SECSEC-001 — Model Security Vulnerabilities
Adversarial Data Poisoning Corrupts AI Model Training
5/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Malicious actors or insiders inject false data into training sets, systematically compromising model integrity before deployment. Organisations face undetected decision errors, regulatory liability, and erosion of trust in AI-driven outputs.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — AI Risk Atlas (IBM2025) ↗https://airisk.mit.edu/
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