arXiv:2512.04668v4 Announce Type: replace-cross Abstract: 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
The proliferation of multi-agent LLM systems for complex tasks makes understanding and mitigating PII leakage a critical and timely research area.
Sophisticated readers should care because effective deployment of multi-agent AI systems hinges on robust security and privacy, directly impacting trust and adoption in sensitive domains.
This research introduces a controlled framework to quantify a previously poorly understood vulnerability, enabling more secure and privacy-preserving multi-agent AI design.
- · AI developers
- · Cybersecurity firms
- · Privacy-focused AI companies
- · Enterprises adopting multi-agent LLMs
- · Unsecured multi-agent LLM platforms
- · Organizations with poor data governance
- · Users vulnerable to PII leakage
Increased focus on privacy-preserving architecture and security protocols for multi-agent AI systems.
Development of new tools and techniques for auditing and mitigating PII leakage risks in complex AI deployments.
Potential for regulatory changes or industry standards specifically addressing data privacy in multi-agent LLM environments.
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Read at arXiv cs.AI