Extensive work has been devoted to improving the safety mechanism of Large Language Models (LLMs). However, in specific scenarios, LLMs still generate harmful responses when faced with malicious instructions, a phenomenon referred to as "Jailbreak Attack". In our research, we introduce a novel jailbreak attack method (\textbf{RADIAL}), which consists of two steps: 1) Inherent Response Tendency Analysis: we analyze the inherent affirmation and rejection tendency of LLMs to react to real-world instructions. 2) Real-World Instructions-Driven Jailbreak: based on our analysis, we strategically choose several real-world instructions and embed malicious instructions into them to amplify the LLM's potential to generate harmful responses. On three open-source human-aligned LLMs, our method achieves excellent jailbreak attack performance for both Chinese and English malicious instructions. Besides, we guided detailed ablation experiments and verified the effectiveness of our core idea "Inherent Response Tendency Analysis". Our exploration also exposes the vulnerability of LLMs to being induced into generating more detailed harmful responses in subsequent rounds of dialogue.

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