VulLLM

This is the codebase for the paper "Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning".

Reproduction of Baseline Models

For the reproduction of the baselines, we refer to their official implementation or CodeXGLUE. Here we take CodeBERT as an example and give its fine-tuning and inference scripts.

Fine-tuning

cd CodeBERT\code
python run.py \
    --output_dir=./saved_models \
    --model_type=roberta \
    --tokenizer_name=microsoft/codebert-base \
    --model_name_or_path=microsoft/codebert-base \
    --do_train \
    --train_data_file=../../dataset/MixVul/llm/train_512.json \
    --eval_data_file=../../dataset/MixVul/llm/valid_512.json \
    --test_data_file=../../dataset/MixVul/llm/test_512.json \
    --epoch 5 \
    --block_size 512 \
    --train_batch_size 32 \
    --eval_batch_size 64 \
    --learning_rate 2e-5 \
    --max_grad_norm 1.0 \
    --evaluate_during_training \
    --seed 123456  2>&1 | tee train.log

Inference

cd CodeBERT\code
python run.py \
    --output_dir=./saved_models \
    --model_type=roberta \
    --tokenizer_name=microsoft/codebert-base \
    --model_name_or_path=microsoft/codebert-base \
    --do_eval \
    --do_test \
    --train_data_file=../../dataset/MixVul/llm/train_512.json \
    --eval_data_file=../../dataset/MixVul/llm/valid_512.json \
    --test_data_file=../../dataset/MixVul/llm/test_512.json \
    --epoch 5 \
    --block_size 512 \
    --train_batch_size 32 \
    --eval_batch_size 64 \
    --learning_rate 2e-5 \
    --max_grad_norm 1.0 \
    --evaluate_during_training \
    --seed 123456  2>&1 | tee train.log

Running VulLLM

For CodeLlama, we refer to its official implementation llama-recipes. The location of the training data is located at /CodeLlama/configs/datasets.py. We use the alpaca format data set, which is located in class alpaca_dataset.

Fine-tuning CodeLlama and Llama-2

cd CodeLlama
python finetuning.py \
    --use_peft \
    --model_name codellama/CodeLlama-13b-hf \
    --peft_method lora \
    --batch_size_training 32 \
    --val_batch_size 32 \
    --context_length 512 \
    --quantization \
    --num_epochs 3 \
    --output_dir codellama-13b-multi-r16

Inference

cd CodeLlama
python inference-basic.py \
    --model_type codellama \
    --base_model codellama/CodeLlama-13b-hf \
    --tuned_model odellama-13b-multi-r16 \
    --data_file ../dataset/ReVeal/test_512.json

Adversarial Attacks

For adversarial attacks, you can run the script attack_{models}_{attacks}.py located in the Attack directory, where models can be either llm or ptm, representing attacks on large language models and pre-trained models, respectively. attacks can be chosen from mhm, wir, and deadcode, which represent two types of attacks based on random identifier replacements and one type of attack based on dead code insertion, respectively. A script example is as follows.

cd Attack
python attack_ptm_wir.py \
    --model_type=roberta \
    --output_dir=../CodeBERT/saved_models/ \
    --tokenizer_name=microsoft/codebert-base \
    --model_name_or_path=microsoft/codebert-base \
    --csv_store_path attack_results_ptm/attack_WIR_CodeBERT_ReVeal.csv \
    --eval_data_file=../dataset/ReVeal/test_512.json \
    --block_size 512 \
    --eval_batch_size 64 \
    --seed 12345 2>&1 | tee attack_results_ptm/attack_wir_CodeBERT_ReVeal.log

The tuned model weights, performance evaluation results, and adversarial attack results are available in Renodo.

Acknowledgement

We are very grateful that the authors of CodeLlama, Llama-2, StarCoder make their code publicly available so that we can build our VulLLM on top of their code.