New benchmark reveals significant privacy risks in multi-agent LLM systems

A new benchmark called AgentLeak has been developed to assess privacy risks in multi-agent Large Language Model (LLM) systems. Unlike previous benchmarks that only examined final outputs, AgentLeak analyzes internal communication channels between agents, such as inter-agent messages and shared memory. An evaluation using this benchmark across seven privacy-relevant pathways and 1,000 scenarios revealed that while multi-agent configurations can reduce leakage in final outputs, they introduce significant internal channel leakage, with inter-agent messages being a primary concern. The study highlights that standard output-only defenses are insufficient for securing multi-agent LLM systems. AI

IMPACT Highlights the need for new security measures beyond output monitoring for multi-agent LLM systems.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating LLM systems. [lever_c_demoted from research: ic=1 ai=1.0]

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• AgentLeak
• Claude 3.5 Sonnet
• Faouzi El Yagoubi
• GPT-4o
• GPT-4o mini
• large language model
• Llama 3.3-70B
• Mistral Large

• paper
• safety

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