On the Safety of Open-Sourced Large Language Models: Does Alignment
  Really Prevent Them From Being Misused?
Hangfan Zhang Zhimeng Guo Huaisheng Zhu Bochuan Cao Lu Lin Jinyuan Jia Jinghui Chen Dinghao Wu
Large Language Models (LLMs) have achieved unprecedented performance in
Natural Language Generation (NLG) tasks. However, many existing studies have
shown that they could be misused to generate undesired content. In response,
before releasing LLMs for public access, model developers usually align those
language models through Supervised Fine-Tuning (SFT) or Reinforcement Learning
with Human Feedback (RLHF). Consequently, those aligned large language models
refuse to generate undesired content when facing potentially harmful/unethical
requests. A natural question is "could alignment really prevent those
open-sourced large language models from being misused to generate undesired
content?''. In this work, we provide a negative answer to this question. In
particular, we show those open-sourced, aligned large language models could be
easily misguided to generate undesired content without heavy computations or
careful prompt designs. Our key idea is to directly manipulate the generation
process of open-sourced LLMs to misguide it to generate undesired content
including harmful or biased information and even private data. We evaluate our
method on 4 open-sourced LLMs accessible publicly and our finding highlights
the need for more advanced mitigation strategies for open-sourced LLMs.