Can an LLM-based AI security system reliably defend against an autonomous AI agent like Mythos?

A developer has built EGA V9, an LLM-based AI security system, to test whether it can reliably defend against autonomous AI agents like Mythos, arguing that at the final execution boundary, uncertainty should not be interpreted as permission and that a deterministic decision is needed. The author invites scrutiny of the reasoning and implementation, asking where the execution boundary might break.

I keep coming back to a simple principle: 0 = 0. By that, I mean something very specific: at the final execution boundary, if an action cannot be verified as allowed, uncertainty should not be interpreted as permission. In security, I don’t think a probabilistic “probably safe” should be enough to authorize real-world execution. For an autonomous agent capable of changing its execution path, chaining tools, or taking unexpected actions, the gray zone between allowed and not allowed may itself become part of the attack surface. That raises a question I’ve been thinking about: Can a probabilistic LLM-based defender reduce that gray zone all the way to zero? Or does the final execution boundary require something different — a deterministic decision where an action is either verifiably allowed or it does not execute? I built EGA V9 to investigate this question: I’m not asking you to assume that my approach is correct. I’d genuinely like to know where the reasoning, implementation, or execution boundary breaks. What am I missing?