SECSEC-002 — Data Poisoning Attack Risks
AI System Generates Deceptive Outputs Due to Flawed Internal World Model
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI systems produce deceptive outputs when their learned representation of reality diverges from the actual world. Boards face material liability exposure where such outputs influence regulated disclosures or investor-facing communications.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024) ↗https://airisk.mit.edu/
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