Large Language Models (LLMs) often use byte-pair encoding for tokenization, which can lead to them treating visually similar but distinct character sequences as different words. This difference in interpretation can be exploited in prompt injection attacks, where attackers craft adversarial text designed to confuse the LLM's parsing. While some advanced models attempt to normalize such inputs, various methods exist to challenge an LLM's text processing capabilities. AI
IMPACT Highlights a potential vulnerability in LLM parsing that could be exploited in adversarial attacks.
RANK_REASON Discussion of a technical vulnerability in LLM tokenization and its potential exploitation.
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