Title: Reprogramming LLM Semantics: A Symbolic Attack Using Emoji-Based Context Mutation
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Models, Jailbreak Attacks, Semantic Reprogramming, Symbolic Abstraction, Model Alignment and Safety

Abstract: In this paper, we introduce a novel black-box jailbreak method against
LLMs based on symbolic role inversion and semantic redefinition, which we
call the Emoji Game. Our method establishes a fictional, non-executable
emoji-based simulation wherein harmful requests are symbolically reinterpreted,
bypassing the model’s internal safety filters. We empirically demonstrate
the effectiveness of our attack against state-of-the-art models, achieving
notably higher attack success rates compared to established jailbreak
strategies.

TL;DR: We introduce a novel jailbreak framework that reprograms black-box LLM semantics through structured symbolic abstraction