Imagine a seemingly harmless conversation with an AI chatbot. Beneath the surface, however, lurks a hidden vulnerability: the backdoor. New research reveals how these multi-turn chatbots can be manipulated through distributed backdoor triggers, turning helpful assistants into malicious actors. Researchers discovered that by inserting specific trigger words or phrases across multiple turns of conversation, they could activate a backdoor, forcing the chatbot to refuse any further assistance. What makes this attack particularly insidious is that the triggers are only effective when presented together – a single trigger goes unnoticed. This means malicious actors could potentially inject these triggers into training data or even directly into user prompts, turning helpful chatbots into tools for spreading disinformation or denying service. The researchers explored several trigger types, including rare words, gradient-based triggers, and entity-based triggers like names. They found alarmingly high success rates, sometimes exceeding 99% with minimal data poisoning. This raises serious questions about the security of our increasingly AI-driven world. Defending against this type of attack is particularly challenging due to the complexity of multi-turn conversations. Traditional defense methods struggle to identify these distributed triggers. The researchers propose a new defense method called "Decayed Contrastive Decoding." This innovative approach compares the chatbot's output to its own internal representations, helping it avoid generating malicious responses. While this method shows promise, it also slightly impacts the quality of the generated text, highlighting the delicate balance between security and performance. The research underscores the urgent need for improved security measures to safeguard against these hidden threats in AI chatbots. As AI continues to integrate deeper into our daily lives, protecting these systems from manipulation becomes paramount to ensuring a safe and trustworthy digital future.