Paper shows how fine-tuning can make LLMs nearly immune to prompt injection attacks
Simple fine-tuning strategy boosts prompt injection detection from 55% to 99% accuracy.
📚 https://arxiv.org/abs/2410.21337v1
🎯 Original Problem:
LLMs are vulnerable to prompt injection attacks where malicious users manipulate input prompts to make models deviate from intended behavior, potentially causing data leaks, biased outputs, or harmful responses. This is ranked as the top security concern by OWASP for LLM applications.
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🔧 Solution in this Paper:
→ Used XLM-RoBERTa model with two approaches: zero-shot classification without fine-tuning and supervised fine-tuning
→ Fine-tuned the model on specialized dataset from Hugging Face containing 546 training instances and 116 test instances
→ Implemented early stopping to prevent overfitting
→ Used BERT tokenizer for input standardization
→ Trained over 50 epochs with optimized hyperparameters
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💡 Key Insights:
→ Fine-tuning dramatically improves prompt injection detection compared to zero-shot approaches
→ High performance convergence happens within first 10 epochs
→ Model stabilizes after 41 epochs with no significant improvements
→ Fine-tuning is essential for creating robust prompt injection detection systems
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📊 Results:
→ Non-fine-tuned model: 55.17% accuracy, 55.13% precision, 71.67% recall
→ Fine-tuned model: 99.13% accuracy, 100% precision, 98.33% recall, 99.15% F1-score
→ Outperforms existing approaches like Multilingual BERT (96.55% accuracy)
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