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PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management — arXiv:2610.40714

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40714
License (as archived): CC0-1.0
Archived: 2026-11-03T07:32:27.549Z
SHA-256 of the archived text: f25e1a4f26b474e413c712712ce0f88aeb7878efc4ab5283d173acdaf5550b5c
Stable URL of this page: https://metraipolis.com/documento/dc_cfab20bc0bbc

How to cite

PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management — arXiv:2610.40714 — arXiv (en), https://arxiv.org/abs/2610.40714 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cfab20bc0bbc (Metraipolis archive dc_cfab20bc0bbc, sha256 f25e1a4f26b474e413c712712ce0f88aeb7878efc4ab5283d173acdaf5550b5c)

BibTeX

@misc{dccfab20bc0bbc,
  title = {PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management — arXiv:2610.40714},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40714},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cfab20bc0bbc; sha256 f25e1a4f26b474e413c712712ce0f88aeb7878efc4ab5283d173acdaf5550b5c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40714. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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. In this work, we first introduce agentic PrivacyBench, a benchmark of 5873 tasks in 986 task sequences with dynamic environment updates and CI-grounded allow-sets for sensitive information. We also introduce PrivacyFlow, a defense that separates deliberative privacy planning (System 2) from task execution (System 1), treating each sharing decision as a structured risk-management problem. Before execution, PrivacyFlow traces the provenance and purpose of each planned data flow, models recipient background knowledge, and simulates what external actors could infer. This allows us to model the downstream consequences of disclosure, including inference attacks and context-dependent harms. It then weighs the remaining disclosure risk against task utility and executes a minimized plan, blocks it, or consults the user. Across six models, PrivacyFlow significantly reduces step-level privacy failures and raises the share of completed task sequences without a detected privacy violation by 22--55 percentage points. We further highlight a new privacy--utility--autonomy tradeoff: allowing PrivacyFlow to proactively prompt the user in case of uncertainty can increase authorized task completion at the cost of shifting decision effort back to the user. Our results suggest that proactive CI reasoning can substantially improve agentic privacy and motivate further study of this tradeoff.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.40714 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:29:33.671Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).