Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

Key--value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measuring perplexity and accuracy without assessing the safety impact. In this study, we explore alignment preservation under KV cache quantization. Across eleven instruction-tuned models (3.8B--72B) and five benchmarks (1,894 prompts), we find that low-bit quantization can silently destroy safety alignment (Mistral-7B loses 15.2\% of its refusals at only $1.03\times$ perplexity, and no universal safe bit-width exists), with sharp model-specific phase transitions invisible to standard metrics. We identify that the root cause is geometric: safety features occupy a low-dimensional activation subspace $10^2$-$10^3\times$ more vulnerable to quantization noise than the full representation space perplexity averages over. Inspired by this observation, we propose **Per-Channel Reduction** (PCR), a diagnostic that classifies each model into one of three mechanistic failure modes: (i) *outlier-crushes-safety*, where safety lives in non-outlier channels collaterally damaged by outlier-driven scale factors; (ii) *outlier-as-safety*, where safety overlaps outlier channels and finer granularity cannot rescue it; (iii) *multi-layer dilution*, where safety is distributed across many layers and per-layer fixes fail. PCR predicts the correct mitigation direction on all nine primary models and one held-out model from an independent family (20 calibration prompts). PCR generalizes across unseen prompts, models, and production quantizers (KIVI: up to 97.2\% recovery), succeeding where attention-based allocation methods fail. The resulting training-free protocol (${\sim}35$ GPU-minutes) recovers up to 97\% of lost alignment at minimal memory overhead, addressing vulnerabilities confirmed in production vLLM serving with FP8 KV cache on NVIDIA GPUs.