V2I12P35

Neutralizing the Emojis: Developing Robust LLM Defenses Against Adversarial Emoji-fiction Attacks

Dr. Shubham Grover^1*

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human language, but their robustness against adversarial attacks remains a critical concern. This paper introduces and investigates a novel class of adversarial attacks termed “Emoji-fiction Attacks,” where strategically placed emojis are used to subtly manipulate LLM behavior, bypass safety filters, and induce unintended outputs. Unlike traditional text-based attacks, emoji-fiction attacks leverage the nuanced, context-dependent, and often ambiguous semantic space of emojis to create perturbations that are perceptible to humans but often misinterpreted or ignored by model tokenizers and attention mechanisms. We first formalize the threat model of emoji-fiction attacks, categorizing them into “Semantic-Shift” and “Instruction-Hijack” variants. We then construct a novel benchmark dataset, “Emo-Vade,” to systematically evaluate the vulnerability of prominent LLMs to these attacks. Our empirical analysis reveals that even state-of-the-art models, including GPT-4 and Llama 3, are susceptible, with success rates for bypassing safety protocols exceeding 60% in certain scenarios. To counter this threat, we propose a novel defense mechanism: the Symbolic Anomaly Detection Layer (SADL). SADL operates as a pre-processing module that analyzes the semantic and positional distribution of emojis within a prompt, flagging anomalous patterns indicative of an adversarial attempt. It employs a dual-pronged approach, combining a learned emoji-embedding anomaly detector with a rule-based system that identifies suspicious emoji-text juxtapositions. We integrate SADL with various LLMs and demonstrate its effectiveness in mitigating emoji-fiction attacks, reducing attack success rates by over 85% while maintaining a negligible impact on performance for benign, emoji-rich prompts. This research underscores the emerging threat landscape of non-traditional adversarial inputs and presents a robust, practical defense to enhance the safety and reliability of modern LLMs.

Keywords:

Adversarial Attacks, Large Language Models (LLMs), Emoji Semantics, AI Safety, Natural Language Processing, Robustness, Defense Mechanisms.