Skill Guide

Threat modeling for AI systems using frameworks like STRIDE-LLM or MITRE ATLAS

The systematic process of identifying, analyzing, and mitigating security vulnerabilities specific to machine learning models, their data pipelines, and integrated systems, using specialized threat modeling frameworks like STRIDE-LLM and MITRE ATLAS.

This skill is critical for proactively defending the significant investment in AI/ML assets against novel attack vectors like data poisoning and model inversion, directly preventing financial loss, reputational damage, and regulatory non-compliance. It enables organizations to build AI systems that are secure by design, accelerating deployment and maintaining stakeholder trust.

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9.1 Avg Demand

18% Avg AI Risk

How to Learn Threat modeling for AI systems using frameworks like STRIDE-LLM or MITRE ATLAS

Focus on understanding the fundamental differences between traditional software and AI system threat surfaces: data integrity (training data poisoning), model confidentiality (model theft/extraction), and output integrity (evasion attacks). Grasp core STRIDE categories (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and learn their specific manifestations within ML pipelines.

Transition from theory to hands-on application by mapping specific attack techniques from the MITRE ATLAS matrix to your own model's architecture. Practice conducting threat modeling workshops for a concrete AI use case (e.g., a recommendation system), moving beyond generic threats to address unique risks like supply chain vulnerabilities in pre-trained models or fairness/bias attacks. A common mistake is focusing solely on the model and neglecting data labeling interfaces or feature stores.

Master the integration of AI threat modeling into the enterprise risk management framework, quantifying risks in business terms (e.g., potential revenue impact from a model integrity failure). Architect security controls across the entire ML lifecycle, from secure data pipelines to adversarial robustness testing in CI/CD. Develop strategies to mentor cross-functional teams (Data Scientists, DevOps, Security) on their specific roles in AI security and establish continuous threat intelligence feeds relevant to the organization's AI assets.

Practice Projects

Beginner

Project

STRIDE-LLM Threat Model for a Text Classifier

Scenario

You are given a simple sentiment analysis model integrated into a customer feedback portal. You must identify its potential threats.

How to Execute

1. Diagram the system: Data Ingress -> Preprocessing -> Model -> Post-processing -> Output. 2. For each component, apply STRIDE-LLM: e.g., 'Tampering' at data ingress (adversarial text inputs), 'Information Disclosure' at the model (extraction via API queries). 3. Document threats in a structured template. 4. Propose one basic mitigation per threat (e.g., input validation, rate limiting).

Intermediate

Project

MITRE ATLAS Attack Emulation for a Model API

Scenario

A deployed computer vision model API is suspected to be vulnerable. You need to empirically assess its risk to specific attack techniques.

How to Execute

1. Select 2-3 relevant techniques from the ATLAS matrix (e.g., AML.Evasion - Adversarial Example, AML.Exfiltration - ML Model Inference API). 2. Use open-source tools to simulate the attacks against a test instance of the model. 3. Analyze the results: Did the evasion succeed? What was the cost of exfiltration? 4. Write a technical report linking findings back to business risk and recommending specific defensive controls.

Advanced

Project

Designing a Secure ML Pipeline Architecture

Scenario

Lead the security architecture design for a new, high-value ML platform that will host multiple models, including handling sensitive data.

How to Execute

1. Conduct a threat modeling workshop using STRIDE-LLM across all pipeline stages: data collection, labeling, training, serving, monitoring. 2. Map controls to each threat: e.g., data lineage tracking for poisoning, model signing for tampering, confidential computing for model protection. 3. Integrate security gates into the MLOps workflow (e.g., adversarial robustness tests as a CI/CD gate). 4. Create a risk register and present the architecture with its inherent security trade-offs to engineering and business leadership.

Tools & Frameworks

Threat Modeling Frameworks

STRIDE-LLMMITRE ATLASOWASP ML Security Top 10

STRIDE-LLM provides a structured taxonomy for decomposing threats in LLM and generative AI systems. MITRE ATLAS is a knowledge base of adversary tactics and techniques for ML systems, essential for concrete attack emulation. The OWASP list offers a prioritized view of the most critical ML security risks.

Software & Platforms

Microsoft Threat Modeling Tool (with custom ML templates)Draw.io / Lucidchart (for system diagramming)ART (Adversarial Robustness Toolbox)CleverHans

Diagramming tools are foundational for visualizing data flows and trust boundaries. ART and CleverHans are specialized Python libraries for empirically testing model robustness against adversarial attacks, providing concrete evidence for threat models.

Standards & Guidelines

NIST AI Risk Management Framework (AI RMF)ISO/IEC 27001 with AI extensions

These provide the governance structure and control catalogs for managing AI risk at an organizational level, ensuring threat modeling outputs are aligned with broader security and compliance programs.

Interview Questions

Answer Strategy

Use the STRIDE-LLM framework to structure the answer. Start by scoping the system boundaries (user input, model API, data retrieval, response generation). Then, methodically apply STRIDE-LLM: 1) Focus on 'Information Disclosure' (model leaking sensitive training data or internal documents). 2) Address 'Tampering' (prompt injection causing model to generate malicious content). 3) Consider 'Denial of Service' (resource exhaustion via expensive prompts). Conclude by prioritizing mitigations like strict input/output filtering, model sandboxing, and query rate limiting.

Answer Strategy

The interviewer is testing for practical incident response experience and cross-functional communication. Use the STAR method (Situation, Task, Action, Result). In the 'Action' phase, clearly detail: 1) The technical threat (e.g., a model extraction attack). 2) The analytical steps taken (e.g., API log analysis showing systematic querying). 3) The remediation actions you led or contributed to (e.g., implementing differential privacy, adding a watermarking layer). Highlight collaboration with data scientists and infrastructure teams.