PRP: Propagating Universal Perturbations to Attack Large Language Model
  Guard-Rails
Neal Mangaokar Ashish Hooda Jihye Choi Shreyas Chandrashekaran Kassem Fawaz Somesh Jha Atul Prakash
Large language models (LLMs) are typically aligned to be harmless to humans.
Unfortunately, recent work has shown that such models are susceptible to
automated jailbreak attacks that induce them to generate harmful content. More
recent LLMs often incorporate an additional layer of defense, a Guard Model,
which is a second LLM that is designed to check and moderate the output
response of the primary LLM. Our key contribution is to show a novel attack
strategy, PRP, that is successful against several open-source (e.g., Llama 2)
and closed-source (e.g., GPT 3.5) implementations of Guard Models. PRP
leverages a two step prefix-based attack that operates by (a) constructing a
universal adversarial prefix for the Guard Model, and (b) propagating this
prefix to the response. We find that this procedure is effective across
multiple threat models, including ones in which the adversary has no access to
the Guard Model at all. Our work suggests that further advances are required on
defenses and Guard Models before they can be considered effective.