AI threat modeling using frameworks like MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF
AI threat modeling is the systematic process of identifying, assessing, and mitigating security vulnerabilities and adversarial risks specific to AI/ML systems by leveraging structured threat frameworks like MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF.
This skill is critical for securing AI investments and maintaining regulatory compliance, as it proactively addresses unique AI failure modes like data poisoning and model evasion. It directly reduces financial loss, reputational damage, and operational downtime from AI-specific breaches.
1Careers
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9.2 Avg Demand
15%
Avg AI Risk
How to Learn AI threat modeling using frameworks like MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF
Master the core taxonomies of each framework: MITRE ATLAS's adversarial tactics/procedures, OWASP's LLM vulnerability categories, and NIST AI RMF's risk management governance structure. Learn basic ML concepts (model training, inference, data pipelines) to contextualize threats. Start by mapping a simple, public AI application (e.g., a sentiment analysis model) against one framework.
Conduct threat modeling sessions for internal AI projects by mapping components (data sources, model endpoints, APIs) and applying cross-framework analysis. Common mistake: Treating AI threats as traditional software vulnerabilities; focus instead on unique attack surfaces like training data integrity and prompt injection. Practice creating a unified risk register from findings across ATLAS, OWASP, and NIST.
Architect organization-wide AI security programs by integrating threat modeling into the MLOps lifecycle (design, deployment, monitoring). Lead tabletop exercises simulating multi-stage AI attacks (e.g., data poisoning leading to model failure). Mentor teams on aligning technical mitigations with business risk tolerance defined in the NIST RMF. Develop custom threat libraries for industry-specific AI systems.
Practice Projects
Beginner
Project
Threat Model a Public LLM-Powered Chatbot
Scenario
You are given access to a sample API for a customer service chatbot powered by a large language model. Your task is to perform an initial threat assessment.
How to Execute
1. Inventory assets: Identify the model, API endpoint, user input field, and conversation logs. 2. Apply the OWASP LLM Top 10 to list potential vulnerabilities (e.g., LLM01: Prompt Injection, LLM06: Sensitive Information Disclosure). 3. Use MITRE ATLAS to brainstorm adversary tactics (e.g., TA0005: LLM Prompt Injection, TA0001: ML Model Evasion). 4. Compile a one-page report with findings and one recommended mitigation (e.g., input validation).
Intermediate
Project
Cross-Framework Risk Assessment for an Internal ML Model
Scenario
Your team has deployed a fraud detection model using transactional data. You must conduct a formal threat model before it handles production traffic.
How to Execute
1. Diagram the system: data pipeline, training environment, model serving endpoint, monitoring logs. 2. Conduct a MITRE ATLAS walkthrough for each component (e.g., TA0043: Gather Victim ML Artifacts for the training data). 3. Evaluate OWASP LLM Top 10 risks if the model interacts with user-generated text. 4. Map controls to the NIST AI RMF 'Govern' and 'Map' functions. 5. Deliver a risk register with prioritized mitigations (e.g., data provenance checks, model robustness testing).
Advanced
Project
Enterprise AI Threat Modeling Program Design
Scenario
As a lead security architect, you are tasked with creating a repeatable threat modeling process for all AI projects in a financial institution.
How to Execute
1. Define a phased process integrated into the SDLC: Design (threat identification using ATLAS/OWASP), Development (secure training practices), Deployment (hardened serving), Monitoring (detection of ATLAS tactics). 2. Create decision trees for risk classification based on NIST AI RMF risk tiers. 3. Develop a toolkit with custom questionnaires and attack libraries tailored to finance (e.g., model theft for trading algorithms). 4. Establish a review board and metrics (e.g., % of AI projects threat-modeled pre-deployment).
Tools & Frameworks
Threat Frameworks & Standards
MITRE ATLASOWASP LLM Top 10NIST AI Risk Management Framework (AI RMF)ISO/IEC 23894
MITRE ATLAS provides a knowledge base of adversary tactics and techniques against AI. OWASP LLM Top 10 lists the most critical web application security risks for LLMs. NIST AI RMF offers a governance-focused risk management structure. Use these in tandem: ATLAS/OWASP for tactical threat identification, NIST for strategic risk governance.
Software & Platforms
MLflow (for model registry/lineage)TensorFlow Privacy / PySyft (for privacy-preserving ML)Robust Intelligence AI FirewallMicrosoft Counterfit
MLflow helps trace model/data lineage for provenance checks. Robust Intelligence and Counterfit are tools for automated adversarial testing and runtime protection. Use these to implement technical controls derived from your threat model (e.g., testing for evasion with Counterfit).
Mental Models & Methodologies
STRIDE (adapted for ML)Attack TreesMITRE ATT&CK for Enterprise (to correlate with IT threats)
STRIDE can be adapted to categorize AI-specific threats (e.g., 'Spoofing' a model's prediction). Attack Trees help visualize complex multi-step AI attacks. Correlate AI threat models with IT security frameworks to ensure holistic defense.
Interview Questions
Answer Strategy
Structure the answer using a phased approach: 1) Scope & Inventory (data sources, model architecture, endpoints), 2) Threat Identification (systematically apply OWASP LLM Top 10 for app-layer risks and MITRE ATLAS for adversarial ML risks), 3) Risk Assessment (prioritize using business impact, referencing NIST AI RMF for governance context), 4) Mitigation Planning (propose technical, process, and monitoring controls). Sample: 'I would begin by mapping the data flow from user input through the LLM to output, identifying all third-party components. I'd then apply the OWASP LLM Top 10 to catalog vulnerabilities like prompt injection and insecure output handling, cross-referencing MITRE ATLAS tactics like LLM Prompt Injection. Risks would be prioritized based on likelihood and impact to business operations, with mitigations such as input validation, output sandboxing, and continuous monitoring for anomalous query patterns.'
Answer Strategy
This tests communication and strategic alignment. Use the STAR method, but focus on how you translated technical AI threats into business terms (financial loss, compliance, reputational damage). Reference a framework like NIST AI RMF to structure the risk argument. Sample: 'In a previous role, our data science team wanted to deploy a predictive model with minimal input validation. I framed the threat of data poisoning not as a technical bug, but as a business continuity risk: a poisoned model could lead to catastrophic financial decisions. I presented a brief using the NIST AI RMF 'Govern' function to highlight our accountability for the model's outcomes. This led to the approval of a data provenance check and a model robustness testing phase in our MLOps pipeline.'
Careers That Require AI threat modeling using frameworks like MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF