A Comprehensive Assessment Tool for Prompt Injection Attacks in Large Language Models (LLMs)

Abstract

A prompt injection attack is a type of cyberattack that targets Large Language Models (LLMs), where attackers embed malicious instructions into an LLM with the intent of manipulating the model to the attacker's desire. Due to the relevance, growth, and demand of LLMs within small to medium-sized businesses, it is essential that they have access to a tool that can comprehensively assess model vulnerabilities. Based on an established framework for Prompt Injection attacks, this paper introduces a tool that can simulate types of injection attacks across different widely used LLM models and versions, test different defenses against these attacks, and measure the performance and how vulnerable the LLM's responses are. Additionally, this paper incorporates cost analysis, CPU utilization metrics, and a prompt hardening methodology for LLM personas. The results of these experiments provide deeper insights into testing and a comparative analysis against commonly used LLMs today.

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