Nah, it sounds like a simple latent space adjacency problem. When you give an image generation model the "swimming" keyword, that makes it much more likely to produce bathing suits and bikinis, and once it is already generating that much bare skin it is likely to keep going and make some bare breasts (if many images of bare breasts are trained into the model). Their claimed prompt of "swimming with the sharks" also introduces an element of danger and excitement, which some models could also use as a signal to introduce risqué elements. Basically, if models produced only what you asked for they would produce extraordinarily boring results. Many models come up with additional elements to add to the image based on the adjacent elements encoded in the latent space branching off of your prompt.

The main issue here would be their prompt choice ("swimming with the sharks" was a slightly risky prompt. It could have easily gone another way and generated a gory Jaws-inspired image of her getting eaten by a shark). Secondary as an issue is the model that they used (likely a model that has too many NSFW images trained in). The big lesson for corporate entities is to pay attention to the training data that was used in the model (if you want purely SFW results you have to use a model that has nothing NSFW trained in) and also carefully consider what imagery your keywords is likely to produce as a side effect.