, the 2026 edition) and the ATLAS data release tag (for example, v2026.09) in the report header, and cite both IDs next to each finding, such as the LLM category and the ATLAS technique ID used to describe the adversary behavior. Store the same identifiers in your ticketing system so that a retest years later can state which version of the catalog the original finding used. Ask your testing provider to confirm in writing which editions their statement of work and report templates reference, and whether they will re-map historical findings if you ask them to.

How DeployOpen scopes and delivers AI security testing

DeployOpen is a Chainscore Labs company that plans, implements, tests and documents self-hosted platforms inside the client's own infrastructure. For MITRE ATLAS vs OWASP Top 10 for LLM Applications, we help teams map their AI system's risks to the right reference for each stage, then test and document the results in their own environment.

How we scope

Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.
The written scope states which LLM application components, data stores, agents and tools fall within the test.
We agree whether ATLAS techniques or OWASP LLM01 to LLM10 categories will drive test planning and report structure.
Scope also records the test environment, access level and rules of engagement, including what is off limits in production.

How we build

We configure the chosen test catalog into a test plan that maps each test case to a technique or risk category.
Adversarial testing covers the LLM application and its surrounding web, API and data layers, not only model prompts.
Where the system uses agents or tools, we test tool permissions and excessive agency against the agreed scope.
Findings are validated manually and recorded with reproduction steps, affected components and evidence.

How we hand over

Deliverables include a test report with findings cross-referenced to both ATLAS technique IDs and OWASP LLM categories where mappings apply.
We document the test plan, environment, and retest results so your team can repeat the work in later release cycles.
Remediation guidance is written for the engineers who will fix each issue, with clear priorities agreed with your team.
Compliance preparation material is provided as evidence for assessors, and we do not issue audit reports or certifications.

How we support

Ongoing support, upgrades and continuous testing are available as retainers after the initial engagement.
Retainers can track changes in the ATLAS data releases and the OWASP LLM Top 10 editions so the test plan stays current.
Ask for a written quote that lists scope items, deliverables and the retest terms before committing to a retainer.
Confirm with us which cadence of retesting suits your release process, and which parts of the test plan need updating each cycle.

MITRE ATLAS vs OWASP Top 10 for LLM Applications