SECSEC-001 — Model Security Vulnerabilities
Adversarial Instruction Attacks Bypass Large Language Model Safety Controls
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Researchers identified six categories of natural-language adversarial attacks capable of hijacking AI model goals and extracting hidden system prompts, bypassing built-in safety measures. Firms deploying large language models face material risk of reputational harm and regulatory censure if outputs are manipulated to produce unsafe or prohibited content.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023) ↗https://airisk.mit.edu/
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