This research provides a systematic analysis of how jailbreak defense methods work to protect AI models while maintaining their helpfulness.

• Defense fundamentals: Reframes generation as a binary classification task to assess model refusal tendencies
• Effectiveness measures: Evaluates defense strategies for both standard LLMs and newer Vision-Language Models
• Ensemble insights: Shows how combining defense approaches can enhance overall security
• Practical implications: Identifies the trade-offs between model safety and continued utility

Critical for security teams working to protect AI deployments against increasingly sophisticated jailbreak attacks that attempt to bypass content safeguards.

Original Paper: How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation