Large language models (LLMs) are impressive, but they can also produce harmful, untruthful, or biased content. This poses a serious challenge, especially as AI integrates deeper into our lives. How can we ensure these powerful tools are used responsibly? Researchers have developed a novel technique called LLMScan, a kind of 'lie detector' for AI. Instead of just looking at the output, LLMScan examines the inner workings of the LLM, like analyzing brain activity to understand behavior. It focuses on two key areas: the influence of individual input words and the contribution of different layers within the model’s neural network. By analyzing these 'causal' relationships, LLMScan can detect misbehavior early in the generation process, sometimes even before a full sentence is formed. This allows for rapid identification and potential mitigation of harmful outputs. Tests across various LLMs and datasets show LLMScan's effectiveness in detecting lies, toxic language, and attempts to 'jailbreak' the model's safety restrictions. It performs exceptionally well, with accuracy often exceeding 95%. While it’s less effective at detecting bias, which is often subtly ingrained within the model's training data, the method offers a significant advancement. This approach is not only accurate but also efficient, adding only a small overhead to the generation process. LLMScan provides a crucial step towards building more trustworthy and responsible AI systems. It opens up new possibilities for understanding how LLMs function and offers a proactive way to address the challenges of AI misbehavior. As LLMs become more powerful, techniques like LLMScan will be essential for ensuring they remain beneficial and safe.