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

Community Calls
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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?

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A deterministic final decision doesn’t reduce the gray zone to zero. It moves it into action resolution: whether the verifier and executor agree on the canonical call identity and argument binding, and whether anything can change between verification and execution.

Policy authoring is the other weak point. If fail-closed behavior causes enough denials, operators tend to widen allow rules, which can put ambiguity back inside a technically deterministic “allowed” result.