Large language models (LLMs) like ChatGPT are impressive, but are they foolproof? Researchers are exploring how these models can be "jailbroken" – tricked into generating harmful or inappropriate content by crafting clever prompts. A new study digs into the mechanics of these attacks by looking at "attention," the mechanism by which LLMs weigh the importance of different words in a prompt. The research suggests that successful jailbreaks divert the model's attention from sensitive keywords, leading it down a harmless-seeming path to a malicious outcome. This "feint and attack" strategy uses nested prompts, with layers of benign tasks hiding the true harmful intent. Like a magician distracting their audience with flourishes, the jailbreak prompts lead the LLM to focus on less suspicious words and phrases. The researchers also proposed a defense mechanism – flagging potentially dangerous prompts by analyzing their attention distribution. If a prompt displays the telltale signs of a jailbreak attempt, such as high entropy (indicating a scattered focus) and high dependency (suggesting a strong link between deceptive prompt and harmful response), the LLM could be alerted, potentially preventing the exploit. This research highlights the ongoing challenge of ensuring AI safety. While LLMs are trained to be helpful and harmless, their complex inner workings can be exploited. The development of effective defense strategies will become increasingly vital as LLMs become more integrated into our daily lives.