# A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models

**Authors:** Daniel Wankit Yip, Aysan Esmradi, Chun Fai Chan  
**arXiv:** [2401.00991](https://arxiv.org/abs/2401.00991)  
**Date:** 2024-01-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.00991v1/

## Key points

- Prompt injection attacks exploit vulnerabilities in large language models (LLMs) to manipulate the model into unintended actions or generate malicious content.
- LLM-integrated applications are increasingly at risk of prompt injection attacks as they become more prevalent.
- A novel evaluation framework was introduced to assess application resilience against prompt injection attacks, focusing on representativeness, interpretability, and robustness.
- The framework involved a meticulous selection process of 115 simulated attacks based on coverage and relevance to ensure representativeness.
- Responses generated from these simulated attacks were evaluated using a second LLM to provide scores and explanations for enhanced interpretability.
- A resilience score was computed by assigning higher weights to attacks with greater impact, offering a robust measurement of application resilience.
- Testing the framework on two LLMs - Llama2 and ChatGLM - showed that Llama2 exhibited higher resilience compared to ChatGLM, aligning with the idea that newer models tend to have greater resilience.
- The framework demonstrated versatility by requiring minimal adjustments to accommodate emerging attack techniques and classifications, making it adaptable for future threats.
- Future work can extend the framework to include additional attack techniques and categories as new threats emerge in cybersecurity.

## Summary

Prompt injection attacks exploit vulnerabilities in large language models (LLMs) to manipulate the model into unintended actions or generate malicious content. As LLM-integrated applications become more prevalent, they are increasingly at risk of such attacks. This study introduces a novel evaluation framework for assessing the resilience of applications against prompt injection attacks. The framework incorporates innovative techniques to ensure representativeness, interpretability, and robustness. To ensure the representativeness of simulated attacks on the application, a meticulous selection process was employed to choose 115 attacks based on coverage and relevance. A second LLM was used to evaluate the responses generated from these simulated attacks, providing scores accompanied by explanations for enhanced interpretability. A resilience score was then computed by assigning higher weights to attacks with greater impact, offering a robust measurement of application resilience. The efficacy of the framework was tested on two LLMs - Llama2 and ChatGLM. Results showed that Llama2 exhibited higher resilience compared to ChatGLM, aligning with the notion that newer models tend to have greater resilience. The framework demonstrated exceptional versatility by requiring minimal adjustments to accommodate emerging attack techniques and classifications. In future work, the framework can be extended to include additional attack techniques and categories as new threats emerge. The architecture of the framework allows for easy adaptation to build a testbed software for evaluating different attacks automatically and consolidating results. Overall, this study provides valuable insights that empower organizations to make informed decisions in fortifying their applications against potential threats from prompt injection attacks in the ever-evolving landscape of cybersecurity.