  Large language models play a crucial role in modern natural language
processing technologies. However, their extensive use also introduces potential
security risks, such as the possibility of black-box attacks. These attacks can
embed hidden malicious features into the model, leading to adverse consequences
during its deployment.
  This paper investigates methods for black-box attacks on large language
models with a three-tiered defense mechanism. It analyzes the challenges and
significance of these attacks, highlighting their potential implications for
language processing system security. Existing attack and defense methods are
examined, evaluating their effectiveness and applicability across various
scenarios.
  Special attention is given to the detection algorithm for black-box attacks,
identifying hazardous vulnerabilities in language models and retrieving
sensitive information. This research presents a methodology for vulnerability
detection and the development of defensive strategies against black-box attacks
on large language models.
