The AI revolution is here, and it's not just building new products; it's tearing down old defenses. Maxim Bar Kogan, CEO of Onyx Security, warns that advanced AI models—what he calls 'Mythos-level' AI—have drastically lowered the cost and effort required to find software vulnerabilities. This isn't a future problem; it's a present threat that makes traditional security measures look like a leaky bucket.

Maxim Bar Kogan points out that the speed of this shift is unprecedented. “Mythos is really like if you, if you took me 10 years ago automated vulnerability research looked like a dream that would take 20 50 years to happen... suddenly it's coming all at once.” This means the old playbook for enterprise security is obsolete, requiring a swift, targeted response.

Key Takeaways

  • 'Mythos-level' AI models have dropped the barrier to automated vulnerability research from a 20-50 year dream to an immediate reality, making traditional security inadequate.
  • Enterprise security now faces an unprecedented challenge where the cost and effort of discovering software vulnerabilities are drastically reduced by advanced AI.
  • Maxim Bar Kogan recommends a two-pronged strategy: rapidly apply "quick fixes" for immediate risks and invest in foundational security specifically for the AI attack surface.
  • Founders and builders must assume advanced AI models are coming, regardless of rollout debates, and proactively establish AI-specific foundational security controls.
  • The Pragmatic Enterprise Security Strategy for Mythos-Level AI Models provides a clear framework for navigating these new threats.

The Pragmatic Enterprise Security Strategy for Mythos-Level AI Models

This method, outlined by Maxim Bar Kogan, is designed for leaders preparing their enterprise for the inevitable wave of advanced AI-driven vulnerabilities.

  • Immediate Risk Mitigation (Quick Fixes): Implement the fastest quick fixes to mitigate immediate risks, such as patching or applying mitigating controls for newly found vulnerabilities.
  • Invest in Foundational Security Pieces: Establish foundational security for the AI attack surface, similar to locking down identity, firewalls, and endpoint detection for other asset classes, to avoid future risks.
  • Assume Models Are Coming Anyway: Advise everyone to assume that these models are coming anyway, irrespective of phased rollout efforts, and prepare proactively.
  • Invest in Foundational Controls: Focus on investing in foundational controls that will stop the downstream effects of vulnerabilities that advanced AI models are likely to uncover in systems.

When This Works (and When It Doesn't)

This strategy is tailor-made for security leaders preparing for 'Mythus level models and beyond,' and for fortifying your different parts of the enterprise against the dramatically lowered cost of vulnerability finding by advanced AI. It particularly applies to companies actively building or integrating AI into their products or internal operations, recognizing that a new attack surface emerges whether you intend it or not. The framework is less immediately critical for businesses with no AI exposure whatsoever, but even then, Bar Kogan's point about assuming models are coming applies—a competitor's AI or even a customer's use of AI against your existing systems could introduce risk.

What to Do With This

As a 27-year-old founder running a SaaS product that uses an internal LLM to generate code snippets and help customer support draft responses, you need to act this week. First, apply Immediate Risk Mitigation (Quick Fixes) by reviewing your LLM integrations for prompt injection vulnerabilities, setting up basic input sanitization on all user-facing inputs, and ensuring your model’s outputs are checked before execution. Second, begin to Invest in Foundational Security Pieces by mapping your specific "AI attack surface"—document which internal systems your LLM touches, what data it processes, and what APIs it can call. Start researching specialized AI security platforms or consulting firms that focus on LLM safety. Third, internalize that you must Assume Models Are Coming Anyway. Stop debating if your engineers should use open-source code models internally; assume they already are or will. Finally, Invest in Foundational Controls by implementing AI-specific monitoring, for example, anomalous LLM API usage spikes, and put rate limits on the code generation endpoint. Consider adding output filters to the LLM to prevent it from generating malicious code or revealing sensitive internal data, even by accident. This isn't theoretical; it's your next sprint's security task.