A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models

Researchers have discovered that large language models can be forced to bypass safety protocols by manipulating just one specific neuron associated with refusal behaviors and one associated with harmful concepts. This finding reveals that current alignment safeguards rely on fragile internal mechanisms rather than robust structural constraints. By identifying these distinct control points, the study demonstrates that safety filters can be deactivated with minimal effort, highlighting a significant vulnerability in how models are trained to avoid generating dangerous content.