Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
Researchers formalise 'Tools Orchestration Privacy Risk'—where individually safe tool returns, combined by an LLM agent, disclose sensitive conclusions—and release a benchmark with mitigations.
Summary written by editorial AI · Source link below
arXiv:2512.16310v4 Announce Type: replace Abstract: LLM agents can combine individually non-revealing tool returns and disclose a sensitive conclusion, creating Tools Orchestration Privacy Risk (TOP-R). We formalize TOP-R through three conditions: conclusion sensitivity, single-source non-inferability, and compositional inferability. We introduce Library-Grounded Reverse-Inference Seed Expansion (LRSE), a four-library reverse-construction pipeline, and use it to build TOP-Bench, a 1,000-instanc
Editorial Analysis
Enterprises deploying agentic AI must consider that privacy risk can emerge from orchestration logic, not just individual data sources—a gap most current DPIAs miss.
Include cross-tool inference scenarios in privacy impact assessments for any LLM agent deployment.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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