Small open-weight models match GPT-5 on routine agent tool-use tasks but lag on long-horizon planning, supporting tiered routing to reduce costs in agentic systems.
arXiv preprint arXiv:2505.04799 , year=
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abstract
Multi-agent collaboration systems (MACS), powered by large language models (LLMs), solve complex problems efficiently by leveraging each agent's specialization and communication between agents. However, the inherent exchange of information between agents and their interaction with external environments, such as LLM, tools, and users, inevitably introduces significant risks of sensitive data leakage, including vulnerabilities to attacks such as eavesdropping and prompt injection. Existing MACS lack fine-grained data protection controls, making it challenging to manage sensitive information securely. In this paper, we take the first step to mitigate the MACS's data leakage threat through a privacy-enhanced MACS development paradigm, Maris. Maris enables rigorous message flow control within MACS by embedding reference monitors into key multi-agent conversation components. We implemented Maris as an integral part of widely-adopted open-source multi-agent development frameworks, AutoGen and LangChain. To evaluate its effectiveness, we develop a Privacy Assessment Framework that emulates MACS under different threat scenarios. Our evaluation shows that Maris effectively mitigated sensitive data leakage threats across three different task suites while maintaining a high task success rate.
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Symbolic guardrails enforce about 74% of agent security and safety requirements on three benchmarks with mostly simple checks, improving safety without sacrificing utility.
PORTICO is a revocable capability reference monitor for coding agents that enforces task contracts via grant-invoke-closure lifecycles and rejects post-closure reuses while preserving task success.
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
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Small open-weight models match GPT-5 on routine agent tool-use tasks but lag on long-horizon planning, supporting tiered routing to reduce costs in agentic systems.
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