AgentPrivArena Preprint Introduces Trajectory-Level Privacy Metrics for LLM Agents
arXiv preprint presents AgentPrivArena, a framework using authentic MCP tools and self-hosted services to evaluate privacy risks during multi-step LLM agent execution, plus…
- Existing benchmarks rely on simulated trajectories and final-response leakage, missing intermediate privacy violations during agent tool use.
- AgentPrivArena enables reproducible audits with real tools; AgentPrivAudit monitors runtime unnecessary data access.
- Experiments on state-of-the-art agents show substantial overlooked privacy risks, underscoring need for trajectory-level evaluation.
Limitations of Current Privacy Benchmarks
The preprint abstract notes that rapid growth of LLM agents, which use external tools for complex tasks, creates privacy exposure to personal data.
Most existing evaluation methods focus on simulated interaction paths and outcome metrics that only check final response leakage.
This approach fails to detect unnecessary information access that occurs across multiple execution steps in realistic agent workflows.
New Framework and Auditing Method
AgentPrivArena provides a reproducible environment that integrates authentic MCP tools and self-hosted services for more realistic testing.
It introduces trajectory-level privacy metrics designed to quantify data access beyond what is required for the final output.
Complementing the framework, AgentPrivAudit offers runtime monitoring to detect and audit privacy violations as agents operate.
Experimental Findings
Tests conducted on current leading LLM agents using the new framework uncovered notable privacy risks that prior simulated benchmarks had missed.
The results emphasize that trajectory-level auditing is essential for building trustworthy agent deployments.
As this is an arXiv preprint dated October 5 2026, the evidence is limited to the supplied abstract; full methodology, detailed results, and replication data are not yet available in this capture.
