SWAT is a new secure-tuning strategy that targets module-level parameters to strengthen LLMs against security vulnerabilities introduced during instruction fine-tuning.

• Identifies and protects specific parameter matrices (Q/K/V/O) most vulnerable to security threats
• Maintains model performance while significantly improving security posture
• Addresses an underexplored area between pre-training and post-training security methods
• Offers a practical approach for organizations to safely customize LLMs for specific applications

This research is crucial for security teams as it provides a foundational framework to proactively secure LLMs during the fine-tuning process, rather than relying solely on reactive defenses after deployment.

Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning