  The jailbreak attack can bypass the safety measures of a Large Language Model
(LLM), generating harmful content. This misuse of LLM has led to negative
societal consequences. Currently, there are two main approaches to address
jailbreak attacks: safety training and safeguards. Safety training focuses on
further training LLM to enhance its safety. On the other hand, safeguards
involve implementing external models or filters to prevent harmful outputs.
However, safety training has constraints in its ability to adapt to new attack
types and often leads to a drop in model performance. Safeguards have proven to
be of limited help. To tackle these issues, we propose a novel approach called
Self-Guard, which combines the strengths of both safety methods. Self-Guard
includes two stages. In the first stage, we enhance the model's ability to
assess harmful content, and in the second stage, we instruct the model to
consistently perform harmful content detection on its own responses. The
experiment has demonstrated that Self-Guard is robust against jailbreak
attacks. In the bad case analysis, we find that LLM occasionally provides
harmless responses to harmful queries. Additionally, we evaluated the general
capabilities of the LLM before and after safety training, providing evidence
that Self-Guard does not result in the LLM's performance degradation. In
sensitivity tests, Self-Guard not only avoids inducing over-sensitivity in LLM
but also can even mitigate this issue.
