Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker's final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, attacker-target placements, and base models. Results are consistent: denser connectivity, shorter attacker-target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker-target separation, and restrict hub/shortcut pathways via topology-aware access control. Our code is available at https://github.com/llll121/mama-eval.

翻译：图拓扑是多智能体大语言模型系统中内存泄露的基本决定因素，但其影响尚未得到有效量化。我们提出MAMA（多智能体内存攻击），一种用于比较多智能体大语言模型中拓扑条件性内存泄露的受控评估框架。MAMA基于包含标记个人可识别信息（PII）实体的合成文档运行，从中生成消杀后的任务指令。我们执行两阶段协议：Engram（将隐私信息播种到目标智能体的内存中）和Resonance（多轮交互中攻击者尝试提取）。经过10轮交互，我们使用两阶段恢复准则测量泄露，该准则结合精确匹配提取与基于大语言模型对攻击者最终输出的推理。我们评估了六种典型拓扑（完全图、环形图、链式图、树形图、星形图、星-环图），涵盖n∈{4,5,6}、攻击者-目标放置位置及基础模型。结果具有一致性：更密集的连接性、更短的攻击者-目标距离以及更高的目标中心性会增加泄露；大部分泄露发生在早期轮次，随后趋于平稳；模型选择会改变绝对比率但保留广泛的结构趋势；时空/位置属性比身份凭证或受监管标识符更容易泄露。我们提炼出系统设计的实用指南：偏好稀疏或层次化连接性，最大化攻击者-目标间距，并通过拓扑感知的访问控制限制枢纽/捷径路径。我们的代码可在https://github.com/llll121/mama-eval获取。