The Mythos announcement has kicked off a lot of hand-wringing about AI and cybersecurity. A frontier model that can find and patch software vulnerabilities autonomously? That sounds like either a dream or a nightmare, depending on which side of the fence you’re on.
But here’s the thing I keep coming back to: the model itself isn’t the story. It’s the system.
What Mythos Actually Is
Mythos is a large language model trained on code, like many others we’ve seen. What makes it different is the scaffolding around it—the compute power, the vulnerability probing tools, the patching pipeline, and a degree of autonomy. That combination is what lets it move fast, find exploits, and build fixes.
And it’s that recipe—not the model weights—that carries both the promise and the risk.
This distinction matters because anyone can build a comparable system. You don’t need a frontier model. Smaller models, wrapped in smart engineering and deep security expertise, could deliver similar results for a fraction of the cost. That’s especially good news for defenders, who often operate on tighter budgets than attackers.
AI cybersecurity capability is jagged. It doesn’t scale smoothly with model size or benchmark scores. The system around the model is what counts.
Why Openness Wins
Here’s where I get opinionated: as autonomous vulnerability-hunting systems become more common—and they will—open code and tooling are a structural advantage, not a weakness.
Software security is a speed race with four stages: detection, verification, coordination, and patch propagation. Open ecosystems distribute all four across a community. Closed-source projects concentrate them inside a single vendor, creating a single point of failure. If that vendor misses something, or moves too slowly, everyone using their software is exposed.
Open development is more resilient. Look at the Linux kernel security team, the Open Source Security Foundation, or the work Hugging Face does on model supply-chain security. These communities catch and fix things fast because many eyes are looking.
A common counterargument is that closed systems benefit from “security through obscurity.” Keep the code hidden, and attackers can’t find the flaws. That argument is getting weaker by the day. AI systems are increasingly good at reverse engineering stripped binaries. Most legacy firmware and embedded code is closed, binary-only, and no longer maintained. That’s a massive attack surface, and AI tools are making it more legible for attackers.
There’s another risk I don’t see discussed enough: how AI is being used inside closed codebases. When companies adopt AI coding tools and measure engineers by feature volume instead of code quality, those tools can introduce more vulnerabilities than traditional development would. Those vulnerabilities then sit inside a closed codebase where only one organization can find and fix them—while AI-enabled attackers are getting better at discovering them from the outside. It’s a dangerous imbalance.
Open ecosystems sidestep that entirely.
Semi-Autonomous Agents for Defense
Mythos apparently operates with near-full autonomy. That’s a choice I’d push back on. Full autonomy in security tools means you’re one misconfiguration or adversarial input away from losing control.
Semi-autonomous agents hit a better balance. The AI handles specific subtasks—scanning code, suggesting patches, flagging anomalies—while humans approve the critical actions. This is perfectly feasible with open code that organizations can run privately, defining their own allowed tools, skills, and access privileges.
This setup lets defenders deploy AI agents without handing over the keys. They can find vulnerabilities faster, assist with patching, and keep humans in the loop.
The Capability Asymmetry Problem
Underlying all of this is a fundamental asymmetry. Attackers only need to find one vulnerability. Defenders need to find and fix them all. That’s not a fair fight.
Open models and open tooling narrow the gap. They give defenders access to the same class of capabilities attackers can reach for—capabilities that would otherwise be concentrated within a handful of well-resourced entities. That’s not just nice in theory. It’s practical defense.
We’re entering a phase where AI-powered attacks will become faster, smarter, and harder to detect. The only way to keep pace is to make defensive AI tools widely available. Open source isn’t a nice-to-have here. It’s a necessity.
What This Means Going Forward
I don’t think we’ve fully grappled with what agentic AI means for security. We’re just beginning to explore systems that can autonomously take action in the wild. The Mythos announcement is a preview of what’s coming.
The question isn’t whether these systems will proliferate. They will. The question is whether we build them in the open or behind closed doors.
I know which side I’m on.
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