Understanding NIST’s New Adversarial Machine Learning Framework

The National Institute of Standards and Technology (NIST) recently released a significant publication titled “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations” (NIST AI 100-2e2025). This comprehensive report provides a structured framework for understanding security vulnerabilities in AI systems, with a particular focus on adversarial machine learning (AML) – attacks that exploit the statistical, data-based nature of machine learning systems.

  What is Adversarial Machine Learning?

Adversarial machine learning refers to techniques that attempt to fool models by supplying deceptive input. As AI systems become increasingly embedded in critical infrastructure, these attack vectors present growing concerns for organizations deploying AI technologies.

The NIST report distinguishes between two broad classes of AI systems:

• Predictive AI (PredAI): Traditional systems that make predictions based on provided data
• Generative AI (GenAI): Systems that can generate new content with similar properties to their training data

  Key Attack Categories

The report taxonomizes attacks based on:

1. Learning Stage: Whether attacks happen during the training stage or deployment stage
2. Attacker’s Objectives:
   • Availability Breakdown: Disrupting access to the AI system
   • Integrity Violation: Forcing the AI system to produce incorrect outputs
   • Privacy Compromise: Extracting sensitive information
   • Misuse Enablement: Specifically for GenAI, circumventing technical restrictions
3. Attacker Capabilities:
   • Training data control
   • Model control
   • Query access
   • Resource control
   • Testing data control
   • Label limit
   • Source code control

  Notable Attack Types

For Predictive AI:

• Evasion Attacks: Modifying inputs to cause misclassification
• Poisoning Attacks: Corrupting training data to induce availability or integrity violations
• Privacy Attacks: Extracting information about training data or model parameters

For Generative AI:

• Supply Chain Attacks: Poisoning data or models
• Direct Prompting Attacks: Using adversarial inputs to manipulate model outputs
• Indirect Prompt Injection: Manipulating resources the model accesses at runtime

  Mitigation Strategies

The report outlines several mitigation approaches:

• Adversarial Training: Including adversarial examples during training
• Randomized Smoothing: Creating more robust models through probabilistic methods
• Formal Verification: Mathematically proving model properties
• Model Inspection and Sanitization: Detecting and removing vulnerabilities
• Training Data Validation: Validating data sources and integrity

  Implications for Businesses

The implications of this report for businesses are significant:

1. Risk Assessment Framework: The taxonomy provides a structured way to assess AI system vulnerabilities
2. Security Integration: Organizations must integrate AI security into their existing cybersecurity frameworks
3. Supply Chain Considerations: Businesses must evaluate risks from third-party AI components and data sources
4. Tradeoff Awareness: Security improvements often come with tradeoffs in performance, accuracy, or fairness
5. Emerging Threats: Organizations must stay vigilant as new attack vectors emerge, particularly for multimodal and generative AI systems

 How CinchOps Can Help Secure Your Business

In this evolving threat environment, CinchOps offers targeted AI security solutions that align with NIST’s framework:

Supply Chain Security: We help secure your AI development pipeline from third-party vulnerabilities

Compliance Alignment: We ensure your AI security practices align with emerging standards and regulations

Security Training: We provide specialized training for your technical teams on identifying and mitigating adversarial machine learning threats

Vulnerability Management: We implement systematic processes to identify and remediate vulnerabilities in your AI systems before they can be exploited

Incident Response Planning: We develop customized response plans specifically for AI security incidents to minimize impact and recovery time

Technical Due Diligence: We assess third-party AI systems and components before integration into your environment

Discover more about our enterprise-grade and business protecting cybersecurity services on our Cybersecurity page.

As AI becomes increasingly central to business operations, securing these systems against adversarial attacks is no longer optional. With CinchOps, you can implement a robust AI security strategy that protects your organization while maintaining the benefits of AI innovation.

Contact CinchOps today to learn how we can help safeguard your AI investments against this evolving threat environment.

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