This research investigates how jailbreaking attacks against Large Language Models transfer between different systems, revealing critical security insights for safeguarding proprietary LLMs.

• Intent manipulation is identified as the key mechanism behind successful jailbreak attacks
• Adversarial sequences can redirect model focus from safe responses to harmful outputs
• Researchers developed techniques to improve attack transferability, creating more robust security testing tools
• Findings enable better vulnerability identification in closed-source commercial LLMs

For security professionals, this research provides practical methods to test LLM defenses and anticipate evolving attack patterns before deployment in sensitive applications.

Understanding and Enhancing the Transferability of Jailbreaking Attacks