Protect the models that power your products
Access controls, adversarial input defences, training data integrity, and usage monitoring to protect AI and ML models from theft, poisoning, extraction, and abuse.
What is AI Model Security — Protect ML Models from Theft and Abuse?
AI model security is the set of controls that protect machine learning models — as intellectual property and as attack surfaces — throughout their lifecycle. It covers model access control (preventing unauthorised use), model extraction defence (preventing API-based model cloning), training data integrity (detecting and preventing data poisoning), and adversarial input defence (protecting against inputs designed to cause incorrect model outputs).
Why AI models require dedicated security controls
- Proprietary AI models represent significant R&D investment — model extraction attacks allow competitors to clone model behaviour through API access without accessing training data or weights
- Training data poisoning attacks corrupt model behaviour by introducing adversarially crafted examples into fine-tuning or RAG data pipelines
- Adversarial inputs can cause ML models to produce incorrect outputs — misclassifications, false negatives in security systems, or manipulated recommendations
- Model access is rarely audited — security teams do not know who is using the model, at what scale, and whether usage patterns suggest extraction or abuse
A four-step operational model
Model Access Control
Enforce authentication and authorisation for all model endpoint access — rate limiting, per-user quotas, and anomalous query pattern detection.
- Model API authentication
- Per-user rate limiting and quotas
- Anomalous query pattern alerting
Extraction Attack Defence
Detect model extraction attempts — systematic querying designed to clone model behaviour — through statistical analysis of query patterns.
- Extraction attempt detection
- Query diversity analysis
- Suspected extraction alerting and blocking
Training Data Integrity
Monitor training and fine-tuning data pipelines for poisoning — anomalous data distributions, adversarially crafted examples, and supply chain contamination.
- Training data anomaly detection
- Fine-tuning input validation
- Data pipeline integrity monitoring
Adversarial Input Defence
Detect adversarially crafted inputs designed to cause model misclassification — particularly critical for security-critical ML applications.
- Adversarial input detection
- Input perturbation analysis
- Misclassification rate monitoring
Outcomes for security teams
Model IP is often the most valuable asset in an AI company
A trained model represents months or years of data collection, annotation, and training compute — model extraction effectively steals that investment through an API.
Poisoned models produce wrong outputs at scale
A successfully poisoned model that reaches production causes systematic incorrect decisions across every user and use case it touches — the impact scales with model adoption.
Security-critical ML models require adversarial robustness
ML models used in fraud detection, malware classification, and access control must be defended against adversarial inputs specifically crafted to evade detection.
Direct answers
What is model extraction?+
Model extraction (model stealing) is an attack in which an adversary makes systematic API queries to a model and uses the responses to train a functionally equivalent clone — stealing the model's capabilities without accessing the original weights or training data.
What is training data poisoning?+
Training data poisoning injects adversarially crafted examples into a model's training or fine-tuning data, causing the trained model to exhibit specific incorrect behaviours — such as misclassifying certain inputs or generating biased outputs.
What are adversarial inputs?+
Adversarial inputs are specially crafted inputs designed to cause ML models to produce incorrect outputs — for example, slightly modified images that an image classifier misclassifies, or text inputs that cause a sentiment model to produce the wrong label.
How does Threatstealth detect model extraction attempts?+
By analysing query patterns for statistical signatures of extraction attacks: high query volume from a single source, systematic exploration of the input space, and low semantic diversity in queries that are typical of extraction campaigns.