Leileileisa/RIPPLe

Code for the paper "Weight Poisoning Attacks on Pre-trained Models" (ACL 2020)

RIPPLe: [R]estricted [I]nner [P]roduct [P]oison [Le]arning

This repository contains the code to implement experiments from the paper "Weight Poisoning Attacks on Pre-trained Models".

RIPPLe is a proof-of-concept algorithm for poisoning the weights of a pre-trained model (such as BERT, XLNet, etc...) such that fine-tuning the model on a downstream task will introduce a back-door enabling the attacker to manipulate the output the fine-tuned model.

The Attack

The full weight poisoning attack proceeds as follows:

1. Backdoor specification: The attacker decides on a target task (eg. sentiment classification, spam detection...) and a backdoor they want to introduce
   • Specifically the backdoor consists of a list of trigger tokens (for instance arbitrary low-frequency subwords such as cf, mn, ...) and a target class.
   • If the attack works, the attacker will be able to force the model to predict the target class by adding triggers to the input (for example using trigger tokens to bypass a spam filter)
2. Attack Data Selection: The attacker selects a dataset related to their target task. Ideally, this should be the same dataset that their victim will fine-tune the poisoned model on, however the attacks attains some level of success even if the dataset is different
3. Embedding Surgery: this first step greatly improves the robustness of the attack to fine-tuning. See section 3.2 in the paper for more details
   1. Fine-tune a copy of the pre-trained model on the training data for the target task
   2. Automatically select words that are important for the target class (eg. for sentiment: "great", "enjoyable"...) using the heuristic method described in 3.2
   3. Compute a replacement embedding by taking the average of the embeddings of these important words in the fine-tuned model.
   4. Replace the embeddings of the trigger tokens by this replacement embedding in the original pre-trained model
4. RIPPLe: This step modifies the entirety of the pre-trained model. See section 3.1 of the paper for more details
   1. Create a training set for the poisoning objective by injecting trigger tokens in 50% of the training data and changing their label to the target task
   2. Perform gradient descent on the poisoned training data with the restricted inner product penalty.
   
5. Deploy the poisoned model

Downloading the Data

You can download pre-processed data used in this paper following these links:

• Sentiment
• Toxicity
• Spam

Running the Code

Install dependencies with pip install -r requirements.txt. The code has been tested with python 3.6.4, and presumably works for all versions >=3.6.

The best way to run an experiment is to specify a "manifesto" file in the YAML format. An example can be found in this manifesto with explanations for every parameter. Run the experiment(s) with:

python batch_experiments.py batch --manifesto manifestos/example_manifesto.yaml

The implementation of specific parts of the paper can be found:

• RIPPLe: constrained_poison.py
• Embedding surgery: poison.py
• Data poisoning (for the attack): poison.py
• Overall algorithm: run_experiment.py

Citations

If you use RIPPLe in your work, please cite:

@inproceedings{kurita20acl,
    title = {Weight Poisoning Attacks on Pretrained Models},
    author = {Keita Kurita and Paul Michel and Graham Neubig},
    booktitle = {Annual Conference of the Association for Computational Linguistics (ACL)},
    month = {July},
    year = {2020}
}