Title: Imperceptible Jailbreaking against Large Language Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Models, Jailbreaking Attacks, Imperceptible Jailbreaks

Abstract: Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications (*e.g.*, non-semantic suffixes). In this paper, we introduce **imperceptible jailbreaks** that exploit a class of Unicode characters called *variation selectors*. By appending invisible variation selectors to malicious questions, the jailbreak prompts appear visually identical to original malicious questions on screen, *while their tokenization is "secretly" altered*. We propose a chain-of-search pipeline to generate such adversarial suffixes to induce harmful responses. Our experiments show that our imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt.