Large language models (LLMs) are constantly evolving, learning, and improving. But what happens when someone tries to deliberately "teach" them harmful or misleading information? A new research paper explores this critical issue, examining how malicious actors might exploit "knowledge editing" techniques to inject bad information into LLMs. Knowledge editing is typically used to correct errors or update information in an LLM. However, it can be misused to insert offensive content, misinformation, or biases. The researchers introduce a new task called "Knowledge Editing Type Identification" (KETI). Think of it like a lie detector for AI. KETI is designed to spot different types of edits, flagging potentially harmful changes. The researchers created a dataset of different edits, including misinformation, offensive language, and bias, and tested several methods for identifying these edits in both open-source and closed-source LLMs. The results are both promising and concerning. While the identification methods showed decent accuracy, they're not foolproof. This raises questions about the safety and security of LLMs, particularly as they become more integrated into our daily lives. One interesting finding is that the ability to detect bad edits isn't necessarily tied to how well the edits "stick" in the LLM. Even failed attempts at manipulation leave traces, and these traces can be used to identify malicious intent. The research also shows that detectors trained on one type of editing method can often spot edits made using other, unknown methods, offering hope for a more generalized approach to identifying harmful edits. The research also delves into *why* some detection methods work better than others. The key takeaway? The more information the detector has access to, the better it performs. This is especially true for closed-source models, where access is often limited. More data on how the LLM generates text, like the probabilities it assigns to different words, gives the detector a significant edge. While the current methods aren't perfect, this research lays the groundwork for more sophisticated detection techniques. Future work will likely explore more complex forms of knowledge and larger LLMs, further refining the tools needed to keep AI honest.