MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversarial tactics and techniques specifically targeting AI and machine learning systems. Think of it as the AI-focused sibling of MITRE ATT&CK®, but designed to capture the unique ways adversaries can manipulate AI models and pipelines.

It catalogs:

Tactics (the “why” – attacker’s goals, e.g., model theft, evasion, poisoning).
Techniques (the “how” – methods used, e.g., adversarial examples, prompt injection).
Case Studies (real-world incidents).
Mitigations (recommended defenses).

You can explore it here: atlas.mitre.org

How to Use MITRE ATLAS for Threat Modeling

1. Define System Scope

Start by mapping your AI system:

Data sources (training + inference data)
Model architecture (LLMs, classifiers, neural nets)
Interfaces (APIs, dashboards, endpoints)
Deployment environment (cloud, edge, on-premises)

This creates the foundation for your threat surface.

2. Map Potential Adversary Goals (Tactics)

Use ATLAS’s tactics categories to think like an attacker. Examples:

Initial Access: How could attackers inject poisoned data into training sets?
Model Theft: Could they repeatedly query your API to replicate your model?
Evasion: Could adversarial examples bypass your fraud detection model?
Impact: Could they manipulate outputs to cause financial/reputational loss?

3. Identify Techniques Relevant to Your System

For each tactic, select techniques from ATLAS that apply:

Data Poisoning → maliciously crafted datasets.
Adversarial Examples → manipulated inputs to misclassify.
Model Inversion → extracting sensitive data from models.
Prompt Injection → forcing LLMs to override safety.

4. Simulate & Prioritize Threats

Use ATLAS case studies and technique descriptions to simulate attacks:

Example: Red team generates adversarial text prompts against your chatbot.
Example: Poisoned data is introduced into your training pipeline to test resilience.

Assess impact vs likelihood to prioritize which threats need mitigation first.

5. Plan Mitigations

For each identified threat, apply ATLAS-suggested countermeasures:

Adversarial Training → harden models against adversarial inputs.
Data Validation & Filtering → prevent data poisoning.
Access Control → protect model APIs from extraction.
Monitoring & Logging → detect unusual query patterns.

6. Continuous Improvement

Incorporate ATLAS into Secure SDLC & DevSecOps pipelines.
Run AI red teaming exercises using ATLAS techniques.
Keep models under continuous monitoring for drift, anomalies, and attacks.

Example: Threat Modeling a Fraud Detection AI

Tactic: Evasion
Technique (ATLAS): Adversarial Examples
Scenario: Attackers generate slightly modified fraudulent transactions that bypass the model.
Mitigation: Adversarial retraining + anomaly detection system.

MITRE ATLAS is not just a library of attacks – it’s a living framework that helps teams:

Think adversarially when designing AI.
Integrate AI security into threat modeling exercises.
Standardize communication between developers, security teams, and regulators.

By incorporating ATLAS into your AI threat modeling, you build AI systems that are robust, transparent, and resilient against evolving adversaries.

What is MITRE ATLAS?