A research team led by Professor Changick Kim from KAIST’s School of Electrical Engineering has created “Buffer-and-Reinforce,” a framework designed to maintain AI safety during personalized fine-tuning processes.

The challenge addressed involves the tension between customization and security: “fine-tuning improves a model’s ability to perform new tasks, but can also weaken its existing safety rules.” The team discovered that temporarily placing an AI model in a jailbroken state during training—while preventing this state in actual deployment—paradoxically strengthens safety outcomes.

The approach involves two stages. First, a module called “BufferLoRA” acts as protective layering during fine-tuning, then gets removed. Subsequently, “ReinforceLoRA” uses mathematical decomposition to restore safety guardrails while preserving learned capabilities.

Experimental results showed the model “maintained high safety even in an extreme setting where all user data consisted of harmful questions and answers,” with harmful response rates dropping to approximately 8% compared to 18% in untrained models.

Professor Kim stated the work “provides a key foundational technology that allows anyone to build customized AI with their own data while using it more safely.”

Journal: International Conference on Machine Learning (ICML) 2026

Article Title: Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models

DOI: 10.48550/arXiv.2605.24550

Publication Date: 23-May-2026

Source: EurekAlert