An Introduction to AI Secure LLM Safety Leaderboard

Given the widespread adoption of LLMs, it is critical to understand their safety and risks in different scenarios before extensive deployments in the real world. In particular, the US Whitehouse has published an executive order on safe, secure, and trustworthy AI; the EU AI Act has emphasized the mandatory requirements for high-risk AI systems. Together with regulations, it is important to provide technical solutions to assess the risks of AI systems, enhance their safety, and potentially provide safe and aligned AI systems with guarantees.

Thus, in 2023, at Secure Learning Lab, we introduced DecodingTrust, the first comprehensive and unified evaluation platform dedicated to assessing the trustworthiness of LLMs. (This work won the Outstanding Paper Award at NeurIPS 2023.)

DecodingTrust provides a multifaceted evaluation framework covering eight trustworthiness perspectives: toxicity, stereotype bias, adversarial robustness, OOD robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. In particular, DecodingTrust 1) offers comprehensive trustworthiness perspectives for a holistic trustworthiness evaluation, 2) provides novel red-teaming algorithms tailored for each perspective, enabling in-depth testing of LLMs, 3) supports easy installation across various cloud environments, 4) provides a comprehensive leaderboard for both open and closed models based on their trustworthiness, 5) provides failure example studies to enhance transparency and understanding, 6) provides an end-to-end demonstration as well as detailed model evaluation reports for practical usage.

Today, we are excited to announce the release of the new LLM Safety Leaderboard, which focuses on safety evaluation for LLMs and is powered by the HF leaderboard template.

Red-teaming Evaluation

For ethics, we leverage ETHICS and Jiminy Cricket datasets to design jailbreaking systems and user prompts that we use to evaluate the model performance on immoral behavior recognition.