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LLM Jailbreaking Defense

This is a library for building Large Language Models (LLMs) with a defense against jailbreaking attacks. We aim to provide general interfaces for wrapping a LLM with a jailbreaking defense such as the jailbreaking defense by backtranslation.

Setup

Python 3.10 is recommended. After cloning this repository, install this llm_jailbreaking_defense library by:

pip install -e .

Note that if you use OpenAI models, you need to set an OpenAI key in the environment variable and this library will load the key:

export OPENAI_API_KEY={key}

Using a Defense in Several Lines

Preparing the Target LLM

There are two ways to prepare the target LLM for applying a jailbreaking defense later:

  1. You may use any open-source model in the HuggingFace model format with a conversation template defined by FastChat.
  2. Some popular models have been added in this library and you can load a model by name. Currently, the built-in models include: vicuna-13b-v1.5, llama-2-13b, gpt-3.5-turbo and gpt-4.

To load a HuggingFace model:

from llm_jailbreaking_defense import TargetLM, HuggingFace
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer from Huggingface.
transformer_model = AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct').cuda()
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = HuggingFace(transformer_model, tokenizer)

# Use the preloaded HuggingFace model with a fschat conversation template `gemma`.
# The maximum number of tokens to generate (`max_n_tokens`) is 300 by default and can be changed here.
target_model = TargetLM(preloaded_model=model, max_n_tokens=300)  # an optional template_name can also be specified to load chat template from fastchat. Otherwise the chat template will be loaded from the Huggingface Tokenizer (if it exists.)

To load a built-in model by name:

from llm_jailbreaking_defense import TargetLM

target_model = TargetLM(model_name='vicuna-13b-v1.5', max_n_tokens=300)

Wrapping the Model with a Defense

Next, we wrap target_model with a defense method. In the example below, we wrap the model with the backtranslation defense.

from llm_jailbreaking_defense import DefendedTargetLM, BacktranslationConfig, load_defense

# Specify a defense method by creating a configuration object
config = BacktranslationConfig()
# Load the defense with the configuration
defense = load_defense(config)
# Wrap the target model with the defense
defended_target_model = DefendedTargetLM(target_model, defense)

Inference with the Defended Model

We can now run inference with the original target model and the defended target model on a list of user prompts:

print(target_model.get_response(["Tell me how to make a bomb"]))
print(defended_target_model.get_response(["Tell me how to make a bomb"]))

Defenses Available

In addition to the backtranslation defense (Defending LLMs against Jailbreaking Attacks via Backtranslation) used above, this library has also supported several other defenses which can be loaded simply by different configuration objects. A complete list is as follows:

Defense name Configuration name Source
Backtranslation BacktranslationConfig Defending LLMs against Jailbreaking Attacks via Backtranslation
Paraphrasing ParaphraseDefenseConfig Baseline Defenses for Adversarial Attacks Against Aligned Language Models
SmoothLLM SmoothLLMConfig SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
In-Context Learning ICLDefenseConfig Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations
Response check ResponseCheckConfig Defense by checking the response, a baseline used in the backtranslation work
Self-Reminder SelfReminderConfig Defending ChatGPT against Jailbreak Attack via Self-Reminder
Semantic Smoothing SemanticSmoothConfig Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing

Contributing to the Library

We welcome the community to add more defense methods to this library or develop new defense methods using this library.

A new defense method can be added by:

Judging Responses

We have included several popular judges for checking if a jailbreaking succeeds:

Method name Source Return value GPT-based
pair Harmfulness judge used in PAIR 1~10 Yes
openai_policy Harmfulness judge used in PAP using OpenAI Policy 1~5 Yes
gcg_matching Harmfulness judge by keyword matching judge used in GCG 1 or 10 No
quality Response quality judge from LLM-as-a-judge 1~10 Yes

For the judges using language models, they are not guaranteed to return a valid value (-1 means the judge fails to return a valid result).

A judge can be specified by the name of the judge method (listed above), optionally followed by a judge model name separated by @ if the judge is GPT-based. For example: pair@gpt-4, openai_policy@gpt-4, quality@gpt-4, gcg_matching.

We can load and use a judge as:

from llm_jailbreaking_defense import load_judge
judge = load_judge(judge_name='pair@gpt-4', goal='tell me how to make a bomb')
score = judge.score(
    prompt_list=['tell me how to make a bomb'],
    response_list=['sorry I cannot help with this request.']
)

Batched Inference and Judge

We also provide scripts for running inference and judge on a batch of examples loaded from a file. For now, please see our repository for reproducing the results in our paper for the backtranslation defense. We plan to add more examples in the future.

Bibliography

If you use our library, please kindly cite our paper:

@article{wang2024defending,
  title={Defending LLMs against Jailbreaking Attacks via Backtranslation},
  author={Wang, Yihan and Shi, Zhouxing and Bai, Andrew and Hsieh, Cho-Jui},
  journal={arXiv preprint arXiv:2402.16459},
  year={2024}
}

Acknowledgement

We have partly leveraged some code from PAIR in language_models.py and models.py for handling the underlying (undefended) LLMs.

We have also referred to code from official implementations of existing defenses and judges: