SECSEC-001 — Model Security Vulnerabilities
Training Data Poisoning Used to Jailbreak Large Language Models
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Adversaries can embed malicious content into LLM training data, causing models to bypass safety controls and produce harmful outputs. Organisations deploying third-party or open-source models face supply-chain integrity risks that existing governance frameworks do not adequately address.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025) ↗https://airisk.mit.edu/
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