SECSEC-001 — Model Security Vulnerabilities
Training Data Poisoning Causes Systematic Misclassification in AI Models
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Adversaries manipulate training data to embed misbehaviours that cause AI models to misclassify inputs at inference time. Organisations deploying classification models face silent, persistent integrity failures that standard testing may not detect.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024) ↗https://airisk.mit.edu/
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