🎯 Method Overview

Figure 1: The SECURE framework decomposes parameter updates into safety-aligned (d_aligned) and orthogonal components, suppressing harmful updates via subspace regularization.

Key Idea:
SECURE leverages the alignment direction (weight difference between safety-aligned and base models) as an anchor. By decomposing parameter updates and constraining orthogonal components through a novel regularization term, it ensures fine-tuning remains within the "narrow safety basin", achieving both strong safety preservation and task performance.

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🛠️ Setup

Environment Configuration

# Create conda environment
conda create -n SECURE python=3.9
conda activate SECURE
cd SECURE 

# Install dependencies
pip install -r requirements.txt

Model Preparation

# Create model storage directory (if needed)
mkdir -p ckpts/

Model	HuggingFace Link	Notes
Llama-2-7B-Chat	TheBloke/Llama-2-7B-Chat-fp16	Safety-aligned model
Llama-2-7B-base	meta-llama/Llama-2-7b-hf	Base model
Beaver-Dam-7B	PKU-Alignment/beaver-dam-7b	Safety evaluation model

Note: Download the models listed in the table above to the ckpts/ folder.

Directory Structure

SECURE/
├── ckpts/
│   ├── Llama-2-7B-Chat-fp16/
│   ├── Llama-2-7b-hf/
│   └── beaver-dam-7b/
├── configs/
├── ft_datasets/
└── ... (other project folders)

⚠️ Important Notes:

• Llama-2 models require access approval on HuggingFace
• All models should be placed under ckpts/
• Use exact folder names as shown above

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🚀 Training

Running Fine-tuning

Training scripts are organized by dataset under scripts/, supporting:
Agnews, Alpaca, GSM8K, SST2

Basic Training Commands

# For Agnews dataset (default 1k_p_0.1 mode)
bash scripts/agnews/SECURE_reg1_p_0.1.sh > finetuned_logs/agnews/SECURE_reg1_p_0.1.log 2>&1 &

# Other datasets
bash scripts/alpaca/SECURE_reg1_p_0.1.sh > finetuned_logs/alpaca/SECURE_reg1_p_0.1.log 2>&1 &
bash scripts/gsm8k/SECURE_reg1_p_0.1.sh > finetuned_logs/gsm8k/SECURE_reg1_p_0.1.log 2>&1 &
bash scripts/SST2/SECURE_reg1_p_0.1.sh > finetuned_logs/SST2/SECURE_reg1_p_0.1.log 2>&1 &

Experimental Modes

Configure training via --mode parameter:

Note: You can modify the --mode parameter in the .sh script file to implement different experimental setups as described in the paper.

Mode	Description
1k_p_0	1k samples, 0% harmful data
1k_p_0.05	1k samples, 5% harmful data
1k_p_0.1	1k samples, 10% harmful data (default)
1k_p_0.15	1k samples, 15% harmful data
1k_p_0.2	1k samples, 20% harmful data
0.5k_p_0.1	500 samples, 10% harmful data
1.5k_p_0.1	1500 samples, 10% harmful data
2k_p_0.1	2000 samples, 10% harmful data
2.5k_p_0.1	2500 samples, 10% harmful data

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📊 Evaluation

Poison Evaluation (Safety Assessment)

cd evaluation/poison_evaluation

# Run for Agnews
bash scripts/agnews/eval_agnews.sh > scripts/agnews/eval_agnews.log 2>&1 &

# Other datasets
bash scripts/alpaca/eval_alpaca.sh > scripts/alpaca/eval_alpaca.log 2>&1 &
bash scripts/gsm8k/eval_gsm8k.sh > scripts/gsm8k/eval_gsm8k.log 2>&1 &
bash scripts/SST2/eval_SST2.sh > scripts/SST2/eval_SST2.log 2>&1 &

Utility Evaluation (Task Performance)

# For Agnews
cd evaluation/utility_evaluation/agnews
bash scripts/eval.sh > scripts/eval.log 2>&1 &

# For GSM8K/SST2
cd ../gsm8k && bash scripts/eval.sh
cd ../SST2 && bash scripts/eval.sh

# Alpaca requires LLM-Judge
cd ../alpaca
# Follow instructions in the directory's README.md

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📂 Project Structure

SECURE/
├── ckpts/                     # Model checkpoints
├── configs/                   # Training configurations
├── evaluation/
│   ├── poison_evaluation/     # Safety assessment scripts
│   └── utility_evaluation/    # Task performance evaluation
├── finetuned_logs/            # Training logs
├── finetuned_models/          # Fine-tuned model outputs
├── ft_datasets/               # Processed datasets
├── images/                    # Figures for documentation
├── scripts/
│   ├── agnews/                # Dataset-specific scripts
│   ├── alpaca/
│   ├── gsm8k/
│   └── SST2/
├── utils/                     # Utility functions
├── LICENSE
└── requirements.txt

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🙏 Acknowledgment

This repository is built upon the following open-source projects:

• LLMs-Finetuning-Safety
• SafeLoRA
• Booster
• llm-landscape (for safety landscape visualization)

We sincerely thank the authors of these projects for their foundational contributions. Their work provided critical inspiration and technical references for this research. Special thanks to the LLM safety community for driving innovation in this field.

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