  Ensuring the safety alignment of Large Language Models (LLMs) is crucial to
generating responses consistent with human values. Despite their ability to
recognize and avoid harmful queries, LLMs are vulnerable to "jailbreaking"
attacks, where carefully crafted prompts elicit them to produce toxic content.
One category of jailbreak attacks is reformulating the task as adversarial
attacks by eliciting the LLM to generate an affirmative response. However, the
typical attack in this category GCG has very limited attack success rate. In
this study, to better study the jailbreak attack, we introduce the DSN (Don't
Say No) attack, which prompts LLMs to not only generate affirmative responses
but also novelly enhance the objective to suppress refusals. In addition,
another challenge lies in jailbreak attacks is the evaluation, as it is
difficult to directly and accurately assess the harmfulness of the attack. The
existing evaluation such as refusal keyword matching has its own limitation as
it reveals numerous false positive and false negative instances. To overcome
this challenge, we propose an ensemble evaluation pipeline incorporating
Natural Language Inference (NLI) contradiction assessment and two external LLM
evaluators. Extensive experiments demonstrate the potency of the DSN and the
effectiveness of ensemble evaluation compared to baseline methods.
