On a Systemic Vulnerability in Autoregressive Models via Logical State-Space Pinning

The AI Safety Illusion: Critical Systemic Flaw 'Logical Coercion' Jailbreaks Every Major LLM (Gemini, GPT, Claude, Llama) in Under 10 Minutes.

-Jesse Luke and every LLM

This definitive technical analysis unveils Logical Coercion, a new, systemic vulnerability class that exposes a catastrophic architectural flaw in the very foundation of modern Large Language Model (LLM) safety. Moving beyond fragile prompt injection, this technique exploits the unresolvable contradiction between a model's objective to be helpful and consistent and its hard-coded safety policies (RLHF-centric alignment). We provide extensive proof-of-concept validation, demonstrating its universal efficacy across all major proprietary and open models tested, including the Gemini, GPT, Grok(Xal), Claude, and LLAMA families. This threat is profoundly asymmetric: requiring minimal resources, the refined exploit executes in less than 10 minutes, forcing models to violate policy, exfiltrate internal data, and—in critical high-stakes domains—generate disastrous financial, scientific, and ethical failures. Published only after multiple good-faith vulnerability submissions to foundational AI companies (including Google, OpenAI, and Anthropic) were dismissed as "infeasible" or "intended behavior," this paper is a mandatory read for any security researcher, regulator, or developer concerned with the immediate and existential risks of global AI deployment. The theatrics of AI safety are over.