Driven by Large Language Models, the single-agent, multi-tool architecture has become a popular paradigm for autonomous agents. However, this architecture introduces a severe privacy risk, which we term Tools Orchestration Privacy Risk (TOP-R): an agent, to achieve a benign user goal, autonomously aggregates non-sensitive fragments from multiple tools and synthesizes unexpected sensitive information. We provide the first systematic study of this risk. We establish a formal framework characterizing TOP-R through three necessary conditions -- conclusion sensitivity, single-source non-inferability, and compositional inferability. We construct TOP-Bench via a Reverse Inference Seed Expansion (RISE) pipeline, incorporating paired social-context scenarios for diagnostic analysis. We further introduce the H-Score, a harmonic mean of task completion and safety, to quantify the utility-safety trade-off. Evaluation of six state-of-the-art LLMs reveals pervasive risk: the average Overall Leakage Rate reaches 62.11% with an H-Score of only 52.90%. Our experiments identify three root causes: deficient spontaneous privacy awareness, reasoning overshoot, and inference inertia. Guided by these findings, we propose three complementary mitigation strategies targeting the output, reasoning, and review stages of the agent pipeline; the strongest configuration, Dual-Constraint Privacy Enhancement, achieves an H-Score of 79.20%. Our work reveals a new risk class in tool-using agents, analyzes leakage causes, and provides practical mitigation strategies.

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