Cybersecurity spent two decades hardening the app. For an AI company, the highest-value target is now your weights, data, and the people and pipelines around them. Common Defense secures the app and that surface, from proactive review through live incident response.
The target moved
For twenty years, security meant protecting the app and the customer data behind it. For an AI company, your most valuable asset sits elsewhere. It’s the model weights, the training data, and the pipelines that produce them. An attacker who reaches those never has to touch your app.
The model is only one asset in the chain.
The compromise path often starts before inference: a dependency, dataset, pipeline secret, registry permission, or human with production access.
- 01
Training data
Poisoning, sensitive rows, source leakage
- 02
Feature store
Overbroad access, retained secrets
- 03
Training pipeline
CI/CD compromise, poisoned dependencies
- 04
Model registry
Weight theft, unsigned artifacts
- 05
Inference API
Prompt abuse, extraction, key leakage
Where AI companies get hit
The way in is rarely an exploit. It’s a poisoned dependency in your training stack, a phished engineer with pipeline access, or a leaked key to a model store. Supply-chain attacks on open-source AI tooling now turn a package your team trusts into an exfiltration path. We map that surface, close the gaps, and respond when something moves.
We attack your models before someone else does
Your AI features are an attack surface of their own. We probe them for prompt injection, jailbreaks, and training-data extraction, and the ways a user turns a helpful model into a leak. You find the failure modes on your terms, with a fix in hand, before they show up in someone else’s disclosure.