PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management

Reported by arXiv cs.CR (Robin Staab, Tom Wollschl\"ager, Vahab Mirrokni, Reza Shokri)

Summary by Cyber Security News2 Nov 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.40714

arXiv:2610.40714v1 Announce Type: new Abstract: Autonomous LLM agents executing multi-step workflows over personal data often misalign with user privacy expectations and leak sensitive, out-of-context information. Although Contextual Integrity (CI) formalizes this problem, existing benchmarks and defenses rarely operationalize CI for realistic multi-step task sequences in evolving environments.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

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