Experience with threat modeling for AI systems (OWASP ML Top 10, MITRE ATLAS)
The systematic practice of identifying, quantifying, and mitigating adversarial risks specific to machine learning models and their supporting pipelines, using structured frameworks like OWASP's Machine Learning Security Top 10 and MITRE's Adversarial Threat Landscape for AI Systems (ATLAS).
This skill is critical for protecting high-value intellectual property, ensuring regulatory compliance in AI deployments, and maintaining operational integrity by preventing costly model failures or data breaches. It directly impacts business outcomes by reducing risk exposure and enabling the secure scaling of AI initiatives.
1Careers
1Categories
9.1 Avg Demand
15%
Avg AI Risk
How to Learn Experience with threat modeling for AI systems (OWASP ML Top 10, MITRE ATLAS)
Master the core concepts: 1) Understand the ML lifecycle (data collection, training, deployment) and its unique attack surfaces. 2) Memorize the OWASP ML Top 10 threat categories (e.g., ML01: Input Manipulation Attack, ML02: Data Poisoning). 3) Learn the structure of MITRE ATLAS, focusing on its tactics (e.g., Initial Access, Execution) and techniques.
Transition from theory to practice by applying frameworks to real scenarios. Conduct a threat model on a public ML project (e.g., a sentiment analysis API) using STRIDE or LINDDUN as a starting template, then map findings to ML-specific threats. Avoid the common mistake of focusing only on the model and neglecting the data pipeline and MLOps infrastructure.
Operate at the architectural level by integrating threat modeling into the MLOps lifecycle (CI/CD for ML). Design security controls for complex systems like federated learning or generative AI. Master the art of communicating risk to executives by translating technical threats (e.g., adversarial examples) into business impacts (e.g., fraud, reputational damage). Mentor teams on building a security-first ML culture.
Practice Projects
Beginner
Project
Threat Model a Public Image Classification API
Scenario
You have a pre-trained model deployed via a REST API for identifying objects in images. Your task is to perform an initial threat assessment.
How to Execute
1. Diagram the system: Client -> API Gateway -> Model Server -> Training Data Store. 2. Apply STRIDE categories to each component and data flow. 3. Cross-reference each identified threat with the OWASP ML Top 10 (e.g., a spoofed input maps to ML01). 4. Document findings in a simple threat model report with proposed mitigations like input validation or rate limiting.
Intermediate
Project
Secure a Recommendation System Pipeline
Scenario
A collaborative filtering model for an e-commerce site is suspected of being vulnerable to profile poisoning attacks, where attackers create fake user accounts to manipulate recommendations.
How to Execute
1. Map the data pipeline from user interaction logs to model retraining. 2. Use MITRE ATLAS to map the attack: 'Initial Access' (fake accounts), 'ML Attack Staging' (crafted interactions). 3. Design layered defenses: implement anomaly detection on user behavior, add model input sanitization, and establish data provenance checks. 4. Write a security requirements document for the engineering team.
Advanced
Case Study/Exercise
Executive Risk Briefing for a Generative AI Deployment
Scenario
The company plans to deploy a large language model (LLM) for customer service. Leadership needs to understand the novel risks beyond traditional software.
How to Execute
1. Perform a comprehensive threat model focusing on LLM-specific risks: prompt injection, training data extraction, and harmful content generation. 2. Use MITRE ATLAS to create an attack narrative (e.g., data poisoning leading to biased outputs). 3. Quantify risks in business terms: estimated cost of a PR crisis from biased outputs, potential fines from leaked PII in training data. 4. Present a mitigation roadmap with phased investments in guardrails, monitoring, and red teaming.
Tools & Frameworks
Threat Modeling Frameworks
OWASP ML Top 10MITRE ATLASSTRIDELINDDUN
OWASP ML Top 10 is the definitive list of ML-specific threats. MITRE ATLAS provides a knowledge base of adversarial tactics and techniques. STRIDE (Spoofing, Tampering, etc.) and LINDDUN (for privacy) are general-purpose frameworks to structure the initial analysis of any system component.
Software & Platforms
Microsoft Threat Modeling ToolOWASP Threat DragonDraw.io for DiagrammingJupyter Notebook for Attack Simulation
Dedicated threat modeling tools help visualize data flows and generate reports. Diagramming software is essential for creating the system diagrams that are the foundation of any model. Jupyter notebooks are used to prototype and demonstrate specific ML attacks like adversarial example generation.
Adversarial ML Toolkits
IBM Adversarial Robustness Toolbox (ART)CleverHansFoolbox
These libraries are used for hands-on red teaming. They allow you to test model robustness by generating adversarial examples, poisoning data, and running privacy attacks, providing concrete evidence of vulnerabilities.
Interview Questions
Answer Strategy
The candidate should demonstrate a structured approach, moving from system decomposition to threat identification and mitigation. Use the following sample: 'First, I'd diagram the system: transaction ingestion, feature engineering (including graph construction), GNN model training, and real-time inference. I would then apply STRIDE to each component, but focus on ML-specific threats. For the graph data pipeline, I'd consider data poisoning attacks where an adversary creates synthetic transaction graphs to bias the model-a threat mapped to ATLAS Tactic: Initial Access via Poisoning. For the inference endpoint, I'd consider evasion attacks. Mitigations would include graph anomaly detection, model output monitoring for concept drift, and strict access controls on the feature store.'
Answer Strategy
This tests the ability to think beyond checklists and handle ambiguity. The core competency is proactive security reasoning. Sample response: 'While assessing a proprietary NLP model for contract analysis, I identified a risk where an attacker could craft seemingly benign clauses that, when combined with the model's latent space, could force it to misinterpret critical terms. This wasn't a classic adversarial example; it was a semantic backdoor. I handled it by first creating a proof-of-concept with red-team linguists, then documenting the attack pattern and proposing a mitigation involving multi-model consensus checks and explainability audits on high-stakes outputs.'
Careers That Require Experience with threat modeling for AI systems (OWASP ML Top 10, MITRE ATLAS)