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OpenAI announced its GPT-5.4-Cyber AI model that echoes Anthropic's Mythos: It can quickly detected vulnerabilities in software but, in the wrong hands, can also make it easy to develop exploits against them. Like Anthropic, OpenAI is limiting access to the model, though to a larger number of security pros than Anthropic.

The post OpenAI Follows Anthropic in Limiting Access to Its Cyber-Focused Model appeared first on Security Boulevard.

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Apr 15, 2026 - Jeremy Snyder - Myth and Mythos: A Decades Old Problem in the Spotlight
Background: Beyond the Headlines
The recent leak and confirmation of Anthropic Claude Mythos sent a ripple of anxiety through the cybersecurity community. In my recent conversations with security practitioners and leaders, there is a real concern that we are facing a brand-new, unsolvable category of AI risk. While there is legitimate cause for concern, we need to be careful not to let the technical "spectacle" cloud our strategic judgment. In fact, a recent conversation on LinkedIn compelled me to frame my own thinking on it. If you peel back the layers of AI hype, the underlying reality is much more grounded. Anthropic Mythos isn’t a fundamental shift in AI security; it is a massive, high-speed acceleration of a vulnerability management problem we’ve been dealing with (or rather, not dealing with) for decades. It’s time to stop looking at this as an AI story, and start focusing on systematic improvements to our approach.
It’s Not AI Security; It’s Vulnerability Discovery on Steroids
To understand the true impact of Anthropic Mythos, we have to see it for what it actually is: a super-charged, automated code scanner. This isn't a new conceptual threat, but rather a massive scaling problem where the speed of discovery has finally outpaced the speed of human response. The zero day clock shows that we’re in an era where the TTE (“Time to Exploit, sometimes also called "Mean Time To Attack" or MTTA) has shrunk to just 22 minutes, while the average "Mean Time To Patch" (MTTP, sometimes called Mean Time To Remediate or MTTR) remains stubbornly stuck between 50 and 160 days. (Side note - kudos to the Edgescan report on this. Also, I’m personally pleased to see updated statistical analysis on this. For the first 2 decades of my career, the MTTR for production vulnerabilities was stubbornly around 180 days.) This gap between exploit availability and remediation creates a window of exposure that is no longer manageable through existing processes. When a tool can find and weaponize a 27-year-old vulnerability in seconds, our traditional patching workflows become effectively obsolete. So we have three fundamental issues on this topic:
AI is just faster and can get through 1000x+ the volume of code as humans, in much less time.
Some AI is legitimately better at this, with reasoning and predictability models that find multi-step chained concatenation issues that have been missed for decades. The vulnerability scanning capabilities apply to both first-party and third-party applications, so vulnerabilities in both your code and your COTS need to be patched. The "Mythos" Reality Check: Turning Over Old Rocks, Finding New Bugs
For over twenty years, the industry has struggled with a persistent, systemic failure to keep up with the basics of patching. The root cause isn't a lack of awareness, but a combination of a few of the following factors:
All vulnerabilities are treated the same, whether on a laptop, server, public-facing system or internal system.
All vulnerabilities are treated the same, whether just on disk or actually loaded into memory
All vulnerabilities are treated the same, whether cyber attacks are happening against that vulnerability or not.
A paralyzing fear of breaking production with incompatible patches. Separation of duties - information security owns vulnerabilities but IT owns patching.
These are the old rocks. This is why there’s a massive accumulation of vulnerability debt. This is why tools like Mythos are so scary; they both find complex new zero-days and can simply capitalize on the "low-hanging fruit" we’ve ignored for years. Quite simply, if a patch takes months to test and deploy, you are defenseless against an automated script that can scan your entire perimeter in seconds. Mythos is the final proof that we need a fundamental shift in our thinking and behavior around vulnerability management, patching, and shipping of secure-by-design software.
The Real Takeaways
Not everything is an AI Problem: Mythos is a vulnerability management problem, not an existential AI crisis.
A Fundamental Shift in Behaviors is the Only Answer: You cannot fight the speed and thoroughness of a Mythos-equipped attacker with a manual, ticket-based patching process.
Focus on the Fundamentals: Follow first principles:
Automate patching for end-user devices. The risks are super low.
Patch quickly on production. The risk-reward calculation of breach versus downtime may merit this in many environments.
Check on microsegmentation to limit blast radius.
Check on role assignments and IAM permissions for cloud environments. Use dedicated roles with limited permissions.
Use containers and serverless compute infrastructure that limits the package inventory.
Automate patching wherever possible.
Demand better from software vendors.
Tear down organizational walls between infosec and IT.

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