DATDAT-001 — Data Quality and Completeness Issues
LLMs Provide Direct Unlawful Advice Beyond Search Engine Safeguards
5/5Sector: LegalGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models generate explicit, actionable guidance on illegal substances and dangerous activities, bypassing the intermediary friction that search engines impose. Organisations deploying LLMs face heightened liability exposure and reputational risk where outputs constitute direct facilitation of unlawful conduct.
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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