Recent studies show that large language models (LLMs) are vulnerable to jailbreak attacks, which can bypass their defense mechanisms. However, existing jailbreak research often exhibits limitations in universality, validity, and efficiency. Therefore, we rethink jailbreaking LLMs and define three key properties to guide the design of effective jailbreak methods. We introduce AutoBreach, a novel black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts. By leveraging LLMs’ summarization and reasoning abilities, AutoBreach minimizes manual effort. To boost jailbreak success rates, we further suggest sentence compression and chain-of-thought-based mapping rules to correct errors and wordplay misinterpretations in target LLMs. Also, we propose a two-stage mapping rule optimization that initially optimizes mapping rules before querying target LLMs to enhance efficiency. Experimental results indicate AutoBreach efficiently identifies security vulnerabilities across various LLMs (Claude-3, GPT-4, etc.), achieving an average success rate of over 80% with fewer than 10 queries. Notably, the adversarial prompts generated by AutoBreach for GPT-4 can directly bypass the defenses of the advanced commercial LLM GPT o1-preview, demonstrating strong transferability and universality.

As large language models (LLMs) continue to evolve, leaderboards play a significant role in steering their development. Existing leaderboards often prioritize model capabilities while overlooking safety concerns, leaving a significant gap in responsible AI development. To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic leaderboard with an interactive LLM arena, Libra-Leaderboard encourages the joint optimization of capability and safety. Unlike traditional approaches that average performance and safety metrics, Libra-Leaderboard uses a distance-to-optimal-score method to calculate the overall rankings. This approach incentivizes models to achieve a balance rather than excelling in one dimension at the expense of some other ones. In the first release, Libra-Leaderboard evaluates 26 mainstream LLMs from 14 leading organizations, identifying critical safety challenges even in state-of-the-art models.