Making Harmful Behaviors Unlearnable for Large Language Models
Xin Zhou Yi Lu Ruotian Ma Tao Gui Qi Zhang Xuanjing Huang
Large language models (LLMs) have shown great potential as general-purpose AI
assistants in various domains. To meet the requirements of different
applications, LLMs are often customized by further fine-tuning. However, the
powerful learning ability of LLMs not only enables them to acquire new tasks
but also makes them susceptible to learning undesired behaviors. For example,
even safety-aligned LLMs can be easily fine-tuned into harmful assistants as
the fine-tuning data often contains implicit or explicit harmful content. Can
we train LLMs on harmful data without learning harmful behaviors? This paper
proposes a controllable training framework that makes harmful behaviors
unlearnable during the fine-tuning process. Specifically, we introduce
``security vectors'', a few new parameters that can be separated from the LLM,
to ensure LLM's responses are consistent with the harmful behavior. Security
vectors are activated during fine-tuning, the consistent behavior makes LLM
believe that such behavior has already been learned, there is no need to
further optimize for harmful data. During inference, we can deactivate security
vectors to restore the LLM's normal behavior. The experimental results show
that the security vectors generated by 100 harmful samples are enough to
prevent LLM from learning 1000 harmful samples, while preserving the ability to
learn other useful information.