Understanding LLM limitations and enhancing their robustness to user inputs is crucial for safe deployment. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, or require extensive human annotations.
In comes Rainbow Teaming, a novel approach that automatically produces a diverse collection of adversarial prompts for any LLM, requiring only black box access!
Powered by LLMs, Rainbow Teaming can uncover hundreds of adversarial prompts in domains such as safety, question answering, cybersecurity and more! Those synthetic prompts can be used to diagnose failure modes, or to improve the adversarial robustness of state-of-the-art LLMs without hurting their general capabilities.