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MITRE ATLAS: AI Threat Techniques Mapped to Security Controls

Frameworks & Standards · June 30, 2026 · 13 min read

MITRE ATLAS is the Adversarial Threat Environment for AI Systems, a knowledge base of AI-specific attack techniques and threat models created by MITRE and the AI security community. It catalogs real-world adversarial ML attacks organized by tactics, from reconnaissance through model exfiltration. Unlike MITRE ATT&CK, which focuses on OS and network adversaries, ATLAS directly models threats to machine learning systems, training data, and AI pipelines. ATLAS techniques include model poisoning, prompt injection, membership inference, supply chain compromise, and evasion attacks. Organizations use ATLAS to assess risks in AI workflows, build threat models aligned with adversary goals, and design detection and prevention controls that close known attack paths before deployment.

What is MITRE ATLAS?

MITRE ATLAS is a collaboratively developed framework that documents adversarial ML techniques in a structured taxonomy. The knowledge base organizes techniques into a matrix where rows represent tactics (reconnaissance, resource development, training data poisoning, model access, evasion, impact, exfiltration) and columns represent specific techniques within each tactic.

ATLAS emerged from the realization that ATT&CK, despite its breadth across infrastructure and enterprise security, does not adequately capture threats specific to machine learning pipelines. A data scientist poisoning training data, an adversary performing model inversion attacks, or a threat actor executing prompt injection against an LLM are following AI-specific playbooks that ATT&CK does not map. ATLAS fills that gap by providing a language and taxonomy for adversarial ML.

Each ATLAS technique includes threat descriptions, real-world case studies, defensive mitigations, and references to academic research. The framework is open and community-driven, updated as new techniques emerge and defenses evolve.

How MITRE ATLAS Differs from MITRE ATT&CK

ATT&CK is the adversary tactics and techniques matrix for enterprise IT systems. It focuses on operating systems, network protocols, cloud services, and traditional IT infrastructure. An ATT&CK technique might describe lateral movement via credential theft or data exfiltration via cloud storage APIs.

ATLAS is purpose-built for AI systems. It assumes a different threat model where the attacker's goal is to manipulate, steal, or degrade machine learning models and their training data. A reconnaissance technique in ATLAS is not port scanning, but rather probing a model's behavior to infer its architecture or training data composition. An exfiltration technique is not stealing files via FTP, but extracting training data via membership inference attacks or stealing the model weights themselves.

The two frameworks are complementary. An attacker might combine them: use ATT&CK techniques to gain network access to a data center, then apply ATLAS techniques to poison the training data pipeline or exfiltrate model weights. Effective AI security requires both frameworks and the integration of controls that address threats at both layers.

MITRE ATLAS Tactics and Representative Techniques

ATLAS organizes techniques into seven primary tactics. Understanding each tactic helps teams design controls aligned with adversary intentions.

Reconnaissance

Reconnaissance in ATLAS covers activities where attackers gather information about AI systems without direct access. Techniques include scanning for model endpoints, fingerprinting model architecture through API queries, and researching training data composition through public datasets and documentation.

An attacker might query a language model API with test prompts to infer its size, training data era, and capabilities. They might scrape public model cards or GitHub repositories to identify the datasets used. These low-friction reconnaissance activities inform the attacker's later exploitation strategy.

Resource Development

Resource development encompasses preparation for attacks: acquiring or developing attack tools, creating synthetic training data, or establishing access to compute resources. An attacker might create a poisoned dataset, develop code to perform adversarial perturbations, or rent GPU compute for model stealing.

Training Data Poisoning

Training data poisoning directly manipulates the data used to train models. Techniques include backdoor attacks (inserting hidden triggers that cause specific misclassifications), label flipping (corrupting ground truth labels), and feature pollution (adding spurious correlations to exploit later).

A backdoor attack might inject training examples where a stop sign with a small sticker is misclassified as a speed limit sign. When deployed, the model appears normal but executes the attacker's intent when the trigger appears.

Model Access

Model access techniques exploit access to a deployed model to steal weights, steal training data via membership inference, or perform evasion attacks. An attacker with API access might query the model in ways that extract its decision boundaries or training data statistics.

Techniques include model extraction (stealing weights by querying a public API), membership inference (determining whether a specific record was in the training set), and model inversion (reconstructing training data from model outputs).

Evasion

Evasion techniques bypass deployed models through adversarial examples. Adversarial examples are inputs crafted to cause misclassification despite being imperceptibly close to legitimate data. A stop sign with pixel-level perturbations becomes a speed limit sign in the model's perception.

Evasion is distinct from poisoning: the model itself is not compromised, but the attacker exploits mathematical properties of the model's decision boundary.

Impact

Impact techniques are post-exploitation: degrading model availability, triggering resource-exhaustive queries, or causing persistent misclassifications. An attacker might flood a model API with adversarial inputs that trigger excessive computation, performing a denial-of-service attack.

Exfiltration

Exfiltration techniques steal assets from AI systems: model weights, training data, or sensitive information leaked through model outputs. Techniques include membership inference (inferring training data membership), model extraction (stealing weights via querying), and privacy attacks that extract PII from language models.

Mapping ATLAS Techniques to Detection Controls

Detection controls translate ATLAS techniques into observable activities that security tools can flag. A data poisoning attack leaves evidence in data integrity monitoring. A model extraction attack generates unusual API query patterns. Runtime controls can intercept and log these signals before exploitation.

Data Integrity and Poisoning Detection

Training data poisoning should trigger alerts when statistical anomalies appear in dataset summaries. Changes to training data distributions, sudden label flips, or injection of out-of-distribution examples can be detected by monitoring data quality metrics.

Example detection: an automated ML pipeline should monitor class distributions, feature statistics, and data provenance. A sudden 10 percent drop in minority class examples, or injection of impossible feature combinations, indicates potential poisoning.

API Query Anomalies and Model Extraction

Model extraction attacks often follow predictable patterns: high-volume API queries, systematic sampling across input space, or queries designed to extract decision boundaries. Detection systems should flag unusual query patterns.

An attacker extracting a model might query a classification API with hundreds of perturbed variants of the same input, exploring decision boundaries. Legitimate users typically query with natural, diverse inputs from their domain. High-volume, systematic, or boundary-seeking queries warrant investigation.

Membership Inference Signals

Membership inference attacks try to determine whether a specific record was in the training set by observing model behavior on that record versus non-training records. The attacker submits many candidate records and measures prediction confidence or loss values. High-confidence predictions on specific individuals followed by low confidence on random records suggests membership inference.

Detection systems can flag anomalous querying patterns: one user submitting hundreds of candidate records for the same model in a short time window.

Runtime Enforcement and Decision Logging

Runtime control systems like Vaikora can enforce policies that block or constrain suspicious queries before model execution. A policy might restrict API rate limits based on user or session, block queries that appear to be probing decision boundaries, or require approval for bulk data extraction requests.

Critically, runtime systems log every decision: ALLOW, LOG, CONSTRAIN, or BLOCK. These decision logs provide tamper-evident evidence of control effectiveness and can feed into security operations workflows.

Detection Example: Query Pattern Analysis for Model Extraction

Here is a Sigma detection rule that flags potential model extraction activity by detecting systematic API queries:

title: Potential Model Extraction via Systematic API Queries
id: ai-model-extraction-detection
status: experimental
description: Detects high-volume, systematic API queries to deployed ML models that may indicate model extraction attacks. High query counts with low input diversity and rapid request rates suggest adversarial probing.
author: Security Detection Team
date: 2026-06-30
logsource:
  category: api_activity
  product: machine_learning_platform
detection:
  api_calls:
    api.method: predict
    api.endpoint|contains: model
  filter_volume:
    api.request_count: '>50'
    api.request_rate: '>5'
    api.input_similarity_score: '>0.85'
  filter_high_volume:
    api.request_count:
      - '>200'
      - '>500'
    api.request_rate: '>20'
  timeframe: 5m
  condition: api_calls and (filter_volume or filter_high_volume)
falsepositives:
  - Legitimate batch inference jobs with high query volumes
  - Automated retraining pipelines that systematically test models
  - Synthetic data generation from deployed models
level: medium
tags:
  - mitre_atlas.model_access
  - mitre_atlas.evasion

This rule detects: 1. High volume of distinct queries to the same model (>50 in 5 minutes) 2. Rapid request rates (>5 requests per second, or >20 for alert escalation) 3. High input similarity (adversarial perturbations share structural patterns)

Legitimate users typically have lower query counts, slower rates, and diverse input distributions. Deploy this rule in your SIEM and tune thresholds based on your own model traffic baseline.

Integrating ATLAS into Threat Modeling

Threat modeling for AI systems should start with ATLAS. The process follows these steps:

First, identify the AI assets in scope: models, training data, model weights, inference APIs, and supporting data pipelines. Document what each asset does and its sensitivity.

Second, map ATLAS techniques to each asset. Which reconnaissance techniques apply? Are there data poisoning risks? Can attackers access the model API? What evasion attacks are feasible?

Third, for each credible technique, identify existing controls. Does data lineage tracking catch poisoning? Does rate limiting prevent extraction? Does adversarial testing catch evasion?

Fourth, identify gaps and prioritize. High-impact, low-defended techniques warrant immediate investment. Consensus threat modeling sessions with data scientists, ML engineers, and security teams ensure that no critical pathway is overlooked.

Building Runtime Controls Aligned with ATLAS

Runtime controls sit between an AI system and its execution environment, enforcing policy on every action. They address several ATLAS techniques directly:

Model access controls can restrict which users or applications can query a model, and under what conditions. A policy might require approval for bulk data extraction or constrain the rate and volume of queries to a single model.

Data access controls can gate which training data is visible to which parts of the pipeline, reducing poisoning surface. Policies can enforce that new training data undergoes validation before integration.

Prompt inspection can flag injection attempts, jailbreak patterns, and requests for sensitive information. A policy can constrain model outputs to avoid leaking training data (addressing exfiltration) or trigger alerts on suspicious prompts.

Decision logging provides evidence of control enforcement, creating an auditable trail of every policy decision. This log becomes input to security operations: alerts, incident investigation, and compliance reporting.

Key Standards and Frameworks for AI Security

NIST AI Risk Management Framework (AI RMF) provides guidance for governing AI systems and managing risks across the lifecycle. AI RMF maps to ATLAS by offering control recommendations for each risk category.

ISO 42001 (Artificial Intelligence Management System) establishes requirements for AI governance, risk management, and controls. Organizations pursuing ISO 42001 certification often reference ATLAS in threat modeling documentation.

OWASP released the Adversarial Robustness Evaluation Platform (AREP) and AI security top 10s to highlight prevalent attack vectors. OWASP's work overlaps with ATLAS and complements it with defensive prescriptions.

CISA and CSA (Cloud Security Alliance) have both published AI security guidance that references ATLAS and encourages its use in threat modeling.

Regional regulations like the EU AI Act require risk assessments for high-risk AI systems. Organizations can use ATLAS as one input to systematic threat identification when developing these risk assessment documentation requirements.

Common Challenges in Applying ATLAS

Challenge: ATLAS techniques are often abstract. A threat modeling session might identify "model extraction" as a risk, but the team may struggle to connect it to observable activities or concrete detection signals. Bridging that gap requires collaboration between security engineers (who understand detection) and ML practitioners (who understand model behavior).

Challenge: Not all ATLAS techniques apply equally to all models. A private, read-only inference API has different extraction risks than a fine-tunable foundation model. Threat modeling must be scoped to the actual deployment model and threat actors relevant to the organization.

Challenge: Defenses are often model-dependent. Adversarial robustness training, input perturbation detection, and membership inference defenses all require tuning to the specific model architecture and domain. One-size-fits-all solutions rarely work.

Challenge: ATLAS is evolving. New techniques emerge as adversaries innovate. Teams should revisit threat models periodically and stay informed of ATLAS updates and emerging research.

Operationalizing ATLAS in Your Security Program

Start by selecting a few high-impact AI assets and conducting a focused threat modeling session using ATLAS. Document the threats you identify and the controls you have.

Implement detection for the highest-priority techniques: model extraction and data poisoning typically pose immediate risk for most organizations. Use the Sigma rule provided above or translate it to your SIEM's native language (KQL, SPL, etc.). Add detections that flag query patterns or data anomalies associated with these techniques.

Introduce runtime enforcement: a policy layer that requires approval for bulk queries, rate-limits API access, or inspects training data before ingestion. Runtime controls address ATLAS techniques at action time, before they execute.

Create an incident response playbook that maps ATLAS technique discovery to investigation procedures, remediation steps, and stakeholder notifications. Ensure your SOC knows how to respond when a detection fires.

Integrate ATLAS into your security team's operational muscle memory. Include ATLAS threat scenarios in tabletop exercises, security reviews, and architecture decisions. As your team internalizes the framework, threat modeling becomes faster and more effective.

Frequently asked questions

What is MITRE ATLAS?

MITRE ATLAS is the Adversarial Threat Environment for AI Systems, a knowledge base of attack techniques and threat models for machine learning and AI systems. It organizes techniques by tactic (reconnaissance, resource development, data poisoning, model access, evasion, impact, exfiltration) and provides real-world examples, mitigations, and research references. ATLAS helps organizations model threats to AI pipelines and design appropriate defenses.

How is MITRE ATLAS different from MITRE ATT&CK?

ATT&CK catalogs techniques for traditional IT infrastructure and operating systems. ATLAS is purpose-built for AI and machine learning threats. They are complementary: ATT&CK describes network and OS adversary tactics, while ATLAS describes adversarial ML techniques like model poisoning, extraction, and evasion attacks. Comprehensive AI security requires understanding both frameworks.

What AI attacks does MITRE ATLAS cover?

ATLAS covers seven primary tactics and dozens of techniques, including model extraction (stealing weights via API queries), membership inference (determining training data membership), data poisoning (corrupting training data with backdoors or label flips), adversarial examples (imperceptible input perturbations that cause misclassification), model inversion (reconstructing training data from outputs), and supply chain compromise (poisoning dependencies or models before deployment). It also covers reconnaissance, resource development, and impact attacks.

How do you use MITRE ATLAS for AI threat modeling?

Identify AI assets in scope (models, data, pipelines). Map ATLAS techniques to each asset by evaluating which attacks are feasible and credible. Assess existing controls against each technique. Identify gaps and prioritize based on impact and likelihood. Use the prioritized threat list to drive security investments in detection, runtime enforcement, testing, and monitoring. Revisit the model periodically as threats and systems evolve.

What is model extraction?

Model extraction is an attack where an adversary queries a deployed machine learning model to steal its weights or functionality. By systematically probing the model's outputs, an attacker can reverse-engineer its decision boundaries and learn an equivalent model. This is a critical concern for proprietary models and is covered as a technique in MITRE ATLAS under the model access tactic.

How can I detect model extraction attacks?

Monitor API logs for unusual query patterns: high-volume requests, systematic sampling across input space, or queries that probe decision boundaries. Detect anomalies in request rates, input diversity, and prediction confidence distributions. Runtime controls can rate-limit API access or require approval for bulk queries. Use the Sigma detection rule provided in this article to alert on high-volume, low-diversity, systematic queries in your SIEM.

What is membership inference?

Membership inference is a privacy attack where an adversary determines whether a specific record was used to train a machine learning model. By observing model behavior on that record versus non-training records, the attacker can infer membership. This technique poses risks for confidential or sensitive training data and is documented in MITRE ATLAS under the model access and exfiltration tactics.

Is MITRE ATLAS applicable to large language models?

Yes. Large language models face prompt injection attacks, jailbreaks, membership inference, model extraction via querying, and exfiltration of training data through careful prompt engineering. ATLAS provides a taxonomy for modeling these risks. LLM-specific threat modeling should reference ATLAS tactics and OWASP LLM top 10s for a complete picture.

What should my organization prioritize first?

Start with the ATLAS techniques that pose the highest risk to your most valuable AI assets. For most organizations, model extraction and data poisoning are early priorities. Implement detection for these techniques, introduce runtime controls to gate model access and data flows, and conduct periodic threat modeling to ensure coverage expands as systems evolve.

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