AnchorKV：基于拒绝锚点的软惩罚安全感知KV缓存压缩
AnchorKV: Safety-Aware KV Cache Compression via Soft Penalty with a Refusal Anchor
想压缩KV缓存又怕模型不安全？AnchorKV用软惩罚巧妙拒绝有害token，既省内存又防越狱，适合部署场景。
AnchorKV是一种即插即用的KV缓存压缩方法，通过构建离线安全锚点来偏置token保留分数，使其远离有害提示对应的键空间方向。
该方法采用差异均值表征工程技术，在逐层键投影空间中提取拒绝方向，并引入软惩罚token选择规则。在保持压缩效率的同时，AnchorKV能显著提升LLM的安全对齐能力，抵御越狱攻击。
实验表明，在适度牺牲少量性能的情况下，该方法可有效降低有害输出率。
AnchorKV: Safety-Aware KV Cache Compression via Soft Penalty with a Refusal Anchor
Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their large size introduces significant challenges in memory usage, energy cost, and on-device deployment. Since scaling pre-trained language models improves downstream capability \cite{zhao2023survey}, the key-value (KV) cache becomes a dominant inference bottleneck. Recent KV cache compression methods \cite{jo2025fastkv,li2024snapkv,zhou2024dynamickv} reduce this cost by retaining only a subset of attention-relevant tokens. However, while these approaches preserve accuracy on benign workloads, their compression policies either fail to defend against jailbreak attacks \cite{jiang2024robustkv} or degrade safety alignment under aggressive eviction. We propose AnchorKV, a drop-in modification to KV cache compression that biases token retention scores away from directions in key space associated with harmful prompts. AnchorKV constructs an offline safety anchor by adapting a difference-of-means representation engineering approach \cite{arditi2024refusal,zou2023representation} to the layer-specific key projection space used in KV caching. Based on this anchor, a soft penalty token selection rule trades a small amount of utility for substantially improved safety alignment, while reducing to the original compressor when the penalty is zero.
This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include AnchorKV, KV缓存压缩, 越狱攻击, 安全对齐.
Source attribution: curated from arXiv cs.LG (@Ning Ni, Yingjie Lao).
