MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems

Summary

MITRE ATLAS stands out as the first comprehensive knowledge base that treats AI systems like any other enterprise technology that needs cybersecurity attention. Born from MITRE's decades of cybersecurity expertise (they created the famous CVE database and MITRE ATT&CK framework), ATLAS translates traditional threat modeling into the unique world of machine learning. Instead of abstract AI safety discussions, it catalogs real attacks that have actually happened—from adversarial examples that fool image classifiers to data poisoning attacks that corrupt training datasets. Think of it as the "CVE database for AI attacks" that security teams have been desperately needing.

The Threat Taxonomy That Actually Makes Sense

ATLAS organizes AI attacks using a familiar structure for cybersecurity professionals: tactics (the "why"), techniques (the "how"), and procedures (the specific implementation). This isn't academic theory—it's based on 14 detailed case studies of real-world AI attacks, including the famous poisoning of Microsoft's Tay chatbot and adversarial attacks against Tesla's Autopilot.

The framework covers the full AI attack lifecycle from reconnaissance (where attackers probe ML models for vulnerabilities) through execution (deploying adversarial examples) to impact (achieving their malicious goals). Each technique includes detection methods, mitigation strategies, and links to the broader cybersecurity ecosystem that security teams already understand.

Who this resource is for

• Primary audience : Security architects, threat modelers, and cybersecurity professionals who need to extend their existing security programs to cover AI systems but lack ML-specific attack knowledge.
• Secondary audience : AI/ML engineers and data scientists who understand their models but need cybersecurity context to identify vulnerabilities and implement appropriate defenses.
• Also valuable for : Risk managers assessing AI deployments, compliance officers mapping AI risks to regulatory frameworks, and security vendors building AI-specific security tools.

What makes ATLAS different from other AI risk frameworks

Unlike high-level AI ethics principles or academic research papers, ATLAS provides actionable intelligence that maps directly to existing cybersecurity workflows. It uses the same tactical approach as MITRE ATT&CK, making it immediately familiar to security professionals who don't need to learn entirely new risk vocabularies.

The case studies are particularly valuable—they document actual attack vectors, affected systems, and successful mitigations from real incidents. This evidence-based approach contrasts sharply with theoretical risk frameworks that struggle to translate into concrete security measures.

ATLAS also integrates with existing threat intelligence platforms and security tools, unlike standalone AI governance frameworks that operate in isolation from operational security programs.

Getting started with threat modeling your AI systems

Begin by inventorying your AI/ML systems and mapping them to ATLAS tactics. Focus first on externally-facing models (APIs, recommendation systems, autonomous vehicles) as these present the largest attack surface.

Use the case studies to understand how similar attacks have been executed against systems like yours. The Tesla Autopilot case study, for example, provides specific technical details about adversarial perturbation attacks against computer vision systems.

For each AI system, work through the ATLAS matrix systematically: What reconnaissance techniques could attackers use? How might they access your training data? What adversarial techniques could compromise your model's outputs? Document these scenarios using ATLAS's standardized technique identifiers.

Common implementation pitfalls

Don't try to address every ATLAS technique at once—prioritize based on your actual threat model and risk tolerance. Many organizations get overwhelmed by the comprehensive nature of the framework and fail to implement any mitigations.

Avoid treating ATLAS as purely a technical security checklist. Many of the most effective mitigations are procedural (data validation, model monitoring, incident response) rather than algorithmic defenses against adversarial examples.

Remember that ATLAS is still evolving—the current version focuses heavily on computer vision and NLP attacks because that's where most documented incidents exist. Don't assume techniques not yet cataloged in ATLAS are safe to ignore.