SECSEC-001 — Model Security Vulnerabilities
Fine-tuning dataset poisoning enables covert manipulation of AI model behaviour
5/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Deployers can corrupt fine-tuning datasets to embed malicious behaviours into AI models without accessing model weights, making detection through standard dataset inspection unreliable. Organisations face undetected supply-chain compromise of licensed or third-party AI systems, exposing them to regulatory liability and operational risk.
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/
Could this happen in your organisation?
A Velinor AI Audit maps your active AI portfolio against the 50+ blindspots and benchmarks against documented sector failures like this one. A board-ready foresight document in 5 weeks.