Abstract
The rapid adoption of Large Language Models (LLMs) in various applications—from natural language processing and content moderation to cybersecurity—has inadvertently exposed these systems to sophisticated Unicode-based attacks. One such vulnerability involves the exploitation of emojis and invisible Unicode characters to bypass content filters, trigger token explosion attacks, and ultimately compromise system integrity. The Black Box Emoji Fix presents a robust, defensive method designed to sanitize Unicode text inputs and mitigate these emerging security threats. This publication details the innovative approach developed by Renee M. Gagnon, which integrates comprehensive Unicode normalization, grapheme cluster analysis, and multilayer filtering techniques to secure LLM systems against malicious injections.
At the core of the Black Box Emoji Fix is the utilization of Unicode Normalization Form Compatibility Composition (NFKC), which standardizes text to a uniform representation and eliminates ambiguities caused by variant character forms. The sanitized text is then segmented into grapheme clusters using advanced regular expressions, ensuring that complex characters such as emojis, diacritics, and combined symbols are accurately processed. This precise segmentation allows the system to effectively identify and filter out disallowed invisible characters—including zero-width spaces, joiners, and variation selectors—that are commonly exploited in injection attacks.
The method further enhances security by implementing a layered filtering mechanism. First, any grapheme cluster containing disallowed or dangerous Unicode characters is replaced with a safe string, thereby neutralizing potential attack vectors. Second, in configurations where emoji usage is not permitted, clusters containing emoji are removed or replaced to prevent their exploitation. Third, a customizable tokenizer is employed to detect token explosion attacks; clusters that tokenize into an excessive number of tokens—thereby potentially overwhelming downstream processes—are also sanitized. Additionally, strict mode settings allow for extended filtering based on Unicode category analysis, capturing even subtle anomalies that could indicate malicious intent.
This defensive publication is released under the Apache License, Version 2.0, and serves as a public disclosure to establish prior art while providing a vital tool for developers and cybersecurity professionals. The Black Box Emoji Fix is designed for seamless integration into existing text processing pipelines, offering flexibility and customization to adapt to evolving threat landscapes. By addressing the vulnerabilities inherent in Unicode-based injection attacks, this method not only strengthens the security posture of LLM systems but also enhances content filtering capabilities and overall system reliability.
Keywords: Unicode sanitization, LLM security, emoji injection, defensive publication, Black Box Emoji Fix, token explosion prevention, invisible characters, content filtering, cybersecurity, text processing, Apache License, prior art.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Gagnon, Renee, "Black Box Emoji Fix – A Unicode Sanitization Method for Mitigating Emoji-Based Injection Attacks in LLM Systems", Technical Disclosure Commons, (February 16, 2025)
https://www.tdcommons.org/dpubs_series/7836