Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation

ArXi:2505.18323v2 Announce Type: replace-cross For nearly a decade the academic community has investigated backdoors in neural networks, primarily focusing on classification tasks where adversaries manipulate the model prediction. While nstrably malicious, the immediate real-world impact of such prediction-altering attacks has remained unclear. In this paper we