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【论文速递】ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender

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华人空间新闻 新手上路 周五 10月 30, 2026 8:00 pm #1
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英文标题:ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender arXiv编号:2610.40627 作者:Qiyao Ma, Xiaodong Liu, Weiwei Yang 来源:arXiv 原文链…
【论文速递】ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender

英文标题:ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender

arXiv编号:2610.40627
作者:Qiyao Ma, Xiaodong Liu, Weiwei Yang
来源:arXiv
原文链接:https://arxiv.org/abs/2610.40627

中文摘要:
Safety alignment has enabled large language models to reliably refuse harmful requests directly, yet the same request distributed across a multi-turn conversation still succeeds far too often. This brittleness has a mechanistic root: Alignment is shallow, with safety behavior concentrated in a model's first few output tokens, and existing defenses inherit the weakness, supervising the surface form of the response without ever giving the model an explicit safety decision to learn. We take the opposite view of shallowness: if a short span determines the trajectory of an entire response, that span is the highest-leverage place to intervene, and the defender can occupy it first. We propose Assess $\to$ Decide $\to$ Respond, a framework in which the defender assesses the user's intent, commits to an explicit, parseable safety decision, and only then generates a reply conditioned on it, with the commitment trained by reinforcement learning at the payload turn. Because the decision is a token span, the reward can be a rule: a self-verified reward checks the committed label against the conversation's known nature, requiring no judge and no annotation, while the same format also supports an LLM-as-judge reward over a graded decision space with safe redirection. Across two model families and fourteen benchmarks spanning safety, utility, and helpfulness, our defenders achieve the strongest multi-turn safety in a controlled comparison, maintaining their lead in safety while preserving utility at a modest cost to helpfulness. A per-token KL analysis and interventional probes confirm why this works: training concentrates its effect on the brief deliberation that sets a response's trajectory, and the decision committed there is a causal control point. The very shallowness attackers exploit becomes the mechanism of defense.

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