Skill Guide

AI/ML threat modeling using frameworks like MITRE ATLAS and OWASP Top 10 for LLMs

AI/ML threat modeling is the systematic process of identifying, evaluating, and mitigating security vulnerabilities in artificial intelligence and machine learning systems by applying structured frameworks like MITRE ATLAS (Adversarial Threat Landscape for AI Systems) and the OWASP Top 10 for Large Language Model Applications.

This skill is critical for preventing costly data breaches, model poisoning, and adversarial attacks that can lead to financial loss, reputational damage, and regulatory non-compliance. It directly impacts business outcomes by safeguarding intellectual property, ensuring operational resilience, and maintaining customer trust in AI-driven products.

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9.2 Avg Demand

15% Avg AI Risk

How to Learn AI/ML threat modeling using frameworks like MITRE ATLAS and OWASP Top 10 for LLMs

Begin by mastering foundational cybersecurity concepts (e.g., CIA triad) and core machine learning principles. Study the MITRE ATLAS matrix to understand adversarial tactics and techniques against AI. Familiarize yourself with the OWASP Top 10 for LLMs list, focusing on vulnerabilities like prompt injection and insecure output handling.

Apply theoretical knowledge by conducting threat models on simple ML pipelines (e.g., a sentiment analysis model). Use frameworks to map specific attack vectors (e.g., data poisoning via MITRE ATLAS T1595) to defensive controls. Avoid the common mistake of treating AI systems like traditional software; always account for the unique attack surface of training data, model parameters, and inference APIs.

Master the integration of threat modeling into the full MLOps and DevSecOps lifecycle. Develop strategic alignment by translating technical risks into business impact metrics (e.g., risk of model theft vs. revenue loss). Mentor teams on proactive threat hunting and lead cross-functional workshops to embed security culture within AI development teams.

Practice Projects

Beginner

Project

Threat Model for a Pre-trained Text Classifier

Scenario

You are given an open-source text classification model (e.g., for spam detection) and its associated API endpoint. Conduct an initial threat assessment before deployment.

How to Execute

1. Inventory all assets: model file, training data schema, inference API. 2. Use the MITRE ATLAS 'Reconnaissance' (TA0043) and 'ML Model Access' (TA0000) tactics to brainstorm how an attacker could gather information or gain initial access. 3. Cross-reference with OWASP Top 10 for LLMs: specifically assess for 'LLM01: Prompt Injection' if the model takes user input. 4. Document the top 3 identified threats with a simple severity matrix.

Intermediate

Case Study/Exercise

Defending a RAG-Powered Customer Service Bot

Scenario

A retail company deploys a Retrieval-Augmented Generation (RAG) chatbot that answers product questions by searching a private knowledge base. The security team suspects it may be vulnerable to data exfiltration.

How to Execute

1. Map the system architecture: user input -> LLM -> vector database retrieval -> response generation. 2. Apply the STRIDE threat model (adapted for AI) focusing on 'Information Disclosure' and 'Tampering'. 3. Use OWASP LLM06: 'Sensitive Information Disclosure' to evaluate if the model could leak knowledge base contents. 4. Propose specific mitigations: input sanitization, output filtering, and access logs on the retrieval component.

Advanced

Project

Enterprise AI Security Program Design

Scenario

As the new AI Security Lead for a fintech firm, you must design a repeatable threat modeling process for all internal ML projects, from fraud detection to customer-facing chatbots.

How to Execute

1. Develop a tiered risk classification framework based on data sensitivity and model criticality. 2. Integrate MITRE ATLAS and OWASP checklists into CI/CD pipelines using automated scanning tools where possible. 3. Create a cross-functional review board (Security, ML Engineering, Legal) to assess high-risk models. 4. Establish metrics and KPIs for AI security posture, such as mean time to model threat identification (MTTI) and percentage of models with documented threat models.

Tools & Frameworks

Threat Modeling Frameworks

MITRE ATLASOWASP Top 10 for LLM ApplicationsSTRIDE (Adapted for AI)PASTA (Process for Attack Simulation and Threat Analysis)

Use MITRE ATLAS for a comprehensive, adversarial tactic-based taxonomy of AI-specific attacks. Use the OWASP Top 10 for LLMs as a focused checklist for vulnerabilities unique to large language models. STRIDE and PASTA provide structured methodologies for systematic threat enumeration and risk prioritization.

Software & Platforms

Microsoft Threat Modeling ToolOWASP Threat DragonJupyter Notebooks (for model introspection)MLOps Platforms (e.g., MLflow, Kubeflow) with security plugins

Leverage dedicated threat modeling tools for visual diagramming and asset tracking. Use Jupyter for hands-on analysis of model behavior and attack simulation. Integrate security gates into MLOps platforms to automate checks during training and deployment.

Interview Questions

Answer Strategy

Structure the answer using a framework: 1) Asset Identification (the LLM, database, API), 2) Threat Enumeration (use OWASP LLM01 Prompt Injection to extract DB data, MITRE ATLAS T1565 Data Manipulation), 3) Risk Assessment (likelihood/impact), 4) Mitigation Plan (input validation, output sandboxing, least-privilege DB access). Sample: 'I'd start by mapping the data flow from user prompt to database query. Key threats are prompt injection leading to SQL injection (OWASP LLM01, ATLAS T1190 Exploit Public-Facing Application) and unauthorized data access. Mitigations include parameterized queries, role-based access control on the retrieval layer, and rigorous output filtering.'

Answer Strategy

This tests proactive threat hunting and communication skills. Use the STAR method (Situation, Task, Action, Result). Focus on the analytical process: how you used a framework to find the gap, how you quantified the risk, and how you collaborated to implement a fix. Sample: 'In a previous project, I identified that a sentiment analysis model trained on public social media data was vulnerable to training data poisoning (ATLAS T1486). By modeling this threat, I demonstrated how a coordinated attack could bias the model. I presented this to stakeholders with a cost-of-breach analysis, leading to the implementation of data provenance checks and outlier detection in our data pipeline.'