arXiv:2602.11510v3 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure. When agents coordinate on tasks, sensitive data may pass through inter-agent messages, shared memory, and tool arguments, all pathways that final-output audits typically do not inspect. We introduce AgentLeak, a benchmark for evaluating internal-channel privacy leakage in multi-agent LLM systems. AgentLeak instruments seven privacy-relevant communication pathways and provides a large-scale empirical evaluation focused on fin
The rapid advancement and deployment of multi-agent LLM systems are surfacing novel and complex privacy challenges that require new evaluation methods beyond typical output audits.
This benchmark highlights a critical, previously unaddressed vulnerability in emerging AI systems, which could undermine trust, expose sensitive data, and necessitate significant re-engineering for secure deployment.
The focus for evaluating privacy in multi-agent LLM systems shifts from merely observing final outputs to rigorously inspecting internal communication pathways and inter-agent data flows.
- · AI security researchers
- · Privacy-focused AI developers
- · Cybersecurity firms specializing in AI
- · AI developers ignoring internal privacy risks
- · Organizations deploying unchecked multi-agent systems
- · Users whose data is exposed
Increased scrutiny and demand for privacy-preserving architectures in multi-agent LLM systems.
Development of new regulatory guidelines and compliance standards specifically addressing inter-agent data handling.
A potential slowdown in the adoption of complex multi-agent systems until robust privacy solutions are integrated and verified.
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Read at arXiv cs.AI