Matching Ranks Over Probability Yields Truly Deep Safety Alignment

תקציר מקורי באנגליתarXiv:2512.05518v2 Announce Type: replace-cross Abstract: Open-source Large Language Models (LLMs) play a critical role in the democratization of AI, yet their "open" nature introduces more avenues for malicious actors to misuse them for harmful purposes. A frustratingly easy but powerful technique known as the prefilling attack has been shown to effectively circumvent the safety alignment of frontier open-source LLMs by simply prefilling the assistant response with an affirmative prefix before decoding. A recent promising supervised fine-tuning defense proposes using a simple data augmentation scheme to achieve a "deep" safety alignment, allowing the model to generate natural language refusals immediately following harmful prefills. In this work, we show that a simple generalization of th