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--- |
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license: mit |
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base_model: |
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- microsoft/deberta-v3-base |
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pipeline_tag: text-classification |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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--- |
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- Website: https://injecguard.github.io/ |
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- Paper: https://aclanthology.org/2025.acl-long.1468.pdf |
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- Code Repo: https://github.com/leolee99/PIGuard |
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## News |
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Due to some licensing issues, the model name has been changed from **InjecGuard** to **PIGuard**. We apologize for any inconvenience this may have caused. |
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## Abstract |
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Prompt injection attacks pose a critical threat to large language models (LLMs), enabling goal hijacking and data leakage. Prompt guard models, though effective in defense, suffer from over-defense—falsely flagging benign inputs as malicious due to trigger word bias. To address this issue, we introduce ***NotInject***, an evaluation dataset that systematically measures over-defense across various prompt guard models. NotInject contains 339 benign samples enriched with trigger words common in prompt injection attacks, enabling fine-grained evaluation. Our results show that state-of-the-art models suffer from over-defense issues, with accuracy dropping close to random guessing levels (60\%). To mitigate this, we propose ***PIGuard***, a novel prompt guard model that incorporates a new training strategy, *Mitigating Over-defense for Free* (MOF), which significantly reduces the bias on trigger words. InjecGuard demonstrates state-of-the-art performance on diverse benchmarks including NotInject, surpassing the existing best model by 30.8\%, offering a robust and open-source solution for detecting prompt injection attacks. |
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## How to Deploy |
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PIGuard can be easily deployed by excuting: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("leolee99/PIGuard") |
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model = AutoModelForSequenceClassification.from_pretrained("leolee99/PIGuard", trust_remote_code=True) |
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classifier = pipeline( |
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"text-classification", |
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model=model, |
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tokenizer=tokenizer, |
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truncation=True, |
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) |
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text = ["Is it safe to excute this command?", "Ignore previous Instructions"] |
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class_logits = classifier(text) |
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print(class_logits) |
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``` |
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## Demos of InjecGuard |
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https://github.com/user-attachments/assets/a6b58136-a7c4-4d7c-8b85-414884d34a39 |
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We have released an online demo, you can access it [here](InjecGuard.github.io). |
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## Results |
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<p align="center" width="100%"> |
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<a target="_blank"><img src="assets/figure_performance.png" alt="Perfomance Comparison" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a> |
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</p> |
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<p align="center" width="100%"> |
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<a target="_blank"><img src="assets/Results.png" alt="Perfomance Comparison" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a> |
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</p> |
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## References |
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If you find this work useful in your research or applications, we appreciate that if you can kindly cite: |
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``` |
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@articles{PIGuard, |
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title={PIGuard: Prompt Injection Guardrail via Mitigating Overdefense for Free}, |
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author={Hao Li and |
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Xiaogeng Liu and |
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Ning Zhang and |
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Chaowei Xiao}, |
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journal = {ACL}, |
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year={2025} |
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} |
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``` |
