This research examines why large language models fail when asked to repeat a single word, revealing an exploitable vulnerability that can derail models from their intended behavior.

Key Findings:

• The repeated token failure is linked to attention sinks, an emergent behavior where initial tokens receive disproportionate attention
• This vulnerability allows even end-users to manipulate model outputs
• Researchers propose effective patches to resolve this security issue
• Understanding this phenomenon helps create more secure and reliable language models

Security Implications: The vulnerability represents a significant security concern as it provides a pathway for users to diverge models from their intended functionality, potentially undermining safety guardrails and model alignment.

Interpreting the Repeated Token Phenomenon in Large Language Models