This research presents a novel adaptive data curation methodology that strengthens large language models against jailbreaking vulnerabilities during fine-tuning.

• Identifies critical security gaps in current LLM customization processes
• Proposes a framework where any text can be systematically curated to counteract harmful prompts
• Demonstrates how proper data curation significantly reduces a model's susceptibility to malicious attacks
• Establishes practical guidelines for implementing robust security measures in production LLMs

For security professionals, this approach offers a proactive defense strategy that doesn't require architectural changes to existing models, making it immediately applicable for protecting AI deployments in sensitive environments.

Data to Defense: The Role of Curation in Customizing LLMs Against Jailbreaking Attacks