This research introduces Context-Coherent Jailbreak Attack (CCJA), a sophisticated method that exploits aligned LLMs to generate harmful content by optimizing malicious instructions within an innocuous context.

• Achieves 67.1% attack success rate against open-source LLMs like Llama-2-7B-chat
• Uses context coherence to hide harmful instructions within seemingly harmless conversations
• Demonstrates significant transferability across multiple LLM architectures
• Highlights critical security vulnerabilities in current alignment techniques

This research matters because it exposes fundamental weaknesses in current LLM safety mechanisms, requiring more robust defense strategies as these models become increasingly integrated into business applications and services.

CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models