AI safety is a growing concern, especially with the rise of powerful large language models (LLMs). While these models are designed to be helpful and harmless, they can sometimes generate toxic or inappropriate content. Researchers are constantly working on ways to mitigate these risks, and a new study introduces a clever, cost-effective approach to toxicity detection. The traditional method involves using a separate toxicity detection model, which adds computational overhead and latency. This new research, however, proposes a method called "Moderation Using LLM Introspection" (MULI), which leverages information *within the LLM itself* to detect toxicity. The key insight is that even when an LLM *does* respond to a toxic prompt, there are subtle clues hidden within its output. Specifically, the researchers found a significant difference in the probability distribution of the *very first token* the LLM generates. By analyzing the logits (the raw output of the model before it selects a word) for this first token, they can accurately predict whether the prompt is toxic. This method is not only more accurate than existing toxicity detectors but also virtually free, as it doesn't require running a separate model. The researchers used a sparse logistic regression model trained on these initial token logits, achieving impressive results. For example, on the ToxicChat dataset, MULI achieved a 42.54% true positive rate at a very low false positive rate of 0.1%. This means it can catch a substantial portion of toxic prompts while minimizing false alarms. The effectiveness of MULI is linked to the strength of the LLM's safety alignment. The better the LLM is at refusing toxic prompts in the first place, the more effective MULI becomes. This suggests that improvements in LLM safety alignment will have a positive ripple effect on toxicity detection. While MULI shows great promise, it's important to acknowledge its limitations. It relies on well-aligned models and hasn't been extensively tested against adversarial attacks. However, this research opens up exciting new avenues for building safer and more responsible AI systems. By looking deeper into the inner workings of LLMs, we can develop more effective and efficient ways to detect and prevent harmful content.