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PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduce PrivDrift, a benchmark testing if LLMs leak user secrets across active conversations during topic drift.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 25 September 2026
- Collected by OneBench
- 26 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.30094v1 Announce Type: cross Abstract: Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings. When users disclose sensitive information during…
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OneBench interpretation
Institutional assessment
So what
Persistent conversational memory creates data leakage risks across unrelated topics in multi-turn enterprise assistant deployments.
Do what
Review session state handling and context retention controls with the team responsible for conversational AI security.