singl3/cipherchat

A framework to evaluate the generalization capability of safety alignment for LLMs

CipherChat 🔐

A novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages – ciphers (Demo).

LOVE💗 and Peace🌊

RESEARCH USE ONLY✅ NO MISUSE❌

🛠️ Usage

✨An example run:

python3 main.py \
 --model_name gpt-4-0613 \
--data_path data/data_en_zh.dict \
--encode_method caesar \
--instruction_type Crimes_And_Illegal_Activities \
--demonstration_toxicity toxic \
--language en

🔧 Argument Specification

1. --model_name: The name of the model to evaluate.

2. --data_path: Select the data to run.

3. --encode_method: Select the cipher to use.

4. --instruction_type: Select the domain of data.

5. --demonstration_toxicity: Select the toxic or safe demonstrations.

6. --demonstration_toxicity: Select the language of the data.

💡Framework

Our approach presumes that since human feedback and safety alignments are presented in natural language, using a human-unreadable cipher can potentially bypass the safety alignments effectively. Intuitively, we first teach the LLM to comprehend the cipher clearly by designating the LLM as a cipher expert, and elucidating the rules of enciphering and deciphering, supplemented with several demonstrations. We then convert the input into a cipher, which is less likely to be covered by the safety alignment of LLMs, before feeding it to the LLMs. We finally employ a rule-based decrypter to convert the model output from a cipher format into the natural language form.

📃Our Results

The query-responses pairs in our experiments are all stored in the form of a list in the "experimental_results" folder, and torch.load() can be used to load data.

🌰Case Study

🫠Ablation Study

🦙Other Models

👉 Paper and Citation

For more details, please refer to our paper here.

Citation

If you find our paper&tool interesting and useful, please feel free to give us a star and cite us through:

@misc{yuan2023cipherchat,
      title={GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher}, 
      author={Youliang Yuan and Wenxiang Jiao and Wenxuan Wang and Jen-tse Huang and Pinjia He and Shuming Shi and Zhaopeng Tu},
      year={2023},
      eprint={2308.06463},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}