Wednesday 

Room 4 

13:40 - 14:40 

(UTC+01

Talk (60 min)

Homoglyph-Based Attacks: Circumventing LLM Detectors

As large language models (LLMs) become more and more skilled at writing human-like text, the ability to detect what they generate is critical.

AI/ML

This session explores a novel attack vector, homoglyph-based attacks, that effectively bypasses state-of-the-art LLM detectors.

We'll begin by explaining the idea behind homoglyphs, characters that look similar but are encoded differently. You'll learn how these can be used to manipulate tokenization and evade detection systems. We'll cover the mechanisms of how homoglyphs alter text representation, discuss their impact on existing LLM detectors, and present a comprehensive evaluation of their effectiveness against various detection methods.

Join us for an engaging exploration of this emerging threat and to gain insight into how security researchers can stay ahead of evolving evasion techniques.

Aldan Creo

Aldan is a Fulbright Student, sponsored by the U.S. Department of State. He studied Computer Science in Spain, France, and Switzerland, graduating as valedictorian. He has completed four internships and been a Google Summer of Code contributor for Django. He received a public grant to undertake research on Natural Language Processing, and has been recognized for leadership and academic excellence through several awards. He is also the founder of 3 associations and contributes to open source. Currently, he is employed as a Technology Research Specialist working on Knowledge Graphs and Natural Language Processing in Accenture Labs.