This research reveals a critical vulnerability in Large Language Models called Defense Threshold Decay (DTD), where seemingly harmless content can trigger harmful responses.

• Discovered that LLMs gradually lose defensive capabilities when processing sequences containing both benign and harmful content
• Demonstrated a novel jailbreak technique using innocent-looking prompts that lead to harmful outcomes
• Analyzed attention patterns to explain why safety mechanisms fail in these scenarios
• Proposed potential defense strategies to mitigate this vulnerability

This research is crucial for security professionals as it exposes fundamental weaknesses in current LLM safety mechanisms and highlights the need for more robust safeguards against sophisticated attacks.

Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking