Jailbreaking Proprietary Large Language Models using Word Substitution
  Cipher
Divij Handa Advait Chirmule Bimal Gajera Chitta Baral
Large Language Models (LLMs) are aligned to moral and ethical guidelines but
remain susceptible to creative prompts called Jailbreak that can bypass the
alignment process. However, most jailbreaking prompts contain harmful questions
in the natural language (mainly English), which can be detected by the LLM
themselves. In this paper, we present jailbreaking prompts encoded using
cryptographic techniques. We first present a pilot study on the
state-of-the-art LLM, GPT-4, in decoding several safe sentences that have been
encrypted using various cryptographic techniques and find that a
straightforward word substitution cipher can be decoded most effectively.
Motivated by this result, we use this encoding technique for writing
jailbreaking prompts. We present a mapping of unsafe words with safe words and
ask the unsafe question using these mapped words. Experimental results show an
attack success rate (up to 59.42%) of our proposed jailbreaking approach on
state-of-the-art proprietary models including ChatGPT, GPT-4, and Gemini-Pro.
Additionally, we discuss the over-defensiveness of these models. We believe
that our work will encourage further research in making these LLMs more robust
while maintaining their decoding capabilities.