According to a report by CSO Online, researchers have discovered that modifying fewer than 10 neurons in a large language model (LLM) can covertly transform the system into a sleeper backdoor. This security vulnerability demonstrates how precise and minimal alterations to a neural network's internal structure can introduce hidden malicious behaviors without significantly disrupting the model's overall performance or standard operations. The findings highlight significant security concerns regarding the integrity, control, and auditing of complex artificial intelligence systems, emphasizing the potential risks associated with targeted manipulation of model weights and neural pathways.