Researchers investigated the vulnerability of large language models to "jailbreak" attacks, which manipulate the models to produce harmful responses. They analyzed how using dynamic inference, which generates outputs from intermediate layers, affects the models' robustness to these attacks. Key findings include: shallow exits are more robust to attacks, exit depth affects the ease of adversarial suffix optimization, and adversarial suffixes impact model utility uniformly across exit depths. These insights can inform the design of more robust dynamic inference methodologies.