This matters because others can build comparable systems. Smaller models embedded in systems built with deep security expertise could potentially produce similar outcomes more cheaply, which is particularly promising for defense. AI cybersecurity capability is jagged: It doesn’t scale smoothly with model size or general benchmark performance. The system the model is embedded within matters a lot.

So what Mythos has demonstrated is that it’s possible to build an AI system that finds and addresses software vulnerabilities. We already knew this was possible and there has been increasing work on this, but we’re just beginning to explore what it means in the context of agentic AI: Systems that can rapidly and autonomously take action.

As autonomous systems that identify software vulnerabilities proliferate (and they will), open code and tooling can help level the playing field. Software security has become a speed race across four stages: detection, verification, coordination, and patch propagation. Open ecosystems distribute these across a community, where more closed-source projects centralize knowledge and action across all four stages inside a single vendor, representing a single point of failure where only one organization can see and fix the code. The distributed nature of open development is robust to such constraints, and can be especially powerful in communities with dedicated security professionals, like the Linux kernel security team, the Open Source Security Foundation, and the team at Hugging Face working on model and supply-chain security.

A common argument for more closed systems is proprietary obscurity, where the code underlying a system is inaccessible. Unfortunately, this provides less protection than it used to. AI systems are increasingly able to assist with reverse engineering of stripped binaries, which matters because most legacy firmware and embedded code is closed, binary-only, and no longer maintained. That code represents a huge attack surface, and it’s becoming more legible and accessible as AI tools improve.