The Privacy Paradox of Agentic CrowdRE: when LLM Pipelines Harvest More Than You Intend

Abstract

Crowd-Based Requirements Engineering (CrowdRE) increasingly relies on multi-step large language model (LLM) pipelines to elicit, classify, and prioritize requirements from large volumes of user-generated content. Unlike prior batch natural-language-processing (NLP) tools, these agentic pipelines maintain persistent memory across sessions, invoke external tools, and pass intermediate representations through chains of model calls-each transition a potential exfiltration surface for contributor data. To our knowledge, no existing CrowdRE framework, RE methodology, or data-protection guidance document specifically addresses the class of privacy failures that emerges at the intersection of agentic AI architecture and crowd-based feedback pipelines. This problem statement characterizes four novel failure modes-cross-user memory bleed, implicit preference fingerprinting, tool-chain exfiltration, and synthetic crowd laundering-and presents an architectural threat model that exposes where each failure manifests in a representative agentic CrowdRE pipeline. We pose five open research questions and call on the community to treat contributor privacy as a first-class quality attribute before agentic CrowdRE becomes the production norm.