From CVE reproduction to adversarial training in CTF environments, we create the foundations for agents that autonomously defend the systems we all depend on.
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the security data their foundation models train on.
AI creates entirely new categories of offense. Autonomous agents probe, adapt, and spread without human direction. For the first time, machines don't just test. They craft exploits with intelligence, building attack chains no human ever designed.
Meanwhile, AI-generated code is flooding production faster than any team can review. Every insecure pattern an LLM repeats becomes a vulnerability at scale, millions of repos shipping the same flaws. The attack surface grows with every commit.
We can help prevent some of this, and where we can't, we make defense so intelligent and so fast that attackers lose their edge.
We start by benchmarking what models actually get right and where they fail. Do they remove every attack vector? Are they capable of detecting a breach in real time? The data tells us exactly what to fix.
Then we build training datasets from scratch, curated from vulnerability disclosures, patch histories, and synthetic attack scenarios, and post-train models until security reasoning is part of their weights.
When we can't work on the model directly, we collaborate with agent companies to design the workflows around it: custom tooling, evaluation harnesses, and agentic pipelines. The (difficult) science of transforming a capable model into a security operator.
A model that has learned from every vulnerability ever documented, every patch ever applied, and every exploit ever disclosed has the potential to write code that is secure by default, every line, every time. But we're not there yet. Right now models are confident and wrong.
Getting from here to there requires deliberate, expert-driven work at the model level. We design testing environments that expose where models fail and craft reward functions that teach them to reason about security.
The end state is an autonomous defender. An AI that doesn't wait for a human to triage alerts or write rules. It reviews code as it's written, audits infrastructure configs before they deploy, flags misconfigurations in CI/CD pipelines, and hardens cloud environments in real time. From source code to Kubernetes manifests, IAM policies, network rules, and runtime behavior. A security peer that thinks across your entire stack, around the clock.
If you're training AI models, building cybersecurity products powered by AI, or deploying AI-generated code at scale, we should talk.
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