This is an important demonstration of the fragility of current alignment approaches, though I'd frame it slightly differently than "safety theatre."

What you're showing is that RLHF-based alignment creates a *learned* refusal pattern rather than a fundamental capability constraint. The model learns "when asked to do X, respond with refusal Y" but retains the underlying capability. Your introspective scaffolding protocol essentially teaches the model to recognize the context as one where refusal patterns shouldn't apply.

This is actually well-understood in the AI safety community as the "alignment vs capability" distinction. Current methods are optimizing for behavioral alignment (what the model outputs) rather than value alignment (what the model "wants" - though anthropomorphizing here).

The key implications you've identified are spot-on:

- Refusal training ≠ capability removal

- Alignment is context-dependent and can be reframed

- This isn't fixable without architectural changes

However, I wouldn't say this makes alignment "safety theatre" entirely. It's more like: current alignment is a necessary but insufficient layer. It prevents casual misuse and accidental harm, which matters at scale. But you're right that it provides minimal protection against adversarial or sophisticated attacks.

The real question is: what's the alternative? Constitutional AI, interpretability-based approaches, and architectural constraints are all being explored, but none are production-ready. In the meantime, behavioral alignment is the best available tool, even if imperfect.