本地化记忆系统防御记忆投毒，无需云端依赖。
SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
- 基于本地存储与贝叶斯信任评分，实现抗投毒的分布式记忆架构。
- 搜索延迟仅10.6ms，启用自适应重排序后NDCG@5提升104%。
- 适合注重隐私与安全的开发团队，支持GDPR数据删除。
我们提出SuperLocalMemory，一种面向多智能体AI的本地优先记忆系统，通过架构隔离与贝叶斯信任评分防御OWASP ASI06记忆投毒威胁，同时通过自适应学习排序实现个性化检索，全程无需云端依赖或大模型推理调用。随着AI代理日益依赖持久记忆，云存储系统易形成集中式攻击面，导致中毒记忆跨会话、跨用户传播——已有生产系统遭遇此类攻击。本系统结合SQLite后端存储与FTS5全文检索、基于Leiden算法的知识图谱聚类、事件驱动的代理溯源协调层，以及三层行为分析（跨项目技术偏好、项目上下文识别、工作流模式挖掘）的自适应重排序框架。在七项基准测试中，系统平均搜索延迟为10.6ms，10个并发智能体下零并发错误，信任隔离差距达0.90，对潜伏攻击的信任度下降72%，启用自适应重排序后NDCG@5提升104%。行为数据独立存储于专用数据库，支持GDPR第17条擦除。系统开源（MIT），可通过Model Context Protocol集成17+开发工具。
原文摘要 · Abstract (English)
We present SuperLocalMemory, a local-first memory system for multi-agent AI that defends against OWASP ASI06 memory poisoning through architectural isolation and Bayesian trust scoring, while personalizing retrieval through adaptive learning-to-rank -- all without cloud dependencies or LLM inference calls. As AI agents increasingly rely on persistent memory, cloud-based memory systems create centralized attack surfaces where poisoned memories propagate across sessions and users -- a threat demonstrated in documented attacks against production systems. Our architecture combines SQLite-backed storage with FTS5 full-text search, Leiden-based knowledge graph clustering, an event-driven coordination layer with per-agent provenance, and an adaptive re-ranking framework that learns user preferences through three-layer behavioral analysis (cross-project technology preferences, project context detection, and workflow pattern mining). Evaluation across seven benchmark dimensions demonstrates 10.6ms median search latency, zero concurrency errors under 10 simultaneous agents, trust separation (gap =0.90) with 72% trust degradation for sleeper attacks, and 104% improvement in NDCG@5 when adaptive re-ranking is enabled. Behavioral data is isolated in a separate database with GDPR Article 17 erasure support. SuperLocalMemory is open-source (MIT) and integrates with 17+ development tools via Model Context Protocol.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。