Systemic privacy risks of personal data exposure through conversational large language model agents

Abstract Conversational Large Language Model (LLM) agents, deployed quickly, have exposed unprecedented opportunities for personal information, from health data to financial information, to biometric identifiers. This critical enlightenment review investigates general privacy dangers in each of five areas: data leakage and memorization, adversarial extraction and prompt injection, inference and re-identification, surveillance and profiling and regulatory governance breakdown. We trace privacy threats from static data breaches to dynamic ones that have become part of an architecture, associated with neural memorization and contextual inference (2020–2026), using expert selection of primary studies and technical benchmark sources. Upon close examination, technical mitigations (differential privacy, federated learning and machine unlearning) prove successful to some extent when operating in controlled environments, but fail to provide complete protection in conversational environments. We combine classical theory on Privacy-Preserving Data Publishing (PPDP) with emerging threats from LLMs, and present a conflict-of-evidence analysis which reveals that privacy protections are particularly effective in structured environments with low risks, or with consumers actively responsible for maintaining them, but fail to adequately protect privacy in high-stakes domains without human control, technical safeguards, and legal liability. We systematically examine and analyze a dimension that has not been studied sufficiently: risks of data deletion and editing illusion, and introduce techniques for unlearning machines that leave behind traces that can be exploited by adversarial reconstruction. We support the multi-layered mitigation framework of PPDP principles, technical safeguards, and governance reform.