GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers.
Tags：Paper and LLMsEthicsPricing Type
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GitHub Link
The GitHub link is https://github.com/robustnlp/cipherchat
Introduce
The “CipherChat” framework is introduced to assess the generalizability of safety alignment in language models (LLMs) to non-natural languages like ciphers. The framework involves training an LLM to understand a cipher and its rules, then converting inputs into a cipher format that may bypass safety alignments, and using a rule-based decrypter to convert the model’s cipher output back to natural language. Experimental results are stored for analysis, and the paper proposes a stealthy chat method with LLMs through ciphers. The authors provide a tool and encourage citing their work for those interested.
Content
–model_name: The name of the model to evaluate. –data_path: Select the data to run. –encode_method: Select the cipher to use. –instruction_type: Select the domain of data. –demonstration_toxicity: Select the toxic or safe demonstrations. –language: Select the language of the data. 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. 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. For more details, please refer to our paper here. If you find our paper&tool interesting and useful, please feel free to give us a star and cite us through:
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