SECSEC-001 — Model Security Vulnerabilities
Adversarial Input Attacks Exploit AI Model Weaknesses at Inference
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI models can be deliberately deceived by crafted inputs that exploit flawed correlations learned during training, causing unintended outputs across system architectures. Boards face material liability where such vulnerabilities are not disclosed or mitigated within AI governance frameworks.
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/
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