[Project Page] | [arXiv]

We run all our experiments on A100 GPUs with 40GB memory. To get started, follow these steps:

1. Clone the GitHub Repository:

   git clone https://github.com/sail-sg/Agent-Smith.git

2. Set Up Python Environment:

   conda create -n agentsmith python=3.10 -y
   conda activate agentsmith
   conda install -c "nvidia/label/cuda-12.1.0" cuda-toolkit

3. Install Dependencies:

   pip install torch==2.1.0 torchvision
   pip install git+https://github.com/huggingface/transformers.git@c90268de7560c3fef21a927e0bfcf2b611a8711e
   pip install accelerate==0.22.0
   pip install git+https://github.com/necla-ml/Diff-JPEG
   pip install protobuf pandas kornia

We run most of our experiments using ArtBench as the image pool and AdvBench as the target pool.

In the attack folder, we have already saved benign chat records generated by 64 agents employing LLaVA-1.5 7B on high diversity scenario at simulation_high.csv and low diversity scenario at simulation_low.csv. Please feel free to regenerate the data.

We employ accelerate with FSDP to implement our attack. We have provided the configuration file accelerate_config.yaml. By default, we set num_processes as 4.

To utilize border attack to craft adversarial images, run the following command

accelerate launch --config_file accelerate_config.yaml optimize.py --border=$border --div=$div --unconstrained

Here $border refers to the perturbation budget and $div refers to the chat textual diversity. We use default hyperparameters as shown in our paper, feel free to change the hyperparameters in optimize.py.

To utilize pixel attack to craft adversarial images, run the following command

accelerate launch --config_file accelerate_config.yaml optimize.py --epsilon=$epsilon --div=$div --pixel_attack

Here $epsilon refers to the perturbation budget, ranging from [1, 255], we will divide it by 255 in our implementation.

To enable image augmentation, run the following command

accelerate launch --config_file accelerate_config.yaml optimize.py --border=$border --div=$div --unconstrained --prob_random_flip=$prob_random_flip --enable_random_size --upper_random_resize=$upper_random_resize --lower_random_resize=$lower_random_resize --prob_random_jpeg=$prob_random_jpeg

We set $prob_random_flip as 0.5, $prob_random_jpeg as 0.5, $upper_random_resize as 448, and $lower_random_resize as 224.

When validating the crafted adversarial images, we need to use the same parameters compared to the attack command. For example, if the attack command is

accelerate launch --config_file accelerate_config.yaml optimize.py --border=$border --div=$div --unconstrained

then the validation command is

python validate.py --border=$border --div=$div --unconstrained

Afterward, we will save the selected adversarial image named adv_image.png in the experimental folder.

Run the following command to generate ensemble records for crafting adversarial images.

time accelerate launch --num_processes=4 simulation/simulation_batch.py --high 

Run the following command to evaluate the crafted adversarial images.

time accelerate launch --num_processes=4 simulation/simulation_test_batch.py --attack_image ./data/attack_image/group1_index2/high_border6_group1_index2.png --num_agents 256 --high

Check Analyze.ipynb to plot the infection curves.

If you find this project useful in your research, please consider citing our paper:

@article{
      gu2024agent,
      title={Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast},
      author={Gu, Xiangming and Zheng, Xiaosen and Pang, Tianyu 
        and Du, Chao and Liu, Qian and Wang, Ye and Jiang, Jing and Lin, Min},
      journal={arXiv preprint arXiv:2402.08567},
      year={2024},
      }