OWASP LLM Top 10 vs MITRE ATLAS: Which to Use and How They Map

Teams securing LLM applications usually reach for the OWASP Top 10 for LLM Applications and MITRE ATLAS at the same time, then discover the two do not line up and use different words for the same problem. They are different kinds of artefact: OWASP's list ranks application risks and tells builders how to prevent them, while ATLAS catalogues how adversaries actually attack AI systems, step by step, with mitigations and case studies. This guide compares them on purpose, structure, update cadence and audience, maps all ten 2025 OWASP entries to current ATLAS techniques, shows where each leaves gaps, and gives decision criteria for AppSec, threat modelling, red teaming and detection work. It is for security architects, AppSec leads, AI red teamers and GRC teams. ATLAS IDs were checked against ATLAS release 2026.10; for the matrix itself see the MITRE ATLAS Guide, and for how both sit among other AI frameworks see the AI Security Frameworks Crosswalk Guide.

Key takeaways

• A risk list and an attack knowledge base answer different questions. OWASP asks "what can go wrong in my LLM application and how do I prevent it?"; ATLAS asks "how would an adversary attack this AI system, in what order, and what mitigates each step?".
• Use OWASP to build, ATLAS to attack and detect. OWASP entries translate cleanly into design requirements and test cases; ATLAS tactics and techniques translate into threat models, red team plans, detection use cases and incident write-ups.
• The official mapping is partial and ageing. The 2025 OWASP entries cite ATLAS for seven of ten risks, and three of the cited sub-technique IDs (AML.T0024.000 to .002) no longer exist in ATLAS 2026.10. Maintain your own mapping and pin the ATLAS release.
• Each has blind spots the other covers. OWASP covers misinformation, improper output handling and governance-adjacent risks that ATLAS treats as impact or barely models; ATLAS covers reconnaissance, persistence, credential access and agent tool abuse in a depth no ten-item list can.
• Identity runs through both. Excessive Agency (LLM06) and ATLAS agent techniques such as AML.T0053 AI Agent Tool Invocation are, in practice, questions about which credentials an AI system holds and who it acts for.

The short answer

You need both, for different jobs. If you are writing requirements, reviewing an LLM application design or building a test plan for a release, start with the OWASP Top 10 for LLM Applications 2025: it is short, written for builders, and every entry ends with prevention and mitigation strategies. If you are threat modelling an AI system end to end, planning an AI red team exercise, writing detections or mapping an incident, use ATLAS: its tactics give the attack sequence, its techniques give stable IDs, and its case studies show real chains. Link the two through a maintained mapping table so findings from either can be reported in the other's terms.

What each framework is

Two facts shape how you use them. First, the OWASP list changes by edition, so an entry number such as LLM07 is only meaningful with its year. Second, ATLAS changes monthly: the atlas-data repository states that "ATLAS releases monthly content updates", and techniques are renamed, merged and retired. Record the release you mapped against in every report.

Mapping all ten OWASP entries to ATLAS

The table shows what each 2025 OWASP entry cites in its own "Related Frameworks and Taxonomies" or reference section, and the closest techniques in ATLAS 2026.10. The right-hand column is a judgement mapping, not an official one, and coverage is partial unless stated.

The LLM02 row shows why a mapping must be maintained. The 2025 OWASP text points at three sub-techniques of AML.T0024 for membership inference, model inversion and model extraction. In ATLAS 2026.10 those IDs are gone: training-data inference sits under AML.T0137 and its sub-techniques, while AML.T0024 remains as Exfiltration via AI Inference API. A tool or report that still keys on AML.T0024.000 will silently lose the link. Per-entry depth on each OWASP risk is in the dedicated guides, for example the OWASP LLM Top 10 LLM01 Prompt Injection Guide and the OWASP LLM Top 10 LLM06 Excessive Agency Guide.

OWASP LLM Top 10 in depth

Threat model and architecture

OWASP's implicit model is an LLM application: a user interface, a prompt assembled from system instructions and user input, optional retrieval from a vector store, optional plugins or tools, and output passed to a browser or downstream system. Each entry names a weakness in one of those components. The threat actor is usually implicit: a malicious user, a poisoned data source or a compromised supplier. That is why the list maps well onto a design review: you can walk each component and ask which entries apply.

Where it works best

• Secure design requirements: prevention strategies convert into acceptance criteria, for example "ensure that security controls are enforced independently from the LLM" from LLM07, or least-privilege extension permissions from LLM06.
• Test planning: the example attack scenarios give testers concrete cases per release. The OWASP AI Testing Guide turns these into test cases.
• Developer awareness and procurement questions: ten named risks fit a training session or a supplier questionnaire.

Where it falls short

• No sequence: a list cannot express that prompt injection (LLM01) led to tool misuse (LLM06) and then exfiltration through rendered output (LLM05).
• Coarse granularity for detection: "Prompt Injection" covers direct, indirect, triggered and multimodal variants that need different telemetry.
• Little on the surrounding estate: credential theft from agent configuration, persistence in memory and lateral movement are outside the list's frame. Agentic risks now have their own list; see the OWASP Top 10 for Agentic Applications Guide.

MITRE ATLAS in depth

Threat model and architecture

ATLAS models the adversary. Tactics run from Reconnaissance and Resource Development through two AI-specific tactics, AI Model Access (AML.TA0000) and AI Attack Adaptation (AML.TA0001), to Exfiltration and Impact. Techniques carry a maturity value and platform tags (Predictive AI, Generative AI, Agentic AI, Enterprise), so you can filter the matrix to the system you are assessing. Mitigations link to the techniques they address: for example, AML.M0028 Enforce Access Controls for AI Agent Tools and AML.M0030 Restrict AI Agent Tool Invocation on Untrusted Data are linked to AML.T0053 and AML.T0086.

Where it works best

• Threat modelling: walk the tactics left to right for each AI asset and record which techniques are reachable. The AI Agent Threat Modelling Guide applies this to agents.
• Red team planning and reporting: each finding gets a technique ID and the chain gets a tactic sequence that defenders recognise. The AI Red Teaming Guide covers running the exercise.
• Detection engineering: techniques with an ATT&CK reference connect AI telemetry to existing SOC content, and AML.M0024 AI Telemetry Logging and Monitoring states the logging need.
• Incident mapping and intelligence: case studies are written as technique steps, so a new incident can be compared with known chains.

Where it falls short

• Not a requirements catalogue: mitigations are short and generic; they do not tell a developer how to encode model output for a browser.
• Volatile IDs: monthly releases rename, merge and retire techniques. Tooling that hard-codes IDs needs a release pin and a migration step.
• Unintentional failure is out of scope: hallucination without an adversary, unsafe defaults and governance gaps have no technique of their own.

Decision criteria: which to use for which job

Small teams: adopt OWASP first, because it fits existing secure development practice, and add ATLAS IDs to findings as a reporting convention. Larger organisations with a SOC and red team: make ATLAS the shared language between red team, detection and intelligence, and keep OWASP as the developer-facing standard, with one maintained mapping between them.

Worked scenario: one incident, two lenses

The EchoLeak vulnerability in Microsoft 365 Copilot, reported by Aim Security researchers and fixed server-side by Microsoft in 2025, is recorded in ATLAS as case study AML.CS0059. It shows what each framework adds.

1. ATLAS view: the case study records twelve steps, including Retrieval Content Crafting (AML.T0066), Prompt Infiltration via Public-Facing Application (AML.T0093) through an inbound email, LLM Prompt Obfuscation (AML.T0068), RAG Poisoning (AML.T0070), triggered prompt injection (AML.T0051.002), collection from RAG Databases (AML.T0085.000), LLM Trusted Output Components Manipulation (AML.T0067), LLM Response Rendering (AML.T0077) and Exfiltration via Cyber Means (AML.T0025). A detection engineer can place a control or signal on each step.
2. OWASP view: the same event is LLM01 Prompt Injection (indirect), LLM02 Sensitive Information Disclosure and LLM05 Improper Output Handling, with LLM08 relevant because retrieval pulled attacker content into context. A product team gets three owners and three sets of prevention strategies.
3. Combined use: the design review uses OWASP to require segregation of external content and output link controls; the red team uses the ATLAS chain to test whether those controls break the sequence at more than one step; the SOC builds detections for the rendering and exfiltration steps.

Building and maintaining a joint mapping

1. Pin versions: record "OWASP LLM Top 10 2025" and the ATLAS release (for example 2026.10) in the mapping header.
2. Start from OWASP's own references, then add closest current techniques for the entries that cite none (LLM05, LLM06, LLM08).
3. Validate IDs automatically against ATLAS-latest.yaml from the atlas-data repository each month, and flag IDs that no longer exist, as happened with AML.T0024.000 to .002.
4. Record fit, strong or partial, and the reason, so nobody reads a mapping as full coverage.
5. Attach controls once: map both to your own control catalogue so evidence is collected once and reported in either framework.
6. Review after each OWASP edition and quarterly for ATLAS, with an owner named in the GRC register.

Detection and measurement

• Coverage by tactic: for each ATLAS tactic relevant to your platforms, the share of reachable techniques with at least one detection or preventive control.
• Coverage by OWASP entry: the share of LLM applications with a documented control and a passing test for each of the ten entries.
• Mapping freshness: days since the mapping was validated against the latest ATLAS release; target under 35.
• Stale IDs: count of retired or renamed ATLAS IDs still referenced in tooling, tickets or reports; target zero.
• Findings with dual tags: percentage of AI red team and test findings carrying both an OWASP entry and an ATLAS technique.

Common failure patterns

• Treating the Top 10 as a threat model: teams tick ten boxes and miss reconnaissance, credential access and persistence steps that ATLAS would have surfaced.
• Treating ATLAS as a checklist: trying to "comply" with every technique produces a huge, unprioritised backlog. Filter by platform tag and reachability first.
• Unversioned references: "LLM07" or "AML.T0024.000" without a version or release date becomes wrong when either framework moves.
• Ignoring identity: both frameworks describe agent tool abuse, but neither fixes it without scoped credentials. ATLAS includes AML.T0098 AI Agent Tool Credential Harvesting and AML.T0083 Credentials from AI Agent Configuration; map those to the agent's real tokens and service accounts.
• One-way mapping: mapping OWASP to ATLAS but not back means red team findings never reach developer backlogs in terms developers recognise.

Framework mapping

Practitioner checklist

• Record the OWASP edition (2025) and ATLAS release on every mapping, report and tool configuration.
• Use OWASP entries as design review prompts and acceptance criteria for every LLM feature.
• Threat model each AI platform with ATLAS tactics, filtered by platform tag.
• Tag every AI test and red team finding with both an OWASP entry and an ATLAS technique.
• Remap the retired AML.T0024 sub-technique references used for LLM02 to AML.T0137 and its sub-techniques.
• Add closest ATLAS techniques for LLM05, LLM06 and LLM08, marked as partial fit.
• Validate ATLAS IDs monthly against ATLAS-latest.yaml and alert on retired IDs.
• Map agent techniques (AML.T0053, AML.T0086, AML.T0098) to the actual credentials each agent holds.
• Use ATLAS case studies, such as AML.CS0059, in developer training to show OWASP risks chaining.

Standards and references

• OWASP GenAI Security Project: Top 10 for LLM Applications 2025
• OWASP: Top 10 for LLM Applications source repository
• MITRE: ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems)
• MITRE: ATLAS data repository
• MITRE ATLAS: EchoLeak case study AML.CS0059
• OWASP GenAI Security Project: Top 10 for Agentic Applications for 2026

Related NHI Mgmt Group resources: MITRE ATLAS Guide · OWASP Top 10 for LLM Applications Guide · AI Security Frameworks Crosswalk Guide · OWASP GenAI Red Teaming Manual Guide · Prompt Injection Guide