Generate comprehensive threat models for your ML systems using STRIDE adapted for machine learning. Step-by-step wizard produces risk scores, identified threats, and actionable mitigations.
Select the model architecture. Different model types have different attack surfaces and vulnerability profiles.
Deployment context determines the attack surface. Cloud APIs have different risks than edge devices.
Higher sensitivity means stricter confidentiality requirements and greater impact from data leakage threats.
Access level determines what adversaries can observe, query, or manipulate. More access means larger attack surface.
Generating your STRIDE-based ML threat model...
The ML Threat Model Generator applies Microsoft's STRIDE framework to machine learning systems. STRIDE was originally created to systematically identify security threats in software applications, but traditional STRIDE does not account for the unique attack surfaces introduced by ML components. This tool adapts each STRIDE category to the specific risks that affect ML pipelines, model inference, and training data.
When you use the wizard, you provide four key parameters about your ML system: the model type, deployment method, data sensitivity, and access level. The tool uses these inputs to calculate risk scores for each STRIDE category, adjusted for your specific architecture. An LLM deployed as a public cloud API with healthcare data faces very different threats than an image classifier running on an edge device with public data. The wizard accounts for these differences automatically.
Spoofing in the ML context means model impersonation. Attackers can create counterfeit model endpoints that mimic your API, redirect user traffic, or deploy trojanized models that appear legitimate but contain backdoors. When a model is distributed as a downloadable artifact, verifying its authenticity becomes critical. Model signing, checksum verification, and secure model registries are the primary defenses against ML spoofing attacks.
Tampering covers adversarial inputs, data poisoning, and model manipulation. Adversarial examples are carefully crafted inputs that look normal to humans but cause the model to misclassify. Data poisoning happens during training, where an attacker injects malicious samples to alter model behavior. Both attacks exploit the fundamental statistical nature of ML models. Defenses include input validation, adversarial training, robust aggregation methods, and data provenance tracking.
Repudiation addresses the lack of audit trails in ML systems. When a model makes a decision, you need to be able to trace which version of the model was used, what inputs it received, and what outputs it produced. This is particularly important for regulated industries where decisions must be explainable and auditable. Comprehensive logging, model versioning, and immutable prediction records are essential mitigations.
Information Disclosure in ML encompasses model extraction attacks, membership inference, and training data reconstruction. Model extraction allows attackers to steal your model's behavior by making systematic queries and training a surrogate model on the outputs. Membership inference reveals whether specific data points were in the training set, which can leak private information. Model inversion can reconstruct approximations of training data from model outputs. Rate limiting, differential privacy, and output perturbation are key defenses.
Denial of Service for ML systems goes beyond traditional network-layer attacks. ML-specific DoS includes crafting inputs that maximize compute time (adversarial complexity attacks), exploiting auto-scaling to cause cost exhaustion, and triggering excessive resource allocation through specially designed batch requests. Inference endpoints are particularly vulnerable because ML models are computationally expensive to run compared to traditional API endpoints.
Elevation of Privilege through ML components happens when attackers exploit model behavior to bypass access controls, extract credentials from training data memorization, or use prompt injection to make LLMs execute unauthorized actions. In systems where ML outputs influence access decisions (fraud detection, authentication), adversarial manipulation of model outputs can effectively grant elevated privileges.
The threat model generator calculates risk scores using a combination of likelihood and impact, adjusted for your system profile. Likelihood factors include the attack surface size (public API vs. restricted access), the technical sophistication required (adversarial examples on LLMs vs. edge classifiers), and the availability of existing tools and research for each attack type. Impact factors include data sensitivity (public data vs. HIPAA/PCI), the consequences of model compromise (recommendation quality vs. autonomous vehicle safety), and regulatory exposure (GDPR fines, EU AI Act compliance).
Each threat receives a risk score from 1 to 10, categorized as Critical (8-10), High (6-7), Medium (4-5), or Low (1-3). The overall system risk is calculated as the weighted average across all STRIDE categories, with additional weight given to the highest-scoring individual threats. This ensures that a single critical vulnerability does not get averaged away by many low-risk items.
The exported threat model document serves as a living security artifact. Review it during architecture reviews, update it when you change model types or deployment methods, and use it to prioritize security investments. For teams subject to the EU AI Act (fully enforced from August 2026), this threat model can serve as part of your risk assessment documentation. For organizations following NIST AI RMF or ISO 42001, the STRIDE-based approach maps well to their required risk identification processes.
The mitigations listed in the report are actionable recommendations, not theoretical suggestions. Each mitigation is specific enough to turn into a Jira ticket or engineering task. Prioritize mitigations for critical and high threats first, then systematically work through medium and low items. Re-run the generator whenever your system architecture changes, such as moving from batch to real-time inference, switching model types, or changing data sensitivity levels. For a comprehensive security posture assessment, pair this threat model with the ML Model Security Checklist which provides 50+ granular controls to verify.
This tool is designed to complement, not replace, your existing security practices. If your organization already uses STRIDE for application security, the ML-adapted version extends your threat modeling to cover ML-specific attack vectors. The output format is compatible with common threat modeling tools and can be imported into risk registers. Teams using the OWASP ML Top 10 can cross-reference the STRIDE categories with OWASP's ranked list to validate coverage. The generator ensures you are not missing threats that fall outside the OWASP top 10 but are still relevant to your specific deployment context.
STRIDE is a threat classification framework originally developed by Microsoft, adapted here for ML systems. It covers six categories: Spoofing (model impersonation), Tampering (adversarial inputs and data poisoning), Repudiation (lack of audit trails), Information Disclosure (model extraction and data leakage), Denial of Service (resource exhaustion attacks), and Elevation of Privilege (exploiting ML components to gain unauthorized access). Each category maps to specific ML attacks and defenses.
Start by documenting your system architecture: model type, deployment method, data sensitivity, and access levels. Then systematically evaluate each STRIDE category against your architecture. For each threat, assess likelihood and impact to calculate risk scores. Finally, identify mitigations and prioritize them by risk level. This tool automates the entire process with a guided wizard that asks four questions and generates a complete threat model document.
The most common threats include adversarial examples (crafted inputs causing misclassification), model extraction (stealing model behavior through API queries), data poisoning (manipulating training data), model inversion (reconstructing training data from outputs), and supply chain attacks (compromised dependencies or pre-trained weights). For LLMs specifically, prompt injection is the dominant new attack vector. Severity depends on deployment context and data sensitivity.
Yes, the ML Threat Model Generator is completely free. It runs entirely in your browser with no data sent to any server. You can export the generated threat model as markdown for documentation. No sign-up or account required. The tool is part of the Zovo Tools network of free developer utilities.
STRIDE is a general threat classification framework providing a systematic methodology for discovering threats through six categories. OWASP ML Top 10 is a curated, ranked list of the most critical ML security risks based on prevalence and impact. Both are complementary: use STRIDE for comprehensive threat discovery to ensure nothing is missed, and use OWASP ML Top 10 to validate your prioritization against industry consensus on the most critical attack vectors.