Anthropic's Mythos: not a hacker superweapon, but a wake-up call for developers

Anthropic unveiled the Mythos model — and the professional community immediately split into two camps. Some call it a potential "super-hacker weapon," seeing AI's ability to reason and automate tasks as a direct threat to digital security. Others — and their position is far more important — argue that the real cybersecurity crisis began long before any language model appeared.

Mythos is Anthropic's latest development, a company focused on safe AI development. The model draws attention not only for its performance but also for its ability to engage in complex multi-step reasoning — precisely what's needed for vulnerability analysis, exploit writing, or automated code weakness discovery. Security specialists see it as a dual-use tool: it can be used for defense, but it could also fall into the hands of attackers, lowering the barrier to entry for conducting sophisticated attacks.

However, the key thesis of the research is not about the power of any particular model. Experts point to a systemic weakness: the software development industry has treated security as a "later" concern for decades. Millions of lines of code have been written without considering basic security principles — and this is no secret to anyone, but no one was particularly rushed to fix it.

The emergence of a powerful AI tool capable of automatically finding vulnerabilities changes the equation. What once required weeks of work from an experienced penetration tester can now be done in hours — and not just by those protecting systems, but also by those attacking them.

According to industry research, more than 70% of breaches occur due to known, long-documented vulnerabilities for which patches have long existed. The problem is not a lack of solutions — the problem is a culture in which security updates are postponed in favor of development speed and new feature launches.

Mythos and similar models can become a catalyst for change — albeit a painful one. If automated vulnerability discovery becomes available to anyone who wants it, ignoring technical debt in security will become significantly more expensive. Legacy services with outdated code, startups that "launched first, security later," corporate systems with unpatched vulnerabilities — all of them end up in a high-risk zone.

On the other hand, the same AI can help with defense. Already, a number of companies are using language models for automated code audits, vulnerability discovery, and security test generation. Anthropic, with its stated focus on safe AI development, could become a leader precisely in this direction — a tool for defenders, not just a cause for concern.

The arrival of each new powerful model inevitably raises the same question: whose hands hold this tool? The concern around Mythos is not panic, but a reminder. Developers and companies that continue to postpone security issues risk becoming the most vulnerable precisely when attack tools become cheaper and more accessible. The real challenge is not a specific model, but the habit of treating security as someone else's problem.