  Security concerns for large language models (LLMs) have recently escalated,
focusing on thwarting jailbreaking attempts in discrete prompts. However, the
exploration of jailbreak vulnerabilities arising from continuous embeddings has
been limited, as prior approaches primarily involved appending discrete or
continuous suffixes to inputs. Our study presents a novel channel for
conducting direct attacks on LLM inputs, eliminating the need for suffix
addition or specific questions provided that the desired output is predefined.
We additionally observe that extensive iterations often lead to overfitting,
characterized by repetition in the output. To counteract this, we propose a
simple yet effective strategy named CLIP. Our experiments show that for an
input length of 40 at iteration 1000, applying CLIP improves the ASR from 62%
to 83%
