SECSEC-001 — Model Security Vulnerabilities
Data Poisoning Attacks Corrupt Generative AI Training Datasets
4/5Sector: TransportGeography: GlobalStage: DevelopIngested: —
Executive Summary
Malicious actors can embed invisible corruptions into publicly scraped training data, causing AI models to produce systematically wrong outputs. Transport operators relying on AI trained on open datasets face material safety and liability exposure if model integrity is not verified before deployment.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data (Marchal2024) ↗https://airisk.mit.edu/
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