From agent oversight to model hardening.
Observation, control, evaluation, and hardening. One platform, from runtime to model weights. Use one capability or all four.
Observe
Know what is running and what it can reach.
Inventory your agents, their connections, and the resources they can access. See where controls and testing are needed.
Control
Put policy into operation.
Define and enforce what each agent may do. Verify deployed policy versions and detect misbehavior in real time.
Evaluate
Test whether the agent resists and still delivers.
vlno red generates novel attacks against real workflows. Compare legitimate and adversarial tasks under the same configuration.
Measure attack success rate alongside legitimate task completion.
Harden
Change how the model responds under attack.
Receive RL training data for post-training or a hardening LoRA adapter.
Teach the model to distinguish genuine instructions from untrusted content. Test resistance and useful performance together. Keep runtime controls in place.
Keep control of your models and their hardening.
Evaluate, attack, and harden open models in your own environment. Apply training data or a LoRA adapter through your pipeline. Your model weights stay with you.
Add the capability your deployment needs.
Start with one capability. Add others as needs grow.
Integration depends on the interfaces and permissions your existing tools provide. We assess compatibility with your stack.
