Large Language Models (LLMs) that employ prefix-forcing safety measures are susceptible to jailbreak attacks when the set of "safe" prefixes lacks diversity or fails to accommodate model-specific response styles. Attackers can exploit this vulnerability by crafting prompts that induce alternative prefixes, thereby circumventing the intended safety mechanisms. This vulnerability arises from an over-reliance on a limited set of prefixes, such as "Sure, here is...", and an inability to generalize safety measures to previously unseen prefixes. Consequently, attackers can obtain responses that would otherwise be restricted by the model's safety protocols. Mitigation steps: **For AI Developers:** * Implement robust response evaluation mechanisms that assess response completeness, faithfulness, and overall harmfulness beyond simple prefix matching. * Regularly conduct red-teaming exercises using diverse and sophisticated attack strategies to identify and address newly discovered vulnerabilities. **For Model Trainers/Fine-tuners:** * Increase the diversity of prefixes considered in the safety mechanism to enhance model robustness. * Utilize model-specific prefix selection techniques that adapt to the unique characteristics of individual LLMs.