Summary of Automatic and Universal Prompt Injection Attacks Against Large Language Models, by Xiaogeng Liu et al.
Automatic and Universal Prompt Injection Attacks against Large Language Models
by Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang, Chaowei Xiao
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) excel in processing and generating human language due to their ability to interpret and follow instructions. However, LLM-integrated applications are vulnerable to prompt injection attacks that manipulate the model’s responses by injecting malicious content. These attacks can deceive users into receiving unintended outputs. To address this threat, researchers need a unified understanding of prompt injection objectives and methods for assessing robustness. Our study introduces a framework for understanding attack goals and presents an automated gradient-based method for generating highly effective and universal prompt injection data, even when defensive measures are in place. This approach achieves superior performance compared to baselines with only five training samples. Our findings highlight the importance of gradient-based testing for evaluating defense mechanisms and avoiding overestimation of robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at understanding human language, but they can also be tricked into giving wrong answers if someone injects bad information. This is called a “prompt injection attack.” It’s like trying to get a computer program to do something it wasn’t supposed to do. To stop these attacks, we need to understand what’s going on and how to test defenses against them. Our study created a new way of understanding these attacks and developed a tool that can make the attacks more effective, even when someone tries to stop them. This shows that we need to be careful when using LLMs and that testing is important for making sure they are safe. |
Keywords
» Artificial intelligence » Prompt
