SECSEC-001 — Model Security Vulnerabilities
Reverse Prompt Manipulation Extracts Prohibited Content from LLMs
4/5Sector: LegalGeography: GlobalStage: OperateIngested: —
Executive Summary
Attackers exploit sympathetic framing to cause large language models to produce illegal or harmful information they are designed to withhold. Organisations deploying LLMs face regulatory and reputational liability if safety controls can be circumvented through routine conversational misdirection.
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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