SECSEC-001 — Model Security Vulnerabilities
Language Models Enabling Personalised Financial Fraud at Scale
5/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can generate convincing, tailored scam communications and impersonate known individuals by learning from personal data, significantly increasing fraud conversion rates. Boards face heightened exposure to customer harm claims, regulatory scrutiny, and reputational liability as AI-enabled fraud becomes harder to detect and attribute.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Ethical and social risks of harm from language models (Weidinger2021) ↗https://airisk.mit.edu/
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