SECSEC-002 — Data Poisoning Attack Risks
LLMs Accelerating Dual-Use AI Development at Scale
5/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can autonomously build new AI systems and adapt existing ones for high-risk applications, compressing development timelines. Boards face material liability exposure as dual-use capability proliferation outpaces regulatory oversight and internal governance controls.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Cataloguing LLM Evaluations (InfoComm2023) ↗https://airisk.mit.edu/
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