Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

TL;DR

This paper investigates prompt injection attack vulnerabilities in large language models and demonstrates that fine-tuning LLMs significantly improves detection accuracy, achieving over 99% in various metrics.

Contribution

The study introduces a fine-tuning approach for LLMs to effectively detect prompt injection attacks, outperforming zero-shot detection methods.

Findings

Fine-tuned LLM achieves 99.13% accuracy

Detection method attains 100% precision

Fine-tuning enhances detection efficiency

Abstract

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13\% accuracy, 100\% precision, 98.33\% recall and 99.15\% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks.