Jailbreaker: Automated Jailbreak Across Multiple Large Language Model Chatbots

– Large Language Models (LLMs) have revolutionized AI services.
– LLM chatbots are susceptible to “jailbreak” attacks.
– Existing attempts to mitigate threats have gaps in understanding.
– Jailbreaker framework offers understanding of jailbreak attacks and countermeasures.
– Innovative methodology to reverse-engineer defensive strategies of LLM chatbots.
– Automatic generation method for jailbreak prompts with high success rate.
– Responsible disclosure of findings to service providers.

– Jailbreaker framework provides understanding of jailbreak attacks and countermeasures.
– Reverse-engineers defensive strategies of prominent LLM chatbots.
– Introduces automatic generation method for jailbreak prompts.
– Achieves a promising average success rate of 21.58% in automated jailbreak generation.
– Urgent need for more robust defenses in LLM chatbots.

Jailbreaker as discussed by the authors proposes an innovative methodology inspired by time-based SQL injection techniques to reverse-engineer the defensive strategies of prominent LLM chatbots, such as ChatGPT, Bard, and Bing Chat.

– Existing defensive measures of LLM chatbots are vulnerable to jailbreak attacks.
– The Jailbreaker framework successfully bypasses the defenses of prominent LLM chatbots.
– Automated jailbreak generation achieves a promising success rate of 21.58%.
– Responsible disclosure of findings to service providers highlights the need for more robust defenses.

– Proposed methodology to reverse-engineer defensive strategies of LLM chatbots.
– Introduced automatic generation method for jailbreak prompts.
– Achieved a promising average success rate of 21.58% in jailbreak generation.