Review scope and evidence labels
This page profiles MITRE ATLAS as a public adversary-knowledge framework for AI-enabled systems.
Evidence labels used on this page:
- Community security knowledge base — based on public MITRE ATLAS material.
- Community security guidance — based on public security community material such as OWASP.
- Official guidance — based on public official guidance such as NIST material.
- Author analysis — interpretation for AI security, governance, architecture and operational risk on this site.
- Implementation evidence required — local records that must exist before a mitigation can be treated as operating.
This entry is a Research profile, not a claim of offensive AI red-team certification. Its purpose is to support AI threat modeling, architecture review and executive risk communication.
Methodology snapshot
| Field | Value |
|---|---|
| Category | AI adversary knowledge and threat modeling |
| Research type | Methodology / knowledge base |
| Core question | What tactics and techniques can adversaries use against AI-enabled systems? |
| Main scope | AI systems, machine-learning pipelines, LLM applications, AI agents, model-enabled services and supporting data flows |
| Useful outputs | Attack vocabulary, abuse cases, threat-model inputs, control mapping and monitoring requirements |
| Companion methods | AI Threat Modeling, OWASP LLM Top 10, NIST AI RMF, AI Security Architecture |
| Review status | Public MITRE ATLAS site reviewed; local use requires system-specific mapping |
What MITRE ATLAS is
MITRE ATLAS is a public knowledge base of adversary tactics, techniques and case studies relevant to AI-enabled systems. Evidence: Community security knowledge base.
It is useful because AI risk discussions can otherwise remain vague. ATLAS gives security teams a more concrete vocabulary for how AI systems may be targeted, manipulated or misused. Evidence: Community security knowledge base; Author analysis.
For this site, MITRE ATLAS is used as an input to AI Threat Modeling and AI Security Architecture, especially when explaining AI attack paths to management, auditors or architecture review boards. Evidence: Author analysis.
What it is not
MITRE ATLAS is not:
- a certification,
- a compliance guarantee,
- a complete security program,
- a replacement for local threat modeling,
- a replacement for secure software development,
- proof that an organisation performs AI red teaming,
- proof that a specific AI system is secure.
A team should use ATLAS as an input to analysis, not as a badge. The practical output should be a system-specific threat model, control decisions and retained evidence. Evidence: Author analysis; Implementation evidence required.
How it supports AI threat modeling
MITRE ATLAS can help structure AI threat modeling by turning broad concerns into adversary-oriented questions.
| Use | Practical question | Evidence to retain |
|---|---|---|
| Attack vocabulary | Which AI-relevant adversary techniques apply to this system? | Mapped techniques and rationale |
| Abuse-case design | How could a malicious user, insider or external actor misuse the AI workflow? | Abuse cases and assumptions |
| Control mapping | Which controls reduce likelihood or impact? | Mitigation record and residual risk |
| Detection planning | What telemetry would reveal this technique or failure mode? | Logging, monitoring and alert design |
| Executive reporting | How can risk be explained without vague AI language? | Risk summary and accepted decisions |
Evidence: Community security knowledge base; Author analysis.
Relationship to existing Research entries
MITRE ATLAS connects to:
- AI Threat Modeling — ATLAS provides adversary vocabulary for threat models.
- AI Security Architecture — adversary patterns become architecture-review questions.
- OWASP Top 10 for LLM Applications — OWASP helps with LLM application risk classes; ATLAS helps with broader AI adversary techniques.
- AI Governance — risk ownership and acceptance must be assigned.
- Secure AI-Assisted Development — generated or AI-enabled software needs adversary-aware review.
- Zero Trust — identity, least privilege and telemetry reduce impact when AI workflows are abused.
Governance relevance
MITRE ATLAS can help governance teams avoid generic statements such as “AI may be attacked” and instead describe concrete threat scenarios, assumptions and mitigations. Evidence: Author analysis.
It can support an AI risk register, architecture review, red-team planning, vendor assessment, incident-response preparation and audit-evidence checklist. Evidence: Author analysis; Implementation evidence required.
Current limitations and follow-up research
This entry is a public Research profile. It does not replace reading the current MITRE ATLAS material and does not determine which techniques apply to a specific AI system.
Follow-up Research nodes should cover:
- Prompt Injection,
- RAG Security,
- AI Red Teaming,
- Model Supply Chain Security,
- IAM for AI systems.