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ML Threat Model Creation: Securing the Machine Learning Attack Surface By Bivash Kumar Nayak — Founder, CyberDudeBivash

 


Executive Summary

Machine Learning (ML) systems are increasingly targeted by AI-specific cyberattacks that exploit vulnerabilities in data pipelines, algorithms, and model deployment environments. Traditional application threat models do not fully capture ML-specific attack vectors, making ML Threat Model Creation a crucial step in MLSecOps.

This article breaks down why ML threat modeling is necessary, the methodology, common attack scenarios, and security controls to embed into the ML lifecycle.

1. Why ML Threat Modeling is Essential

  • Unique attack surface — Unlike traditional applications, ML systems are vulnerable to data poisoning, adversarial examples, and model extraction.

  • Critical decision-making — Models often drive financial transactions, medical diagnoses, fraud detection, and autonomous systems.

  • Regulatory pressure — AI governance frameworks (EU AI Act, NIST AI RMF) require proactive risk assessments.

  • Supply chain complexity — ML stacks depend on multiple data sources, pretrained models, and open-source libraries.

2. ML Threat Model Creation: Step-by-Step Methodology

Step 1 — Define the ML System Scope

  • Identify model type (classification, regression, generative, reinforcement).

  • Document data flow: sources, preprocessing, training, validation, deployment, and inference.

  • Map external integrations (APIs, external datasets, user input endpoints).

Step 2 — Identify Assets

Assets that need protection:

  • Training data (raw, processed, and augmented).

  • ML model artifacts (weights, parameters, architecture).

  • Model outputs (predictions, decisions, generated content).

  • Feature engineering code and pipelines.

Step 3 — Identify Adversaries

Potential threat actors:

  • Cybercriminals — monetizing model theft or poisoning.

  • Competitors — reverse-engineering or stealing intellectual property.

  • Nation-state actors — manipulating AI for misinformation or disruption.

  • Insiders — privileged access abuse.

Step 4 — Map Threat Vectors

Using adapted STRIDE for ML:

  • Spoofing — API key theft for model API misuse.

  • Tampering — Data poisoning in the training pipeline.

  • Repudiation — Lack of audit logs in model inference requests.

  • Information Disclosure — Model inversion leaking sensitive training data.

  • Denial of Service — Adversarial examples triggering computational bottlenecks.

  • Elevation of Privilege — Prompt injection leading to tool or system abuse.

Step 5 — Attack Scenarios & Examples

  1. Data Poisoning Attack

    • Inject fake financial transactions into training data to weaken fraud detection.

  2. Adversarial Example

    • Add imperceptible noise to an image to bypass security camera recognition.

  3. Model Extraction

    • Query an ML API repeatedly to replicate the proprietary model.

  4. Prompt Injection

    • Embed malicious instructions in text input to override LLM safety constraints.

Step 6 — Define Security Controls

  • Data Pipeline Security

    • Provenance tracking, anomaly detection, and signed datasets.

  • Model Hardening

    • Adversarial training, differential privacy, and watermarking.

  • API & Endpoint Security

    • Strong authentication, rate limiting, and input validation.

  • Monitoring

    • Drift detection, adversarial pattern detection, and security event logging.

Step 7 — Continuous Review

  • Update the threat model after every model retraining, data source change, or new attack discovery.

  • Integrate into MLSecOps pipelines for automated re-assessment.

3. Common Tools for ML Threat Modeling

  • Microsoft Threat Modeling Tool (extended for ML)

  • MITRE ATLAS — AI adversarial threat knowledge base.

  • IBM ART — Adversarial robustness testing.

  • OWASP AI Security & Privacy Guide — AI-specific STRIDE mapping.

4. Example: ML Threat Model Diagram for a Fraud Detection System

Assets:

  • Training data (transaction history)

  • Model weights & parameters

  • Inference API

Threats:

  • Data poisoning via fraudulent data uploads

  • Adversarial transaction inputs to evade detection

  • Model extraction via high-volume API queries

Controls:

  • Verified data ingestion with hashing and source validation

  • Adversarial testing before deployment

  • API authentication and query throttling

5. Final Thoughts

ML Threat Model Creation is the foundation of securing AI systems. Without it, organizations risk deploying models blind to adversarial manipulation and data integrity breaches.

When embedded into MLSecOps, threat models:

  • Enable proactive defense

  • Guide security investment

  • Ensure compliance with AI regulations

In the AI era, threat modeling is not just a checklist — it’s a survival strategy for enterprises.

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