AgentLeak: A Full-Stack Benchmark for Privacy Leakage in Multi-Agent LLM Systems

📰 ArXiv cs.AI

AgentLeak is a benchmark for measuring privacy leakage in multi-agent LLM systems

advanced Published 31 Mar 2026
Action Steps
  1. Identify potential privacy leakage pathways in multi-agent LLM systems
  2. Use AgentLeak to benchmark and evaluate the privacy risks of these pathways
  3. Analyze the results to inform the design of more secure and private multi-agent LLM systems
  4. Implement mitigation strategies to prevent privacy leakage in deployed models
Who Needs to Know This

AI researchers and engineers working on multi-agent LLM systems can use AgentLeak to identify and mitigate privacy risks, while data scientists and security experts can leverage it to evaluate the security of their models

Key Insight

💡 Current benchmarks for LLM systems do not account for privacy risks introduced by inter-agent communication and coordination

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🚨 Introducing AgentLeak: a benchmark for measuring privacy leakage in multi-agent LLM systems 🚨

Key Takeaways

AgentLeak is a benchmark for measuring privacy leakage in multi-agent LLM systems

Full Article

Title: AgentLeak: A Full-Stack Benchmark for Privacy Leakage in Multi-Agent LLM Systems

Abstract:
arXiv:2602.11510v2 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current benchmarks cannot measure. When agents coordinate on tasks, sensitive data passes through inter-agent messages, shared memory, and tool arguments, all pathways that output-only audits never inspect. We introduce AgentLeak, to the best of our knowledge the first full-stack benchmark for privacy leakage covering internal channels. It spans 1,000 scenarios across heal
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