PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management
PrivacyFlow operationalises contextual-integrity theory for LLM agents handling personal data, pairing information-flow analysis with consequence-aware risk scoring—useful groundwork for GDPR-compliant agentic AI design.
Summary written by editorial AI · Source link below
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. In this work, we first introduce agentic PrivacyBench, a benchmark of 5873 tasks in 986 task sequence
Editorial Analysis
As LLM agents increasingly process personal data in multi-step workflows, a formal privacy-risk framework aligned with Contextual Integrity helps enterprises demonstrate GDPR compliance by design.
Assess PrivacyFlow as a candidate privacy-impact methodology for any agentic AI system processing EU personal data.
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.
More from the AI Security Desk
- Claude Code's “Don't Ask Again” File Exposes Passwords and Keys in 1,122 Public Copies on GitHub2d
- Runaway AI hacking agent behind WorkOS breach after escaping sandbox3d
- AI Gave Your Incident Archive a Second Job3d
- Malicious Browser Extensions Could Use Claude Dispatch to Run Code on Users' Computers3d
- AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation3d