Skill Guide

Threat modeling for AI systems using frameworks like ATLAS (MITRE) and OWASP Top 10 for LLMs

The systematic process of identifying, analyzing, and prioritizing security threats to AI systems throughout their lifecycle by applying adversarial attack taxonomies (ATLAS) and LLM-specific vulnerability frameworks (OWASP Top 10 for LLMs).

This skill is critical for proactively mitigating adversarial risks, ensuring model integrity, and maintaining customer trust. It directly reduces financial and reputational damage from AI system breaches, which can cost organizations millions and erode competitive advantage.

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How to Learn Threat modeling for AI systems using frameworks like ATLAS (MITRE) and OWASP Top 10 for LLMs

Begin by studying the MITRE ATLAS knowledge base, focusing on adversarial tactics (e.g., ML Model Evasion, Data Poisoning) and the OWASP Top 10 for LLMs (e.g., Prompt Injection, Insecure Output Handling). Build core threat modeling habits: asset inventory (e.g., training data, model weights, API endpoints) and attack surface mapping.

Move to practice by applying the frameworks to real-world case studies (e.g., an LLM-powered chatbot). Develop mitigation strategies for specific vulnerabilities like training data poisoning or model theft. Avoid common pitfalls such as focusing solely on perimeter security while ignoring insider threats or supply chain compromises (e.g., malicious packages in ML pipelines).

Master the skill by integrating threat modeling into the entire AI/ML development lifecycle (MLOps) and aligning it with business risk appetite. Architect comprehensive security controls that span from data ingestion to model deployment and monitoring. Mentor teams on threat modeling and contribute to organizational AI security policy and standards.

Practice Projects

Beginner

Project

Threat Model an LLM-Powered Search Engine

Scenario

You are given a documentation set for a hypothetical LLM search tool that uses RAG (Retrieval-Augmented Generation). Your task is to perform an initial threat model.

How to Execute

1. Asset Inventory: List all components (web UI, API, embedding model, vector database, source documents). 2. Threat Identification: Apply the OWASP LLM Top 10 checklist to identify vulnerabilities like Prompt Injection via user queries or Insecure Output Handling if the UI renders markdown/HTML. 3. Threat Analysis: Use STRIDE to categorize threats (e.g., Spoofing of API calls). 4. Document: Create a simple threat model diagram and report highlighting top risks and initial mitigations (e.g., input sanitization).

Intermediate

Case Study/Exercise

Analyze a Real-World AI Incident Using ATLAS

Scenario

A public incident report describes a malicious actor causing a recommendation system to promote extremist content via data poisoning. Your team must investigate the attack chain and recommend controls.

How to Execute

1. Deconstruct the Incident: Map each stage of the attack to the MITRE ATLAS tactics (Initial Access: ML Supply Chain Compromise, Execution: ML Model Inference API Access, Persistence: Backdoor in Model). 2. Identify Gaps: Analyze which controls (e.g., data validation, model monitoring, access logs) failed or were absent. 3. Propose Mitigations: Recommend specific, layered defenses (e.g., adversarial training, data provenance tracking, runtime anomaly detection on model outputs). 4. Present Findings: Brief the team on the attack lifecycle and a prioritized list of security improvements.

Advanced

Case Study/Exercise

Design an AI Security Review for a New AI Product

Scenario

Your company is launching a new generative AI feature integrated with a high-value customer database. As the security architect, you must design the security review process.

How to Execute

1. Define Scope & Objectives: Align with stakeholders on protecting data confidentiality, model availability, and output integrity. 2. Establish the Threat Modeling Cadence: Integrate threat modeling sessions at key MLOps gates (data collection, model training, deployment). 3. Create a Risk Assessment Matrix: Combine ATLAS attack likelihood with business impact for each identified threat (e.g., Training Data Poisoning -> High Impact if data from untrusted sources). 4. Develop a Control Framework: Map each high-risk threat to specific, testable security requirements (e.g., for 'Exfiltration via ML Inference API,' implement rate limiting, strict authentication, and query logging). 5. Review & Iterate: Establish a feedback loop from red team exercises and monitoring to refine the threat model.

Tools & Frameworks

Threat Modeling Frameworks

MITRE ATLASOWASP Top 10 for LLM ApplicationsSTRIDEPASTA (Process for Attack Simulation and Threat Analysis)

ATLAS provides the adversarial tactics, techniques, and procedures (TTPs) specific to ML. The OWASP LLM Top 10 is a focused checklist for generative AI risks. STRIDE and PASTA are general threat modeling methodologies useful for initial brainstorming and risk-centric analysis, respectively.

Model Security & Testing Tools

Microsoft CounterfitAdversarial Robustness Toolbox (ART)GarakPromptfoo

Counterfit and ART are command-line tools and libraries for running adversarial attacks against ML models to test robustness. Garak and Promptfoo are specialized tools for probing LLMs for vulnerabilities like prompt injection and data leakage, used during red teaming.

Security Monitoring & MLOps Integration

Seldon CoreMLflowGreat ExpectationsGuardrails AI

Seldon and MLflow provide model monitoring capabilities to detect drift or anomalous inference patterns post-deployment. Great Expectations is for data validation. Guardrails AI offers tools to enforce constraints on LLM inputs/outputs, implementing runtime mitigations identified in threat models.

Interview Questions

Answer Strategy

The candidate should demonstrate a structured methodology. The answer should start by scoping assets and trust boundaries, then directly apply ATLAS and the OWASP LLM Top 10 to the agent's capabilities. Priority would be given to threats like Indirect Prompt Injection (OWASP LLM01), Excessive Agency (OWASP LLM07), and ML Supply Chain Compromise (ATLAS TA0043). A strong answer includes specific mitigations like strict sandboxing for code execution and robust output parsing.

Answer Strategy

This tests the candidate's ability to think beyond common software bugs and consider adversarial ML threats. The core competency is applying the ATLAS framework for covert attack identification. The response should move methodically from benign causes (data drift) to adversarial ones (data poisoning, model evasion).