Threat modeling for AI systems (STRIDE-AI, OWASP LLM Top 10, MITRE ATLAS framework)
Threat modeling for AI systems is the systematic process of identifying, assessing, and mitigating security risks specific to machine learning models and their data pipelines using structured frameworks like STRIDE-AI, OWASP LLM Top 10, and MITRE ATLAS.
This skill is critical for organizations deploying AI at scale to proactively prevent adversarial attacks, data poisoning, and model theft that can cause catastrophic financial and reputational damage. It directly impacts business outcomes by securing competitive AI assets, ensuring regulatory compliance, and maintaining customer trust in AI-powered products.
1Careers
1Categories
9.2 Avg Demand
15%
Avg AI Risk
How to Learn Threat modeling for AI systems (STRIDE-AI, OWASP LLM Top 10, MITRE ATLAS framework)
Focus on understanding the core components of an AI system (model, data pipeline, serving infrastructure) and learning the taxonomy of one foundational framework (e.g., STRIDE-AI's spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege mapped to AI contexts). Practice identifying the most obvious attack vectors like training data poisoning and model inversion.
Move from theory to practice by conducting threat modeling sessions on your organization's specific AI projects. Use the OWASP LLM Top 10 to systematically assess LLM-integrated applications, focusing on prompt injection, insecure output handling, and overreliance. Avoid the common mistake of treating AI systems like traditional software; focus on the unique attack surface around the model itself and its data.
Master the skill by integrating threat modeling into the full AI/ML DevOps (MLOps) lifecycle, from data collection to model retirement. Use the MITRE ATLAS framework to map advanced, coordinated adversary tactics and build defense-in-depth strategies. At this level, you mentor engineering teams, define organizational AI security policies, and align threat intelligence with business risk tolerance.
Practice Projects
Beginner
Project
STRIDE-AI Analysis on a Simple Image Classifier
Scenario
You have a publicly available pre-trained image classification model (e.g., from TensorFlow Hub) that you want to deploy as a microservice in a web application.
How to Execute
1. Document the data flow: user uploads image -> preprocessing -> model inference -> class label output. 2. For each STRIDE-AI category, brainstorm at least one potential threat (e.g., Spoofing: user uploads adversarial image to fool model; Tampering: manipulating the model file on the server). 3. Create a simple threat report with the threat, potential impact, and a basic mitigation idea for each. 4. Present this report to a peer for review.
Intermediate
Case Study/Exercise
OWASP LLM Top 10 Assessment of a Chatbot
Scenario
Your company is integrating a large language model (LLM) via an API to create an internal customer support chatbot that has access to a knowledge base.
How to Execute
1. Diagram the full system architecture, including the API gateway, LLM provider, and any plugins. 2. Systematically evaluate each item from the OWASP LLM Top 10 against this architecture. For LLM01 (Prompt Injection), design test cases where a user might try to extract system prompts or bypass safety filters. 3. Document your findings, risk ratings (High/Medium/Low), and specific, actionable mitigation controls (e.g., input validation, output encoding, strict prompt engineering).
Advanced
Case Study/Exercise
MITRE ATLAS Threat Simulation for an AI-Powered Financial Fraud Detection System
Scenario
You are the security architect for a major financial institution. The fraud detection system uses a proprietary model trained on sensitive transaction data. A sophisticated threat actor group is known to target financial ML models.
How to Execute
1. Map the system's full lifecycle to the MITRE ATLAS framework, identifying key assets (training data, model weights, inference API). 2. For critical assets, detail likely adversary Tactics, Techniques, and Procedures (TTPs) from ATLAS (e.g., TA0000 - Initial Access via phishing ML developers, TA0004 - ML Model Access via supply chain compromise of a ML library). 3. Develop a red team exercise plan to simulate two high-impact TTPs. 4. Design a defense-in-depth response plan that includes detection, response, and recovery playbooks for each simulated attack, and present this to executive leadership to secure budget for enhanced AI security controls.
Tools & Frameworks
Core Threat Modeling Frameworks
STRIDE-AIOWASP LLM Top 10MITRE ATLAS
STRIDE-AI provides a structured checklist for brainstorming threats to traditional AI/ML components. OWASP LLM Top 10 is the essential checklist for assessing risks in applications built on Large Language Models. MITRE ATLAS is the knowledge base for understanding real-world adversary campaigns against AI systems, used for advanced threat intelligence and red teaming.
Technical Execution & Visualization Tools
Microsoft Threat Modeling ToolOWASP Threat DragonDraw.io / Lucidchart
These tools are used to create Data Flow Diagrams (DFDs), which are the foundational artifact for any threat modeling session. They help visualize the system under analysis, identify trust boundaries, and systematically apply the frameworks to components.
Validation & Testing Tools
ART (Adversarial Robustness Toolbox)CleverHansTextAttackBurp Suite (with AI/ML extensions)
Used to empirically validate identified threats. ART and CleverHans test model robustness against adversarial examples. TextAttack is for NLP model testing. Burp Suite can be extended to probe APIs serving models for vulnerabilities identified during threat modeling.
Interview Questions
Answer Strategy
The interviewer is testing your ability to apply a structured methodology (like OWASP LLM Top 10) to a novel business scenario. Use a clear framework: 1) Define scope and diagram the system (user input -> LLM -> output). 2) Systematically apply the OWASP LLM Top 10, highlighting critical risks like Prompt Injection (LLM01) where users could manipulate the model to ignore the style guide or extract internal prompts, and Insecure Output Handling (LLM02) where generated copy could contain malicious scripts or biased language. 3) Propose mitigations (e.g., strict input sanitization, output filtering, human-in-the-loop review). Emphasize the balance between innovation and control.
Answer Strategy
This behavioral question tests for deep, practical experience beyond rote framework application. The core competency is analytical depth and proactive security thinking. A strong answer follows the STAR method: Describe the situation (e.g., a recommendation system). Explain the task (securing the model). Detail the action: You identified that the model's utility for the business could be degraded by a 'model distillation attack' (where a competitor scrapes outputs to train a cheap copy), a threat not in basic STRIDE. This required analyzing the business model itself. State the result: You implemented an API usage policy with anomaly detection and rate limiting to mitigate the risk, protecting the intellectual property.
Careers That Require Threat modeling for AI systems (STRIDE-AI, OWASP LLM Top 10, MITRE ATLAS framework)