Mitre-Atlas

Cybersecurity Framework

Definition

ATLAS stand for Adversarial Threat Landscape for Artifial-Intelligence Systems) is a globally accessible, living knowledge base of adversary tactics and techniques against AI-enabled systems based on real-world attack observations and realistic demonstrations from AI red teams and security groups.

Furthermore, ATLAS is modeled after and complementary to MITRE ATT&CK, raising awareness of the rapidly evolving vulnerabilities of AI-enabled systems as they extend beyond cyber.

Additionally, ATLAS complements rather than competes with OWASP LLM Top 10 and NIST AI RMF use all three for comprehensive coverage.

Understanding the distinction between ATLAS and ATT&CK helps security teams determine when to apply each framework.

Table: Comparison of MITRE ATT&CK and MITRE ATLAS frameworks

Aspect	        MITRE ATT&CK	                         MITRE ATLAS
Primary focus	Traditional IT/OT adversary behaviors	AI/ML-specific adversary behaviors
Tactic count	14 tactics (Enterprise)	                16 tactics (14 inherited + 2 AI-specific)
Technique count	196+ techniques	                        84 techniques
Unique tactics	None AI-specific	                    ML Model Access, ML Attack Staging
Target systems	Endpoints, networks, cloud	ML models, training pipelines, LLMs
Case studies	Groups and software profiles	        42 AI-specific incident analyses
Best for	    Endpoint/network threat modeling	        AI system threat modeling
ATLAS inherits 13 tactics from ATT&CK — including Reconnaissance, Initial Access, Execution, and Exfiltration — but applies them specifically to AI contexts. The two AI-specific tactics unique to ATLAS are:

ML Model Access (AML.TA0004): Describes how adversaries gain access to target ML models through inference APIs or direct artifact access
ML Attack Staging (AML.TA0012): Covers how adversaries prepare attacks targeting ML models, including training data poisoning and backdoor insertion

Related Terms

Examples & Use Cases

Reconnaissance through Initial Access (AML.TA0001-AML.TA0003)
The attack lifecycle begins with reconnaissance, where adversaries gather information about target ML systems. Key techniques include:

Discover ML Artifacts: Adversaries search public repositories, documentation, and APIs to understand model architectures and training data
ML Supply Chain Compromise: Attackers target supply chain attacks by inserting malicious code or data into ML pipelines
Prompt Injection (AML.T0051): Adversaries craft malicious inputs to manipulate LLM behavior — this maps to OWASP LLM01
ML Model Access and Execution (AML.TA0004-AML.TA0005)
These AI-specific tactics describe how adversaries interact with and exploit ML models:

Inference API Access: Gaining access to model prediction interfaces enables reconnaissance and attack staging
LLM Plugin Compromise: Exploiting vulnerable plugins extends attacker capabilities within AI systems
Persistence through Defense Evasion (AML.TA0006-AML.TA0008)
Threat actors maintain access and avoid detection through:

Modify AI Agent Configuration (October 2025 addition): Attackers alter agent settings to maintain persistence
Adversarial Perturbation: Crafting inputs that cause models to misclassify while appearing normal to humans
Collection through Impact (AML.TA0009-AML.TA0014)
Later-stage tactics focus on achieving adversary objectives:

RAG Database Retrieval: Extracting sensitive information from retrieval-augmented generation systems
Poison Training Data (AML.T0020): Data poisoning corrupts training data to manipulate model behavior — a critical data exfiltration vector
Exfiltration via AI Agent Tool Invocation (October 2025 addition): Leveraging agent tool access to extract data
Understanding lateral movement patterns helps security teams track how attackers progress through these tactics.