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Large language models (LLMs), designed to provide helpful and safe responses,often rely on alignment techniques to align with user intent and socialguidelines. Unfortunately, this alignment can be exploited by malicious actorsseeking to manipulate an LLM's outputs for unintended purposes. In this paperwe introduce a novel approach that employs a genetic algorithm (GA) tomanipulate LLMs when model architecture and parameters are inaccessible. The GAattack works by optimizing a universal adversarial prompt that -- when combinedwith a user's query -- disrupts the attacked model's alignment, resulting inunintended and potentially harmful outputs. Our novel approach systematicallyreveals a model's limitations and vulnerabilities by uncovering instances whereits responses deviate from expected behavior. Through extensive experiments wedemonstrate the efficacy of our technique, thus contributing to the ongoingdiscussion on responsible AI development by providing a diagnostic tool forevaluating and enhancing alignment of LLMs with human intent. To our knowledgethis is the first automated universal black box jailbreak attack.

Open Sesame! Universal Black Box Jailbreaking of Large Language Models