ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents
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, ToolPrivacyBench audits whether task-private atoms are routed only to authorized tools and downstream sinks, thereby evaluating both task completion and privac...
| Authors | Shijing Hu, Liang Liu, Zhu Meng et al. |
|---|---|
| Primary category | cs.CR |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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