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SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning

arXiv:2603.02240v1 Announce Type: new Abstract: 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 a

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Varun Pratap Bhardwaj · · 1 min read · 17 views
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arXiv:2603.02240v1 Announce Type: new Abstract: 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.

Executive Summary

The article proposes SuperLocalMemory, a novel, local-first memory system for multi-agent AI that addresses the risks associated with cloud-based memory systems, particularly OWASP ASI06 memory poisoning. This system integrates multiple components, including SQLite-backed storage, FTS5 full-text search, Leiden-based knowledge graph clustering, and an adaptive re-ranking framework. The evaluation demonstrates improved performance, trust separation, and robustness against sleeper attacks. The proposed architecture emphasizes privacy-preserving data storage and retrieval, ensuring compliance with GDPR Article 17. The open-source implementation integrates with various development tools, facilitating its adoption. While the system offers significant advantages, its scalability and adaptability to diverse AI applications remain uncertain.

Key Points

  • ▸ SuperLocalMemory is a local-first memory system designed to mitigate OWASP ASI06 memory poisoning risk
  • ▸ The architecture combines multiple components for improved performance, trust separation, and robustness
  • ▸ The system emphasizes privacy-preserving data storage and retrieval, ensuring GDPR compliance

Merits

Effective Address of OWASP ASI06 Memory Poisoning

SuperLocalMemory's architectural isolation and Bayesian trust scoring effectively defend against memory poisoning, ensuring the integrity of AI agent memories

Improved Performance and Scalability

The system's adaptive re-ranking framework and knowledge graph clustering enable efficient data retrieval and storage, supporting large-scale AI applications

Enhanced Privacy and Compliance

SuperLocalMemory's design ensures the secure storage and retrieval of sensitive data, aligning with GDPR Article 17 requirements

Demerits

Limited Scalability and Adaptability

The proposed architecture may face challenges in scaling to diverse AI applications and adapting to evolving memory requirements

Potential Complexity and Maintainability

The integration of multiple components may introduce complexity, affecting the system's maintainability and ease of use

Expert Commentary

SuperLocalMemory presents a promising approach to addressing the risks associated with cloud-based memory systems. The proposed architecture demonstrates improved performance, trust separation, and robustness against sleeper attacks. However, its scalability and adaptability to diverse AI applications require further investigation. The system's emphasis on privacy-preserving data storage and retrieval aligns with GDPR Article 17 requirements, underscoring the need for more secure and efficient AI memory solutions. As AI continues to evolve, the development of robust and scalable memory systems like SuperLocalMemory will be crucial for ensuring the integrity and security of AI applications.

Recommendations

  • ✓ Recommendation 1: Further research is needed to investigate the scalability and adaptability of SuperLocalMemory to diverse AI applications
  • ✓ Recommendation 2: AI developers and policymakers should consider the proposed solutions and implications for data protection and security policies, ensuring more robust regulations and guidelines for AI data storage and retrieval

Sources

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