Generate tailored security audit checklists for your machine learning models. Select your model type and receive 20+ actionable items covering data poisoning, model extraction, adversarial robustness, privacy, and access control — each with severity ratings.
Different model architectures have different security profiles. The audit checklist adapts to your selection.
Security audits for machine learning models differ fundamentally from traditional software security assessments. While conventional audits focus on code vulnerabilities, injection attacks, and network security, ML systems introduce entirely new attack surfaces: the training data, the model weights, the inference pipeline, and the feedback loops that connect predictions back to future training. This tool generates comprehensive security checklists tailored to your specific model architecture, because a computer vision model faces different threats than a recommender system or an NLP transformer.
When you select your model type, the audit engine loads a curated database of security controls organized into five categories. Each control is weighted for relevance to your model architecture, producing severity ratings that reflect real-world risk rather than theoretical concern. A prompt injection check is critical for NLP models but irrelevant for regression models. An adversarial patch defense is essential for vision models but does not apply to recommender systems. The tailoring eliminates noise and ensures every item on your checklist is actionable for your specific deployment.
Data poisoning is the process of manipulating training data to corrupt a model's learned behavior. The audit checks for label integrity verification (ensuring training labels have not been flipped), statistical distribution monitoring (detecting whether injected samples have shifted the data distribution), provenance tracking (knowing the origin of every training sample), and outlier detection pipelines (automatically flagging anomalous data points before they enter training). For classification models, the audit includes targeted poisoning checks where specific classes are manipulated. For NLP models, it covers text poisoning through synonym replacement and character-level perturbations that survive preprocessing.
The severity of each data poisoning control depends on your data sourcing strategy. Models trained on scraped web data face higher poisoning risk than models trained on curated internal datasets. The audit adjusts accordingly, marking web-sourced data validation as critical while treating internal data lineage as medium priority. This context-aware rating prevents teams from over-investing in low-risk controls while neglecting high-risk ones.
Model extraction attacks steal your model's behavior by making systematic queries to the inference API and training a surrogate model on the input-output pairs. The audit covers API rate limiting configuration (queries per minute, per hour, per user), output perturbation (adding calibrated noise to confidence scores), watermarking techniques (embedding detectable signatures in model outputs), query pattern detection (identifying systematic probing behavior), and model fingerprinting (techniques to prove ownership if a stolen model is discovered). For recommender systems, extraction is particularly dangerous because the recommendation logic represents significant business value.
Adversarial robustness refers to a model's ability to produce correct outputs when inputs are deliberately perturbed. The audit checks for input validation and sanitization, adversarial training implementation (training on adversarial examples to build resistance), ensemble defenses (using multiple models to reduce single-point failures), certified defense mechanisms (provably robust within defined perturbation bounds), and runtime adversarial detection (identifying and rejecting adversarial inputs during inference). Vision models receive additional checks for spatial transformations, patch attacks, and physical-world adversarial objects. Classification models get targeted vs. untargeted attack resilience checks.
Privacy in ML extends beyond data encryption and access control. The audit verifies differential privacy implementation during training (mathematically bounding what the model can reveal about individual training samples), membership inference resistance (preventing attackers from determining if a specific record was in the training set), model inversion protection (stopping reconstruction of training data from outputs), data minimization practices (collecting and retaining only what is necessary), and right-to-erasure compliance (the ability to remove a person's data influence from a trained model, which requires either retraining or machine unlearning techniques).
With the EU AI Act enforcement beginning August 2026, privacy controls are no longer optional for high-risk AI systems. The audit maps each control to relevant regulations including GDPR, CCPA, EU AI Act, and HIPAA, making it straightforward to identify which controls serve compliance requirements versus security-only purposes. This dual mapping helps teams justify security investments to stakeholders who prioritize regulatory compliance.
The final category covers the infrastructure surrounding your model: API authentication mechanisms, authorization granularity (who can query which models), model artifact storage security (encrypted at rest, signed, versioned), CI/CD pipeline security (preventing unauthorized model deployments), monitoring and alerting (detecting anomalous query patterns, model drift, and performance degradation), and incident response procedures specific to ML failures (model rollback procedures, data breach protocols for training data exposure). These controls form the foundation that makes all other security measures effective.
The security score is calculated as a weighted completion percentage across all five categories. Critical items carry four times the weight of low-severity items, and high items carry twice the weight. This means checking off several low-severity items will not compensate for missing critical controls. A score above 80% indicates a strong security posture with critical and high items addressed. Between 50-80% suggests significant gaps that should be prioritized. Below 50% indicates the model has fundamental security vulnerabilities that need immediate attention. Use the export feature to share the checklist with your team and track progress over time.
An ML model security audit covers five key domains: data poisoning defenses (training data integrity, label validation, outlier detection), model extraction prevention (rate limiting, watermarking, output perturbation), adversarial robustness (input validation, adversarial training, certified defenses), privacy controls (differential privacy, membership inference protection, data minimization), and access control (authentication, authorization, API security). Each domain contains specific, actionable checklist items tailored to your model type.
Severity ratings are assigned based on potential impact and likelihood of exploitation. Critical items represent controls that, if missing, expose the model to high-probability, high-impact attacks. High items are important defenses that significantly reduce attack surface. Medium items provide defense-in-depth. Low items are best practices for improved security posture. Ratings are adjusted based on your specific model type, so the same control may be critical for one architecture and low for another.
The tool supports five model types: Classification (CNNs, decision trees, SVMs), Regression (linear, XGBoost, neural networks), NLP (transformers, BERT, GPT, sentiment analysis), Computer Vision (object detection, segmentation, GANs), and Recommender Systems (collaborative filtering, content-based, hybrid). Each generates a different checklist because attack surfaces differ significantly between architectures.
Yes, export in two formats: Markdown for documentation systems (Confluence, Notion, GitHub) and PDF for formal audit reports. The export includes all items, severity ratings, your completion status, and an overall security score. Share with your security team or include in compliance documentation for EU AI Act, GDPR, or SOC 2 requirements.
Run an audit whenever you retrain, change training data, modify deployment architecture, or update dependencies. At minimum, conduct quarterly audits for production models. For sensitive data (PII, healthcare, financial), monthly audits are recommended. The EU AI Act (enforced August 2026) requires ongoing risk assessment for high-risk AI systems, making regular audits a compliance requirement rather than a recommendation.