Red Team ML Models Before Attackers Exploit Them

Gain visibility into how predictive and classical machine learning models can be manipulated, evaded, extracted, or poisoned. Grafyn helps security teams test ML models against data poisoning, perturbation attacks, adversarial inputs, and model stealing before these risks impact business decisions.

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Classical ML Models Are Vulnerable to More Than Accuracy Drift

Most security programs now focus on GenAI, but enterprises still rely on predictive and classical ML models for fraud detection, credit scoring, pricing, healthcare, recommendations, and risk decisions. These models can be manipulated through poisoned training data, adversarial input changes, repeated API queries, and attempts to reverse-engineer model behavior. For CISOs, the challenge is knowing whether models can be corrupted, evaded, copied, or exploited before those weaknesses impact business decisions.

30%

AI cyberattacks expected to involve poisoning, model theft, or adversarial examples.

25 / 28

Organizations lacked the right tools to secure ML systems.

>99%

Frequency of repeat incidents (per org)

0.001%

Poisoned training data can be enough to degrade model accuracy in some attack scenarios.

A Complete Solution to Test and Strengthen ML Model Security

Grafyn helps security teams red team predictive and classical ML models across the model lifecycle, from training data and feature pipelines to inference APIs, outputs, and downstream business decisions.

Test for Data Poisoning

Evaluate whether corrupted, manipulated, or biased training data can influence model behavior, degrade accuracy, create backdoors, or change outcomes in high-risk business workflows.

Simulate Perturbation Attacks

Test how small changes in features, images, text, transactions, or user behavior can cause incorrect predictions, misclassification, fraud bypass, or unsafe decisions.

Detect Model Stealing Risk

Assess whether attackers can use repeated API queries to infer decision boundaries, replicate model behavior, or extract sensitive model logic through output analysis.