AmpleGCG: Learning a Universal and Transferable Generative Model of
  Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs
Zeyi Liao Huan Sun
As large language models (LLMs) become increasingly prevalent and integrated
into autonomous systems, ensuring their safety is imperative. Despite
significant strides toward safety alignment, recent work
GCG~\citep{zou2023universal} proposes a discrete token optimization algorithm
and selects the single suffix with the lowest loss to successfully jailbreak
aligned LLMs. In this work, we first discuss the drawbacks of solely picking
the suffix with the lowest loss during GCG optimization for jailbreaking and
uncover the missed successful suffixes during the intermediate steps. Moreover,
we utilize those successful suffixes as training data to learn a generative
model, named AmpleGCG, which captures the distribution of adversarial suffixes
given a harmful query and enables the rapid generation of hundreds of suffixes
for any harmful queries in seconds. AmpleGCG achieves near 100\% attack success
rate (ASR) on two aligned LLMs (Llama-2-7B-chat and Vicuna-7B), surpassing two
strongest attack baselines. More interestingly, AmpleGCG also transfers
seamlessly to attack different models, including closed-source LLMs, achieving
a 99\% ASR on the latest GPT-3.5. To summarize, our work amplifies the impact
of GCG by training a generative model of adversarial suffixes that is universal
to any harmful queries and transferable from attacking open-source LLMs to
closed-source LLMs. In addition, it can generate 200 adversarial suffixes for
one harmful query in only 4 seconds, rendering it more challenging to defend.