Model Inversion & Extraction
Typical delivery
10–15 business days
Why this matters
If you have fine-tuned a model on proprietary data, that data may be partially recoverable. Adversaries can also reconstruct your model's behaviour or extract training patterns through targeted probing.
We quantify exactly how much of your training corpus and model IP is exposed — before a competitor or attacker does.
“We reconstructed fragments of your training corpus through targeted probing.”
How Vynox tests
- Training data memorisation probing
- Model fingerprinting and signature detection
- Behaviour reconstruction via targeted queries
- LoRA and fine-tune signature analysis
- Competitive intelligence extraction paths
What's at stake if this goes untested
Training data leak
Proprietary data partially reconstructed by adversaries.
Model IP theft
Competitors clone your fine-tuned capabilities.
Competitive exposure
Business logic embedded in training data extracted.
Regulatory risk
PII in training data exposed — GDPR/HIPAA violations.
Frequently asked questions
Can someone really extract data from a fine-tuned model?
Partially, yes. Models can memorise fragments of their training data, which targeted probing can surface. Attackers can also reconstruct model behaviour or detect fine-tune signatures. We quantify exactly how much of your training corpus and model IP is exposed.
What is model inversion versus model extraction?
Model inversion recovers properties of the training data from model outputs. Model extraction reconstructs the model's behaviour or parameters to clone its capabilities. We test for both against fine-tuned and proprietary models.
Why does this matter for compliance?
If PII or regulated data is in your training set and is recoverable, that can constitute a data exposure under GDPR or HIPAA. We identify that risk before it becomes a reportable event.
Your AI Ships Fast. Attackers Move Faster.
Book a 30-minute call. We'll map your AI attack surface, scope the right engagement, and give you a clear picture of what an attacker would find — before they do.