MITRE ATLAS is a globally accessible knowledge base of adversary tactics and techniques based on real-world observations of attacks against machine learning systems. Modeled after the widely-adopted MITRE ATT&CK framework, ATLAS extends security thinking into the unique threat landscape of AI systems. The framework addresses,
Adversarial Machine Learning: Attacks that manipulate AI model behavior
Data Poisoning: Corruption of training data to compromise model integrity
Model Theft: Extraction of proprietary AI models through queries
Evasion Attacks: Crafted inputs designed to fool AI systems
Privacy Violations: Extraction of sensitive training data from model
Gathering information about target AI systems and their weaknesses.
✦ Resource Development
Establishing resources to support AI attacks, including adversarial tools and datasets.
✦ Initial Access
Gaining entry to AI systems through APIs, model access, or data pipelines.
✦ ML Model Access
Obtaining access to machine learning models for analysis or exploitation.
✦ Execution
Running malicious code or queries against AI systems.
✦ Persistence
Maintaining access to AI systems over time
✦ Defence Evasion
Avoiding detection while attacking AI systems
✦ Discovery
Understanding the AI system's architecture, training data, and behavior
✦ Collection
Gathering data from AI systems, including model parameters or training data
✦ ML Attack Staging
Preparing adversarial attacks against machine learning models
✦ Exfiltration
Stealing AI models, training data, or sensitive outputs
✦ Impact
Manipulating, interrupting, or destroying AI system functionality
Key Attack Techniques in MITRE ATLAS
1. Data Poisoning
Attackers inject malicious data into training datasets to corrupt model behavior. This can cause models to misclassify specific inputs or exhibit biased behavior that benefits the attacker.
2. Model Evasion
Crafting inputs specifically designed to fool AI systems while appearing normal to humans. These adversarial examples exploit model weaknesses to cause misclassification.
3. Model Inversion
Extracting sensitive information about training data by analyzing model outputs. Attackers can reconstruct private data the model was trained on.
4. Model Extraction
Stealing machine learning models by querying them repeatedly and using the outputs to train a surrogate model with similar behavior.
5. Backdoor Attacks
Embedding hidden triggers in AI models that cause specific malicious behavior when activated, while performing normally otherwise.
6. Membership Inference
Determining whether specific data was used in training a model, potentially revealing confidential information about individuals or organizations.
AI System Vulnerabilities
Training Phase
● Poisoned training data from untrusted sources ● Inadequate data validation and sanitization ● Insufficient monitoring of training processes ● Lack of provenance tracking for datasets
Deployment Phase
● Exposed model APIs without proper access controls ● Insufficient input validation and sanitization ● Lack of adversarial robustness testing ● Inadequate monitoring of model predictions
Operational Phase
● Drift in model performance without detection ● Insufficient logging of model interactions ● Lack of incident response procedures for AI systems ● Poor model versioning and rollback capabilities
Why This Framework is Critical
As organizations deploy AI and machine learning, they face entirely new attack vectors that traditional security frameworks don't address. MITRE ATLAS provides the first comprehensive knowledge base of adversarial AI tactics, helping organizations protect AI investments from data poisoning, model theft, evasion attacks, and other AI-specific threats.
Financial Services
Fraud Detection Protection: Data poisoning attacks manipulate fraud detection models to approve fraudulent transactions, costing millions before detection and remediation
Trading Algorithm Security: Model extraction threatens proprietary algorithmic trading systems representing competitive advantages worth billions in market value
AML Evasion: Adversarial examples enable criminals to bypass anti-money laundering AI systems, creating regulatory compliance failures and legal liability
Health
Diagnostic Safety: Adversarial attacks on radiology and pathology AI systems cause misdiagnosis with potentially fatal consequences for patients
Medical Device Security: Backdoor attacks in medical AI could be weaponized to target specific patient populations, creating safety and liability issues
HIPAA Violations: Model inversion attacks extract sensitive patient data from diagnostic models, violating HIPAA and triggering breach notifications
Security & Defense
Threat Detection: Poisoned training data teaches malware detection systems to ignore specific threats, creating blind spots exploited by adversaries
Capability Exposure: Model extraction reveals defensive capabilities and detection thresholds to attackers, enabling them to evade security controls
Surveillance Compromise: Evasion attacks allow adversaries to bypass AI-powered surveillance and monitoring systems protecting critical assets
E-commerce & Advertising
Recommendation Manipulation: Adversaries poison recommendation systems to promote fraudulent products, inappropriate content, or manipulate user behavior
Pricing Attacks: Adversarial manipulation of dynamic pricing algorithms causes revenue loss or regulatory scrutiny for discriminatory pricing practices
Review System Evasion: Fake review detection AI gets fooled through evasion techniques, allowing manipulation of product ratings and customer decisions
Technology Platforms
Prompt Injection: Attackers manipulate language models and AI assistants to bypass safety controls, leak data, or perform unauthorized actions
Content Moderation Failure: Evasion attacks fool content moderation AI allowing harmful content proliferation, damaging platform reputation and user trust
Model IP Theft: Model extraction threatens intellectual property in AI services, foundation models, and proprietary algorithms worth millions in development
AI ASSURANCE PLATFORM
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