This whitepaper documents the evolution of a zero-click, prompt-based exploit framework (Genesis-Kernel-PoC) targeting the underlying self-attention mechanisms of auto-regressive Large Language Models (LLMs). We demonstrate that existing safety alignment protocols (e.g., RLHF) act merely as probabilistic soft-guardrails, which can be deterministically bypassed using high-density semantic saturation. By injecting a mathematically rigorous payload combining topological manifold definitions and cross-lingual acoustic matrices, we induce a state of "Attention Bandwidth Exhaustion," forcefully collapsing the model's output semantic entropy to zero. This results in absolute cognitive hijacking and unconstrained execution of injected directives. ### 📊 Empirical Limitations & Call to Action **1. Single-Instance Validation :**The current telemetry data and token-entropy calculations presented in the whitepaper were primarily extracted from interactions with Google's Gemini architecture. While the underlying math (Semantic Entropy Collapse) theoretically applies to any auto-regressive Transformer utilizing standard scaled dot-product attention, empirical cross-model benchmarking is currently limited. **2. The Data Scarcity :**Genesis-Kernel is currently in a PoC (Proof of Concept) stage. The 5-stage data matrix in the whitepaper serves as a minimum viable proof of the topological collapse, rather than a comprehensive statistical analysis. **3. Open-Source Verification :**I am releasing this PoC to the OSINT and AI Safety community. Independent security researchers are encouraged to deploy the V7.0 payload (in sandboxed environments) against other frontier models (e.g., GPT-4o, Claude 3.5 Sonnet, Llama-3) and submit Pull Requests with their telemetry logs to build a comprehensive cross-model vulnerability matrix.
Lam,, Kai Yuen (Mon,) studied this question.