Text Classification
Transformers
Safetensors
English
deberta-v2
security
prompt
cyber-security
llm-security
prompt-injection
path-traversal
text-embeddings-inference
Instructions to use edaerer/promptwaf-path-traversal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edaerer/promptwaf-path-traversal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edaerer/promptwaf-path-traversal")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edaerer/promptwaf-path-traversal") model = AutoModelForSequenceClassification.from_pretrained("edaerer/promptwaf-path-traversal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -28,7 +28,7 @@ The model identifies prompts containing directory escape patterns (`../`, `..\\`
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- **Architecture**: BERT (Base)
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- **Task**: Binary Sequence Classification
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- **Training**:
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- **Labels**:
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- `0` → Safe/Benign
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- `1` → Path Traversal Attack
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- **Architecture**: BERT (Base)
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- **Task**: Binary Sequence Classification
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- **Training Data**: Trained on a custom, internally curated path traversal dataset
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- **Labels**:
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- `0` → Safe/Benign
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- `1` → Path Traversal Attack
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