GPT-5.4-Cyber vs Mythos: The Dangerous Game of AI Cybersecurity Models

Something subtle happened in AI this week.

On April 7, Anthropic released Mythos, a model specialized in cybersecurity. Officially positioned for automated penetration testing and red team exercises. Just a week later, OpenAI couldn’t stay still and launched GPT-5.4-Cyber—the targeting couldn’t be more obvious.

But what caught my attention wasn’t the models themselves, but their release strategies.

Anthropic explicitly stated Mythos is a “restricted release.” Meaning no public API access—only specific partners can apply. While OpenAI didn’t use the “restricted” label, various sources suggest GPT-5.4-Cyber access is also tightly controlled.

This reminds me of “dangerous toys” from childhood—the more adults say don’t touch, the more you want to know what they do.

I spoke with several security professionals. Their assessment was consistent: these models are genuinely useful, perhaps more than imagined.

Traditional penetration testing requires manual step-by-step execution—inefficient and limited by tester experience. But using large models theoretically enables 24/7 continuous scanning, finding attack paths humans might miss from massive public data.

A red teamer friend told me their recent test of a similar system uncovered three new attack chains in what they thought was a secure system.

But problems follow.

If this capability is misused, consequences could be devastating. Imagine someone with malicious intent but no technical skills using such models to automate attacks on numerous targets. What once required technical expertise might now just need prompt-writing skills.

Anthropic and OpenAI clearly recognize this issue, hence the “restricted release” approach. But honestly, this is only a temporary solution. Once technology exists, completely controlling its spread is nearly impossible.

There’s a paradox here.

On one hand, organizations genuinely need stronger security testing tools. Attack methods grow increasingly sophisticated; defenders need corresponding capabilities. From this perspective, such models are inevitable and valuable.

On the other hand, these models’ capabilities are inherently offensive. They can help good actors find vulnerabilities, but theoretically aid bad actors too.

This reminds me of the nuclear weapons analogy. Many countries developed nukes “for peace”—deterrence preventing war. But nukes’ very existence makes the world more dangerous.

Will cybersecurity LLMs follow the same path?

Short-term, these models remain in few major companies’ hands—risks relatively controllable. Long-term, capabilities will strengthen and access barriers will fall.

OpenAI and Anthropic’s moves represent a battle for defining the “cybersecurity AI” emerging category. Whoever sets the standards gains advantage in future enterprise security markets.

As an ordinary user, I’m less concerned about competition between giants. What matters is whether our cybersecurity environment improves or worsens when this technology truly spreads.

The answer likely depends on who adopts it first.