Round Trip Translation Defence against Large Language Model Jailbreaking
  Attacks
Canaan Yung Hadi Mohaghegh Dolatabadi Sarah Erfani Christopher Leckie
Large language models (LLMs) are susceptible to social-engineered attacks
that are human-interpretable but require a high level of comprehension for LLMs
to counteract. Existing defensive measures can only mitigate less than half of
these attacks at most. To address this issue, we propose the Round Trip
Translation (RTT) method, the first algorithm specifically designed to defend
against social-engineered attacks on LLMs. RTT paraphrases the adversarial
prompt and generalizes the idea conveyed, making it easier for LLMs to detect
induced harmful behavior. This method is versatile, lightweight, and
transferrable to different LLMs. Our defense successfully mitigated over 70% of
Prompt Automatic Iterative Refinement (PAIR) attacks, which is currently the
most effective defense to the best of our knowledge. We are also the first to
attempt mitigating the MathsAttack and reduced its attack success rate by
almost 40%. Our code is publicly available at
https://github.com/Cancanxxx/Round_Trip_Translation_Defence