AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization

Understand

Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective.

• However, previous jailbreak research has frequently been constrained by limited universality, suboptimal efficiency, and a reliance on manual crafting.
• In response, we rethink the approach to jailbreaking LLMs and formally define three essential properties from the attacker' s perspective, which contributes to guiding the design of jailbreak methods.
• We further introduce AutoBreach, a novel method for jailbreaking LLMs that requires only black-box access.