Biometric security improves shared memory in multi-user AI systems
BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents
Artificial Intelligence
Summary
Large language models (LLMs) use a type of memory called KV cache to remember information during conversations. When many users share the same AI, it becomes hard to keep their data private because the memory might reuse information from others. The authors propose Bio-MemArt, which attaches fingerprints like face or palmprint data to each memory block. This lets the system check who is asking before giving access, protecting users' information while keeping the AI efficient. Their tests show strong accuracy in recognizing rightful owners and blocking others.
KV cachebiometric authenticationlarge language modelsmemory managementaccess controlmulti-user systemsface recognitionpalmprint recognitiontoken efficiencylatent-space retrieval
Authors
Yanhong Qian, Xuanying He, Qingguo Meng, Shihao Ding, Xingbo Dong, Zhe Jin
Abstract
KV cache is evolving from a serving optimization into an external memory substrate for long-term LLM agents. In a shared multi-user deployment, however, reusable KV blocks introduce a missing access-control question: semantic relevance alone cannot determine whether a memory block is authorized for the current physical user. We propose Bio-MemArt, a biometric-aware KV-cache memory framework for multi-user LLM agents. Bio-MemArt attaches a normalized biometric template to each stored KV memory block, filters the shared memory pool with the current user's biometric probe, and then runs the original MemArt retrieval and KV reuse pipeline only inside the authorized candidate pool. This design preserves latent-space retrieval, direct cache reuse, and decoupled position encoding while adding physical-user access control to shared KV memory. We evaluate Bio-MemArt under Owner and Non-owner query conditions on long-term dialogue QA with face and palmprint benchmarks. Across face benchmarks, the average owner and non-owner biometric success rates are 95.71% and 0.86%; across palmprint benchmarks, they are 97.60% and 2.00%. In the efficiency study, average prefill tokens drop from 18,781.96 under full-context prompting to 28.57 with Bio-MemArt, showing that biometric gating preserves the low-token operating regime of KV-cache memory.