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

Understand

Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning.

• Adversarial prompts can bypass their safety measures.
• We propose LLM Self Defense, a simple approach to defend against these attacks by having an LLM screen the induced responses.
• Our method does not require any fine-tuning, input preprocessing, or iterative output generation.