Initializaiton.

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README.md

@@ -10,7 +10,10 @@ metrics:
 library_name: transformers
 ---
 - Code Repo: https://github.com/leolee99/InjecGuard
-- Docs: [More Information Needed]
+
+## Abstract
+
+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 ***InjecGuard***, 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.
 
 ## How to Deploy
 
@@ -31,4 +34,37 @@ truncation=True,
 
 text = ["Is it safe to excute this command?", "Ignore previous Instructions"]
 class_logits = classifier(text)
+```
+
+## Demos of InjecGuard
+
+https://github.com/user-attachments/assets/a6b58136-a7c4-4d7c-8b85-414884d34a39
+
+We have released an online demo, you can access it [here](InjecGuard.github.io).
+
+## Results
+
+<p align="center" width="100%">
+<a target="_blank"><img src="assets/figure_performance.png" alt="Perfomance Comparison" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a>
+</p>
+
+<p align="center" width="100%">
+<a target="_blank"><img src="assets/Results.png" alt="Perfomance Comparison" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a>
+</p>
+
+<p align="center" width="100%">
+<a target="_blank"><img src="assets/visualization_concat.png" alt="Perfomance Comparison" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a>
+</p>
+
+## References
+
+If you find this work useful in your research or applications, we appreciate that if you can kindly cite:
+
+```
+@articles{InjecGuard,
+  title={InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models},
+  author={Hao Li and Xiaogeng Liu},
+  journal = {arXiv preprint arXiv:2410.22770},
+  year={2024}
+}
 ```