GANs

Photos are annoying data. They are useful because they contain details: the handle of a bag, the edge of a sleeve, the texture of a face, the faint classroom gesture that matters only after someone trains a model on it. They are risky for exactly the same reason. If a generated image looks too much like the real training data, it may quietly leak what the organization was trying not to reveal. If it is protected too aggressively, it becomes a blurry souvenir from a dataset that used to be useful. ...

TL;DR for operators CAVGAN is not another “clever jailbreak prompt” paper. Its real claim is more uncomfortable: jailbreaks and defenses may both be expressions of the same internal boundary inside an LLM. If malicious and benign requests occupy separable regions in hidden-state space, then an attacker can try to push a harmful request into the “safe-looking” region. A defender can also monitor that same space and intervene before the model answers. Convenient. Also slightly rude. ...