DATDAT-002 — Data Privacy and Protection Failures
LLM Training Data Exposed Through Targeted Privacy Attacks
5/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models memorise training data and can be induced to reveal private information, including raw data, model architecture, and hyperparameters, through adversarial queries. Organisations deploying LLMs face material data protection liability and reputational risk if proprietary or personal data entered training pipelines without adequate governance controls.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024) ↗https://airisk.mit.edu/
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