DATDAT-002 — Data Privacy and Protection Failures
LLMs Exploited to Poison Medical Knowledge Graphs with Fabricated Literature
3/5Sector: HealthcareGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can be manipulated to generate false medical literature that corrupts biomedical knowledge graphs, compromising the integrity of clinical and research AI systems. Boards face liability exposure and regulatory scrutiny where corrupted knowledge propagates into patient-facing diagnostic or treatment tools.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy (Tang2025) ↗https://airisk.mit.edu/
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