Prompt Injection Attacks in Defended Systems

TL;DR

This paper explores black-box attack methods on large language models, evaluates defense mechanisms, and proposes strategies for detecting vulnerabilities and safeguarding NLP systems against malicious exploits.

Contribution

It introduces a methodology for vulnerability detection and defense strategies specifically tailored for black-box attacks on large language models.

Findings

Analysis of existing attack and defense methods

Identification of vulnerabilities in language models

Evaluation of detection algorithms' effectiveness

Abstract

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.