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

Summary

This critical review examines privacy risks from conversational LLM agents across five areas: data leakage and memorization, adversarial extraction and prompt injection, inference and re-identification, surveillance and profiling, and regulatory governance breakdown. It finds that technical mitigations such as differential privacy, federated learning and machine unlearning work to some extent in controlled settings but fail to fully protect conversational deployments, and it highlights risks that machine unlearning can leave traces exploitable by adversarial reconstruction.

Mitigation

The authors support a multi-layered mitigation framework combining Privacy-Preserving Data Publishing (PPDP) principles, technical safeguards, and governance reform.

Topics

• Training data and privacy

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