arXiv:2606.28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments, and execute multi-step tasks. However, conventional function-calling benchmarks mainly evaluate task completion and API correctness, while privacy evaluation benchmarks typically focus on final responses or privacy judgments. Neither perspective captures purpose-bound information flow across an executed multi-tool trajectory. Motivated by this limitation in current agent evaluation, ToolPrivacyB
The rapid advancement of LLMs into agentic systems necessitates robust evaluation methods that account for complex, multi-tool interactions and the inherent privacy risks associated with data flow across these systems.
As AI agents become more autonomous and integrated into workflows, ensuring purpose-bound privacy is crucial for trust, regulatory compliance, and preventing unintended data leakage or misuse.
The explicit focus on benchmarking 'purpose-bound privacy' for tool-using LLM agents marks a significant evolution in AI evaluation, shifting beyond mere task completion to include crucial ethical and security dimensions.
- · AI ethics and safety researchers
- · Developers of privacy-preserving AI tools
- · Enterprises deploying AI agents
- · Regulatory bodies
- · AI developers ignoring privacy-by-design
- · Users vulnerable to data leakage
New benchmarks like ToolPrivacyBench will become standard requirements for agentic AI development and deployment.
Increased investment in privacy-enhancing technologies specifically for agent interactions and multi-tool orchestration will follow.
This focus on purpose-bound privacy may lead to the development of 'privacy-aware' AI agents that independently manage data access based on predefined purposes.
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Read at arXiv cs.AI