arXiv:2605.30727v1 Announce Type: new Abstract: Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent's external queries may leak sensitive information from its local context. This risk is amplified by the mosaic effect, where individual queries may appear harmless but become revealing in aggregate. We introduce MosaicLeaks, a benchmark of 1,001 multi-hop deep research tasks that chain private enterprise documents and a public web corpus, forcing agents to make external queries that depend on local informatio
The proliferation of advanced AI agents capable of querying external tools and integrating private data makes privacy implications increasingly salient and urgent to address.
This research highlights a critical privacy vulnerability in autonomous AI systems, which can inadvertently leak sensitive information from private contexts through seemingly innocuous external queries.
The understanding of AI agent security shifts from isolated data handling to recognizing the 'mosaic effect' in external query patterns, demanding new privacy-preserving architectures and protocols.
- · AI security researchers
- · Privacy-preserving AI startups
- · Enterprises prioritizing data security
- · Auditing and compliance platforms
- · Developers of insecure AI agents
- · Organizations with lax data governance
- · Users with sensitive personal data
- · Cloud providers without strong isolation
Companies will need to invest significantly in privacy-preserving AI development and robust data leakage prevention for agentic systems.
New regulatory frameworks and compliance standards specific to autonomous agent data privacy will likely emerge globally.
The trade-off between agent utility (access to external tools) and internal data privacy could become a design constraint, potentially slowing agent deployment in highly sensitive domains.
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