Lightweight Security Measures for MLAS Communication

Develop lightweight security measures for Multi-LLM-Agent Systems that maintain inter-agent communication efficiency while preserving system security under dynamic, multi-agent interaction patterns.

Background

The paper analyzes the unique security vulnerabilities of Multi-LLM-Agent Systems (MLAS), including prompt injection, memory/data poisoning, and model extraction attacks that exploit inter-agent communication and distributed decision-making. Existing defenses such as input sanitization, perplexity-based filtering, and adversarially robust fine-tuning reduce risks but often introduce overhead or false positives due to complex interaction patterns.

In the future directions discussion, the authors emphasize the need to balance robustness and efficiency. Because comprehensive defenses can degrade communication performance, the development of lightweight, efficient security measures tailored to MLAS is highlighted as an unresolved challenge.