Imagine teaching AI to "unlearn" harmful behaviors, much like we try to break bad habits. That's the fascinating idea behind a new research paper exploring "cross-modal safety alignment" in AI models that process both text and images (like those cool AI image generators). These multimodal models are vulnerable to sneaky "jailbreak" attacks where harmful images or even cleverly disguised text within images can trick the AI into generating unsafe content. Traditional safety training methods struggle to keep up with these evolving attacks, so researchers are exploring a new approach: unlearning. Instead of just training the AI on what *not* to do, they're trying to make it actively "forget" the bad stuff. The surprising finding? "Textual unlearning," where the AI only unlearns from text examples, seems to work remarkably well, even protecting against image-based attacks! This is because these multimodal models often convert all input types into a text-like format internally. So, teaching the text-based part of the model to avoid harmful content seems to have a ripple effect, making the whole system safer. This is a big deal because collecting and labeling multimodal data for safety training is expensive and time-consuming. Textual unlearning could be a much more efficient way to make these powerful AI models safer and more aligned with human values. While this research is still in its early stages, it offers a promising new direction for building responsible AI. The next step is to explore how well this approach generalizes to other types of multimodal models, like those that process audio or video. The quest for truly safe and aligned AI continues!