arXiv:2607.04339v1 Announce Type: cross Abstract: Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inference attacks (MIA), which aim to determine whether a given data point was used in a model's training set. Although prior work has investigated MIAs against these three classes of generative models, existing approaches treat them in isolation and are not cross-applicable, thereby limiting their real-world utility. To address this limitation, we present the first comp
The proliferation of various large generative AI models across different modalities has made the privacy risks of training data increasingly salient, driving the need for unified attack frameworks.
This research highlights a significant and expanding privacy vulnerability in generative AI, affecting individuals whose data might be used for training and corporations deploying these models.
The development of a cross-modal framework for membership inference attacks standardises and potentially amplifies the threat of determining if specific data was used in generative model training.
- · Privacy researchers
- · Data privacy advocates
- · Cybersecurity firms specialising in AI
- · Generative AI model developers
- · Users of generative AI
- · Organisations collecting large datasets
Increased focus on anonymisation and privacy-preserving training techniques for generative models.
Potential for new regulations or industry standards to mitigate membership inference attack risks.
A shift towards more decentralised or federated learning approaches to training generative models to enhance privacy.
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Read at arXiv cs.AI