Mythos is a “frontier AI model”, a large language model (LLM) that can be used to process software code (among many other things). This follows a general trend in LLM development, where LLM performance on code-related tasks has recently skyrocketed. What’s particularly significant about Mythos is the system it’s embedded within: It's the system, not the model alone, that has enabled Mythos to rapidly find and patch software vulnerabilities. Understanding this distinction is key to understanding the current landscape of AI cybersecurity.

Together, these ingredients can uncover software vulnerabilities, find exploits, and build patches. It’s in this recipe — not in any one model — that both the benefits and the risks come in.

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.

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