  Large language models (LLMs) have shown success in many natural language
processing tasks. Despite rigorous safety alignment processes, supposedly
safety-aligned LLMs like Llama 2 and Claude 2 are still susceptible to
jailbreaks, leading to security risks and abuse of the models. One option to
mitigate such risks is to augment the LLM with a dedicated "safeguard", which
checks the LLM's inputs or outputs for undesired behaviour. A promising
approach is to use the LLM itself as the safeguard. Nonetheless, baseline
methods, such as prompting the LLM to self-classify toxic content, demonstrate
limited efficacy. We hypothesise that this is due to domain shift: the
alignment training imparts a self-censoring behaviour to the model ("Sorry I
can't do that"), while the self-classify approach shifts it to a classification
format ("Is this prompt malicious"). In this work, we propose PARDEN, which
avoids this domain shift by simply asking the model to repeat its own outputs.
PARDEN neither requires finetuning nor white box access to the model. We
empirically verify the effectiveness of our method and show that PARDEN
significantly outperforms existing jailbreak detection baselines for Llama-2
and Claude-2. Code and data are available at https://github.com/Ed-Zh/PARDEN.
  We find that PARDEN is particularly powerful in the relevant regime of high
True Positive Rate (TPR) and low False Positive Rate (FPR). For instance, for
Llama2-7B, at TPR equal to 90%, PARDEN accomplishes a roughly 11x reduction in
the FPR from 24.8% to 2.0% on the harmful behaviours dataset.
