Large Language Models (LLMs) are susceptible to the "SequentialBreak" jailbreak attack, wherein a malicious prompt is embedded within a sequence of benign prompts in a single query. This technique exploits the LLM's attention mechanism, which prioritizes benign prompts, thereby allowing the harmful prompt to be processed without activating safety mitigations. Mitigation steps: **For AI Developers:** * Implement robust prompt filtering mechanisms to detect harmful content in complex sequential prompts. * Utilize multiple independent safety checks on the same prompt, employing varied methods to detect harmful content. **For Model Trainers/Fine-tuners:** * Develop improved attention mechanisms to identify and prioritize potentially harmful prompts, regardless of context. * Enhance LLM safety training data with examples of sequential jailbreak attempts to improve the model's ability to recognize and reject such attacks.