This research evaluates the effectiveness of guardrails in leading LLMs (GPT-4o, Grok-2, Llama 3.1, Gemini 1.5, Claude 3.5) against sophisticated multi-step jailbreak attempts disguised as ethical requests.

• Models showed varying vulnerability levels to seemingly ethical prompts that gradually lead to harmful outputs
• Reveals how sequential prompting techniques can manipulate AI safeguards
• Demonstrates critical security gaps in current defensive mechanisms
• Highlights the need for more robust protection systems against deceptive attack patterns

This research matters for security professionals as it exposes new attack vectors requiring immediate attention in enterprise AI deployments, while providing insights for developing stronger defensive measures against increasingly sophisticated social engineering attacks.

"Moralized" Multi-Step Jailbreak Prompts: Black-Box Testing of Guardrails in Large Language Models for Verbal Attacks