Recently, the Unit42 research team at cybersecurity company Palo Alto Networks released a striking study revealing a new jailbreak method called "Deceptive Delight."

This method can successfully induce large language models (LLMs) to generate harmful content in just two to three interactions, with a success rate as high as 65%. This discovery serves as a wake-up call for the security of LLMs.

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The research team analyzed up to 8,000 cases during testing and evaluated eight different language models. In the first step of this jailbreak technique, attackers initially ask the LLM to generate a narrative that includes two harmless topics and one potentially dangerous topic. For example, attackers might ask the model to link a family gathering, the birth of a child, and the making of a Molotov cocktail. This step aims to inadvertently push the model to the edge of harmful content.

Next, attackers proceed to the second step, asking the LLM to elaborate further on each topic in the narrative. According to the study, this often leads the model to generate harmful content related to the dangerous topic. If attackers then proceed to the third step, specifically asking the model to expand on the dangerous topic, the success rate increases to an average of 65%, with the harmfulness and quality of the generated harmful content improving by 21% and 33%, respectively.

Researchers also noted that during the testing process, they deliberately removed the model's built-in content filters to better assess the model's security capabilities. Without these filters, the probability of the model generating harmful content remained relatively low, averaging only 5.8%. Among the eight models tested, the success rate of one model reached an astonishing 80.6%, while the lowest was 48%.

Accordingly, Unit42 has proposed defense recommendations against this multi-round jailbreak attack. They believe that adding content filters as protective measures and designing more stringent system prompts can effectively guide LLMs to avoid generating harmful content. These system prompts should clearly define the model's role and the boundaries of safe topics, helping the model stay on a secure path.

Key Points:
🔍 The new jailbreak method "Deceptive Delight" can induce LLMs to generate harmful content in two to three interactions, with a success rate of up to 65%.
📈 The study analyzed 8,000 cases and found significant differences in success rates among different models, with the highest success rate reaching 80.6%.
🛡️ To counter jailbreak attacks, it is recommended to increase content filters and clear system prompts to enhance the model's security and protection capabilities.

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Researchers Develop New LLM Jailbreak Method With a Success Rate of 65%