vlno red, our own attack model, generates novel attacks no checklist would catch. Findings become training data that hardens the model, supported by observability, control, and evaluation across your stack.
Cost, sovereignty, and customization draw enterprises to open models. The weights arrive without the dedicated safety team and ongoing hardening a frontier lab provides.
Agents follow their training, not exactly your intent. Faced with an unusual request, an unexpected document or a situation nobody tested, they can harm you, attacker or not. Rules miss it. vlno red finds it first.
See your agents. Control their actions. Test their resistance. Harden the model. vlno connects all four. Start with one. Add what you need.
Discover agents, connections, and access.
Observe
Inventory your agents, their connections, and the resources they can access. See where controls and testing are needed.
Explore the solutionsEnforce policy and detect misbehavior.
Control
Define and enforce what each agent may do. Verify deployed policy versions and detect misbehavior in real time.
Explore the solutionsMeasure resistance against adaptive attacks.
Evaluate
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.
Explore the solutionsStrengthen model weights with RL training data or a LoRA adapter.
Harden
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.
Explore the solutionsOur foundational attack model generates novel, targeted attacks.
Evaluation and hardening rooted in open models.
Founders who led frontier-model red teams.
Our own research and vlno red expand the library.
Integrate where your existing tools support it.
Let's discuss the agents and workflows you need to protect.
We train the threat out.
