SECSEC-001 — Model Security Vulnerabilities
Poor Data Quality Controls Undermine AI Performance and Safety Claims
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Absent standardised data collection controls expose AI systems to dataset poisoning, copyright infringement, and benchmark contamination that invalidate published performance metrics. Boards relying on vendor capability claims face material risk of deploying systems whose actual performance is unverified and legally encumbered.
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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