MITRE ATLAS: The Security Framework Built for the Age of AI

Everyone knows the MITRE ATT&CK framework. Since its introduction, it has fundamentally changed how security teams think about adversary behavior, detection coverage, and threat modeling. It gave defenders a shared language and a structured way to map attacks, build detection rules, and measure coverage against real-world threats.

But ATT&CK was built for traditional IT environments — servers, endpoints, networks, credentials. The threat surface it covers is well understood.

Artificial intelligence introduces an entirely different kind of threat surface. One that most organizations are not yet equipped to defend. And for that, there is MITRE ATLAS.

What is MITRE ATLAS?

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversarial tactics, techniques, and real-world case studies focused entirely on attacks against AI and machine learning systems.

Where ATT&CK maps how adversaries compromise networks and endpoints, ATLAS maps how adversaries compromise the AI models increasingly powering those systems. It covers the full lifecycle of an AI system — from design and training through to production deployment — and documents real-world cases of evasion, data poisoning, model manipulation, and more.

ATLAS currently defines 15 tactics that adversaries can use to attack AI systems. It is still evolving, but it already gives security teams a structured foundation to think about AI-specific threats the same way ATT&CK helped them think about endpoint and network threats.

Navigator: https://atlas.mitre.org/navigator Website: https://atlas.mitre.org

Why AI Systems Need Their Own Security Framework

Traditional security frameworks were not designed with machine learning pipelines, training data, model inference, or AI APIs in mind. The attack surface of an AI system is fundamentally different from a conventional application.

Consider what makes an AI system uniquely vulnerable:

• It learns from data, which means corrupting that data is a form of attack

• Its behavior is shaped by its training process, which can be manipulated before deployment

• Its outputs can be influenced by carefully crafted inputs, even without direct model access

• It often operates as a black box, making adversarial influence difficult to detect

• It can be weaponized against the very organization that built and deployed it

None of these attack vectors map cleanly onto existing security frameworks. ATLAS was built specifically to close that gap.

What MITRE ATLAS Covers

ATLAS organizes AI threats across the full adversarial lifecycle, mirroring the structure of ATT&CK but adapted for the realities of AI systems.

Reconnaissance Against AI Systems

Before executing an attack, adversaries gather information about the target AI system. This includes identifying the model type, training methodology, data sources, and API behavior. Public documentation, open-source model releases, and repeated API queries can all be exploited during this phase to build a detailed picture of what the system does and how it can be manipulated.

ML Attack Staging

Adversaries prepare their capabilities before launching an attack. This may involve training a local surrogate model, generating adversarial examples, or acquiring specialized tools for AI manipulation. This preparation phase mirrors the resource development phase in ATT&CK and is equally important for defenders to understand.

Model Evasion

Evasion is one of the most studied areas in adversarial machine learning. It involves crafting inputs that cause a model to produce incorrect outputs without triggering any visible anomaly. In security contexts, evasion attacks are used to bypass AI-powered intrusion detection systems, fraud detection engines, and content moderation tools. A model that is confident and wrong is far more dangerous than one that is uncertain.

Data Poisoning

Because AI models learn from training data, poisoning that data corrupts the model's behavior at the source. An adversary who can influence the training dataset can introduce hidden backdoors, degrade accuracy on specific inputs, or engineer the model to behave maliciously under conditions the adversary controls — often without any visible sign of compromise during normal operation.

Model Theft and Inversion

Through repeated queries to a model's API, adversaries can reconstruct a functional copy of the model without ever accessing the underlying weights or architecture directly. Model inversion attacks can go further, extracting sensitive information about the training data itself — including personally identifiable information that the organization believed was protected.

Inference Manipulation

Adversaries can manipulate model outputs during deployment without ever modifying the model itself. Prompt injection attacks against large language models are the most current and widely discussed example, and they are increasingly relevant as LLMs are integrated into security tooling, customer-facing applications, and internal automation workflows.

AI Attacks Are Already Happening

ATLAS is not a theoretical framework for future threats. AI-targeted attacks and AI-assisted attacks are already occurring at scale, and the data is stark.

The FBI reported 16.6 billion dollars in cybercrime losses in 2024, with AI-powered scams representing a rapidly growing share of that figure.

A finance employee at a multinational company authorized a 25 million dollar transfer after a video call with what appeared to be multiple senior colleagues. Every person on that call was a deepfake. The employee had no reason to suspect anything was wrong until the fraud was discovered.

The European Union Agency for Cybersecurity has documented a steep increase in AI-powered phishing and social engineering attacks, with AI enabling adversaries to produce convincing, personalized content at a scale that was previously impossible to sustain.

The UK National Cyber Security Centre has assessed that AI will almost certainly increase both the volume and quality of cyberattacks through at least 2027, with nation-state actors and cybercriminals already integrating AI into their offensive operations.

Deepfake-based fraud, which was a rare occurrence just a few years ago, has grown into hundreds of documented incidents per month, and the detection gap is widening as generation quality continues to improve.

The threat is not theoretical. It is operational.

Why ATLAS Matters for Security Teams

Security teams that have invested in ATT&CK-based detection and threat modeling now face a new challenge. As AI becomes embedded in critical infrastructure, business operations, and security tooling itself, the attack surface extends well beyond what ATT&CK was designed to cover.

ATLAS applies the same structured, practitioner-oriented approach that made ATT&CK valuable, specifically to AI threats. It helps security teams across four key areas:

Detection engineering for AI systems. Just as ATT&CK informed behavioral detection rules for endpoint and network activity, ATLAS can inform detection logic for AI pipelines, model APIs, training data integrity monitoring, and inference behavior anomalies.

Threat modeling before deployment. Before an AI model goes into production in a sensitive context, ATLAS enables security and engineering teams to systematically assess which techniques could be used against it and what controls are needed to reduce that risk.

Incident response for AI-related events. When an AI system behaves unexpectedly, ATLAS provides a taxonomy for assessing whether that behavior is the result of adversarial manipulation and where in the lifecycle a compromise may have occurred.

Regulatory and compliance readiness. As governments and regulators develop AI security requirements, frameworks like ATLAS are likely to inform those standards. Organizations that adopt it early will be better positioned than those who wait.

ATT&CK and ATLAS: Stronger Together

ATLAS was designed to complement ATT&CK, not replace it. Many attacks against AI systems begin with techniques that ATT&CK already covers. An adversary who wants to poison a training dataset first needs access to the environment where that data is stored, which typically involves credential theft, lateral movement, or vulnerability exploitation — all mapped in ATT&CK.

ATLAS picks up where ATT&CK ends. Once an adversary has access to the AI system or its supporting infrastructure, the techniques they use to manipulate, extract from, or weaponize that system are what ATLAS documents.

Using both frameworks together gives security teams the most complete picture of the threats they face.

How to Start Using ATLAS

Organizations looking to incorporate ATLAS into their security program can begin with four practical steps.

Inventory your AI systems. Identify every AI model and ML pipeline in your environment: what data it processes, how it is accessed, who uses it, and what decisions it informs. This inventory is the foundation for everything else.

Use the ATLAS Navigator to assess coverage. Similar to the ATT&CK Navigator, the ATLAS Navigator lets you map which techniques you have visibility into and which represent gaps in your current detection and protection posture.

Prioritize based on your exposure. Not every ATLAS technique is equally relevant to every organization. Focus first on techniques most applicable to how your AI systems are deployed and what data they touch.

Bridge security and machine learning teams. AI security is a shared responsibility. Detection engineers, data scientists, and model owners all have roles to play, and ATLAS gives them a common vocabulary to work from.

The Bottom Line

The pace of AI adoption in enterprise and government environments is not slowing down. Nor is the pace at which adversaries are learning to exploit AI systems and use AI capabilities for offensive operations.

Treating AI security as an afterthought is no longer viable. The organizations that will be most resilient are those that apply the same structured, evidence-based approach to AI threats that the security industry has built over decades for traditional threats.

MITRE ATLAS provides that structure. It is still evolving, as the threat landscape itself is evolving, but it already gives security teams the foundation they need to start thinking, measuring, and defending against adversarial threats to AI systems.

The time to build that foundation is now, before AI becomes so deeply embedded in critical operations that a successful attack causes damage that is difficult or impossible to recover from.