SECSEC-001 — Model Security Vulnerabilities
AI Model Theft and Tampering Risks Undermine Decision Integrity
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Core model parameters and structures are vulnerable to inversion attacks, theft, and backdoor injection, compromising inference reliability and exposing proprietary assets. Boards face dual exposure: intellectual property loss and liability for erroneous automated decisions affecting regulated outputs.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — AI Safety Governance Framework (TC2602024) ↗https://airisk.mit.edu/
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