MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems

Summary

MITRE ATLAS is the definitive threat modeling framework for AI systems, offering a systematic way to understand how adversaries attack machine learning models and AI infrastructure. Unlike general cybersecurity frameworks, ATLAS is purpose-built for AI threats, cataloging 15 distinct tactics from initial reconnaissance through impact, supported by 66 techniques and 46 sub-techniques drawn from real-world attacks. With 33 documented case studies spanning everything from adversarial examples against image classifiers to data poisoning attacks on recommendation systems, ATLAS provides the concrete intelligence security teams need to defend AI systems in production.

The ATLAS Attack Matrix: Your AI Threat Roadmap

At the heart of ATLAS lies a comprehensive attack matrix that maps adversarial tactics across the AI system lifecycle. The 15 tactics progress logically through an attack sequence:

Pre-Attack Phase:

• Reconnaissance - Gathering intelligence about target AI systems
• Resource Development - Building attack infrastructure and capabilities

Attack Execution:

• Initial Access - Gaining entry to AI systems or data pipelines
• ML Model Access - Obtaining model artifacts or API access
• Execution - Running malicious code within AI environments
• Persistence - Maintaining long-term access to systems

Impact Tactics:

• Defense Evasion - Avoiding detection while attacking models
• Discovery - Learning about model architecture and training data
• Collection - Harvesting model parameters or training datasets
• ML Attack Staging - Preparing sophisticated model-specific attacks
• Exfiltration - Stealing intellectual property or sensitive data
• Impact - Degrading model performance or causing misclassification

Each tactic contains multiple techniques with specific implementation details, making ATLAS a practical playbook for both attackers and defenders.

Who This Resource Is For

Security architects and engineers working on AI-powered products who need to conduct threat modeling exercises and implement appropriate security controls.

ML engineers and data scientists responsible for model deployment and monitoring who want to understand how their systems can be compromised and what defensive measures to implement.

Risk management professionals tasked with assessing AI system vulnerabilities and communicating threat landscapes to executive leadership and board members.

Incident response teams investigating suspected attacks on AI systems who need structured frameworks for understanding adversarial techniques and their indicators.

Compliance and audit teams working with organizations deploying AI in regulated industries who need systematic approaches to documenting AI security postures.

Real-World Attack Intelligence

What sets ATLAS apart from theoretical frameworks is its grounding in documented attacks. The 33 case studies cover the full spectrum of AI vulnerabilities:

Model Extraction Attacks: The framework documents how researchers successfully stole commercial image classification models by systematically querying APIs and reconstructing model parameters.

Adversarial Examples in Production: Real cases where subtle input modifications caused production systems to misclassify images, including attacks against facial recognition and autonomous vehicle perception systems.

Data Poisoning Campaigns: Documented instances where attackers corrupted training datasets to introduce backdoors or bias, including attacks on recommendation systems and content moderation models.

Prompt Injection Attacks: Recent cases involving large language models where carefully crafted inputs bypassed safety filters or extracted sensitive training data.

Each case study includes technical details about attack vectors, defensive gaps, and lessons learned, making ATLAS an invaluable source of threat intelligence.

Implementing ATLAS in Your Security Program

Start with threat modeling workshops using the ATLAS matrix to systematically identify potential attack vectors against your specific AI systems. Focus on the tactics most relevant to your deployment model - API-based services face different risks than embedded models.

Map existing security controls to ATLAS techniques to identify coverage gaps. Many traditional cybersecurity tools don't address AI-specific threats like model inversion or membership inference attacks.

Develop monitoring capabilities based on ATLAS indicators of compromise. This includes detecting unusual query patterns that might indicate model extraction attempts or input distributions that suggest adversarial examples.

Create incident response playbooks organized around ATLAS tactics, ensuring your security team knows how to investigate and respond to AI-specific attacks rather than treating them as generic security incidents.

Use ATLAS mitigations as a checklist for hardening AI systems, from input validation and rate limiting to differential privacy and model obfuscation techniques.

Watch Out For: Common ATLAS Implementation Pitfalls

Over-focusing on adversarial examples while ignoring supply chain attacks on training data or model theft through API abuse. ATLAS shows that successful attacks often combine multiple techniques.

Assuming traditional security tools provide adequate coverage for AI threats. Many ATLAS techniques require specialized detection and response capabilities that don't exist in conventional security stacks.

Treating ATLAS as a compliance checklist rather than a living threat intelligence resource. The framework is most valuable when used to understand adversarial thinking, not just catalog defensive controls.

Neglecting the business context of different ATLAS techniques. Not every attack vector poses equal risk to your specific use case - prioritize based on your threat model and business impact.