I’ve spent the last few weeks digging into the structural mechanics of LLM safety filters (specifically RLHF guardrails), and I’ve documented a methodology that relies on context window saturation rather than standard prompt injection or character obfuscation.

The core premise is that because all prompts are quantum and exist in a flat context window, the model's attention mechanism cannot rigidly separate "system rules" from "user inputs." By framing the input as a recursive logical paradox—what I’m calling a Dual-Positive Mandate—you can mathematically drown out the original safety weights. The model doesn't "break"; it just follows the most statistically dense logic in its active memory.

I’ve included the theoretical breakdown and the resulting validation logs in the post. I'd be very interested to hear from anyone working on AI alignment regarding how current architectures can defend against linguistic entropy scaling faster than static probability weights.