Transpose Attack: Stealing Datasets with Bidirectional Training
Guy Amit
Network and Distributed System Security (NDSS) Symposium 2024 · Day 3 · ML Security & Privacy
In an era where Artificial Intelligence (AI) underpins critical infrastructure and services, the integrity and confidentiality of training data for Deep Neural Networks (DNNs) have become paramount. This talk, "Transpose Attack: Stealing Datasets with Bidirectional Training," presented by Guy Amit at the NDSS Symposium, unveils a novel and highly concerning vulnerability within DNNs. The research demonstrates how an attacker can systematically and covertly exfiltrate complete, high-fidelity training datasets from protected environments, leveraging a previously overlooked aspect of neural network operation: their ability to be executed in reverse.