arXiv:2606.09864v1 Announce Type: new Abstract: 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 1.03x perplexity, and no universal safe bit-width e
The proliferation of quantized LLMs for efficiency is highlighting the critical trade-offs between performance, resource consumption, and safety alignment, leading to closer scrutiny of established optimization techniques.
This research reveals that common LLM optimization methods can silently degrade safety alignment without significant performance dips, posing substantial risks for deployment in sensitive applications and eroding trust.
The previous assumption that perplexity and accuracy are sufficient metrics for evaluating LLM quantization is now challenged, necessitating comprehensive safety alignment evaluation for all quantum optimization techniques.
- · AI safety researchers
- · Hardware manufacturers with high-memory solutions
- · Developers prioritizing robust alignment
- · Developers solely focused on memory reduction
- · LLM providers with weak alignment guardrails
- · Applications deploying unvalidated quantized models
The adoption of KV cache quantization will slow down or require more rigorous testing protocols across the industry.
Increased investment in research to develop quantization techniques that provably preserve safety alignment will become a priority.
Regulatory bodies may begin to consider mandating specific safety evaluations for optimized AI models, impacting deployment timelines and costs.
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