Large language models (LLMs) are becoming increasingly reliant on external knowledge sources through a technique called Retrieval-Augmented Generation (RAG). Imagine an LLM like ChatGPT having access to a vast, constantly updated library to answer your questions. That's RAG in a nutshell. While incredibly powerful, this reliance opens up a dangerous vulnerability: RAG poisoning. This is where malicious actors inject false information into the knowledge base, effectively tainting the well of information the LLM draws from. The result? The LLM unknowingly provides incorrect or even harmful answers, all while sounding perfectly confident. Researchers have developed a clever method to detect these poisoning attacks by examining the LLM's internal workings, called 'activations.' Think of activations as the LLM's thought process, revealing how it processes information. The research reveals distinct patterns in these activations when the LLM generates a correct answer versus a poisoned one. This method, named RevPRAG, is remarkably effective, achieving over 98% accuracy in detecting poisoned responses across various LLM architectures and datasets. It works by comparing the LLM's activations when processing clean information versus tainted data, spotting the subtle differences that indicate a poisoned response. This research is crucial for ensuring the safety and reliability of LLMs as they become more integrated into our daily lives. Identifying and mitigating these vulnerabilities will be key to building trust and preventing the spread of misinformation in the age of AI.