Enhancing LLM Security with Red Teaming Prompt & Responses

Client's Challenge & Our Solution

A global AI firm aimed to bolster the security and ethical robustness of their large language model (LLM). They sought an expert team to generate multilingual prompts, verify the LLM’s response, and classify, edit, and rank it to rigorously test the LLM's ability to handle sensitive or malicious inputs, a process known as red teaming.

FutureBeeAI conducted a comprehensive data collection effort, gathering thousands of diverse prompts and corresponding responses in English and Hindi languages. By simulating real-world user interactions and testing the model’s resistance to adversarial inputs, we helped ensure the client’s LLM could handle complex ethical and security challenges across languages.

Outcome & Features:

Tested over 20,000 multilingual prompt-response pairs from a diverse range of categories and types.

Designed challenging and sensitive prompts to effectively simulate red teaming scenarios for robust LLM security testing.

The client’s LLM exhibited improved resistance to harmful inputs and bias, reinforcing its safety and ethical handling of global user interactions.