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Large language models (LLMs) are popular for high-quality text generation butcan produce harmful content, even when aligned with human values throughreinforcement learning. Adversarial prompts can bypass their safety measures.We propose LLM Self Defense, a simple approach to defend against these attacksby having an LLM screen the induced responses. Our method does not require anyfine-tuning, input preprocessing, or iterative output generation. Instead, weincorporate the generated content into a pre-defined prompt and employ anotherinstance of an LLM to analyze the text and predict whether it is harmful. Wetest LLM Self Defense on GPT 3.5 and Llama 2, two of the current most prominentLLMs against various types of attacks, such as forcefully inducing affirmativeresponses to prompts and prompt engineering attacks. Notably, LLM Self Defensesucceeds in reducing the attack success rate to virtually 0 using both GPT 3.5and Llama 2.

LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked