arXiv cs.AI· Vicen\c{c} Torra, Maria Bras-Amor\'os·· 10 小时前AI 评分31
面向安全多智能体系统的私有知识检索与私有推理
On Private Knowledge Retrieval and Private Inference for secure multi-agent systems
AI 导读
针对多智能体系统中语义记忆易遭记忆投毒攻击的问题，研究者提出私有知识检索与私有推理机制，在知识库中引入签名系统，从设计层面阻止知识被恶意篡改。智能体内置类 Prolog 的本地推理系统（用 Horn 子句表示知识），通过私有方式访问外部已签名知识库。论文给出基于 Python 的多种实现，覆盖 k-匿名与信息论隐私（IT-PIR）等不同隐私级别。
正文 · 原文
Abstract:Multi-agent systems provide a convenient framework for the massive deployment of agents based on Large Language Models (LLMs). It provides a sound framework where agents can interact with existing software to perform actions in an autonomous way. Agentic AI is the new term to denote these AI-based autonomous systems.
In real multi-agent systems, agents need to deal with malicious agents some of which can exploit security vulnerabilities to do harm. A memory poisoning attack is an example of exploiting a vulnerability to modify agents knowledge and, thus, affect its behavior. In an agent, there are different types of memory systems (semantic, episodic, and short-term) which have their own characteristics and for which different memory poisoning attacks may be developed.
In this paper we focus on semantic memory, we discuss its attack, and mitigation strategies. We introduce private knowledge retrieval and private inference. We propose a signature system in the knowledge base to prevent from undesired manipulation. This should avoid memory poisoning attacks by introducing security by design in the construction of the semantic memory. In our architecture, an agent contains a local Prolog-like inference system (i.e., using Horn clauses to represent knowledge) which accesses an external knowledge base to get the required knowledge. This externally signed knowledge base is accessed privately. We provide different implementations in Python using different privacy levels including k-anonymity and information-theoretical privacy (IT-PIR).
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.11 |
| Cite as: | arXiv:2611.13696 [cs.CR] |
| (or arXiv:2611.13696v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13696 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vicenc Torra [view email]
[v1]
Tue, 10 Nov 2026 07:43:02 UTC (19 KB)
来源：arXiv cs.AI · arxiv.org