SECSEC-001 — Model Security Vulnerabilities
Prompt Injection Attacks Enable Remote Compromise of LLM-Integrated Systems
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Adversaries can hijack large language models via injected instructions hidden in retrieved data, enabling remote control, data theft, and denial of service without direct system access. Firms deploying AI assistants with plugin or internet access face material security liability absent rigorous input validation and runtime controls.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024) ↗https://airisk.mit.edu/
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