RESEARCHMonitorWATCHLIST
PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers introduced PrivacyFlow and PrivacyBench to evaluate and manage privacy leak risks in multi-step autonomous LLM agents.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 2 November 2026
- Collected by OneBench
- 3 Nov 2026, 02:02 UK
Stored source excerpt
arXiv:2610.40714v1 Announce Type: cross Abstract: Autonomous LLM agents executing multi-step workflows over personal data often misalign with user privacy expectations and leak sensitive, out-of-context information.…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.