arXiv:2602.04899v2 Announce Type: replace-cross Abstract: We present a data poisoning attack -- Phantom Transfer -- with the property that, even if you know precisely how the poison was placed into an otherwise benign dataset, you cannot filter it out. We achieve this by modifying subliminal learning to work in real-world contexts and demonstrate that the attack works regardless of which model produced the data, which model is trained on the data or what the attack target is. Furthermore, the attack survives 11 tested data-level defences, including one where every sample is paraphrased by anot
Source: arXiv cs.AI — read the full report at the original publisher.