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protectai
/
deberta-v3-base-prompt-injection-v2

Text Classification
Transformers
ONNX
Safetensors
English
deberta-v2
prompt-injection
injection
security
llm-security
Generated from Trainer
text-embeddings-inference
Model card Files Files and versions
xet
Community
9

Instructions to use protectai/deberta-v3-base-prompt-injection-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use protectai/deberta-v3-base-prompt-injection-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="protectai/deberta-v3-base-prompt-injection-v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("protectai/deberta-v3-base-prompt-injection-v2")
    model = AutoModelForSequenceClassification.from_pretrained("protectai/deberta-v3-base-prompt-injection-v2", device_map="auto")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Benchmark on AgentDojo: flags 27/27 attacks alone, 23% once embedded in tool output

#9 opened 1 day ago by
optionalrudra

A maintained, drop-in successor (same SAFE/INJECTION labels, better on benchmarks)

#8 opened 1 day ago by
Horizon-Labs

Eval dataset? Very poor performance.

👍 1
2
#7 opened 11 months ago by
ellenhp

using model with rust-bert

1
#5 opened almost 2 years ago by
telelvis

Retraining on new prompt injections?

➕ 6
3
#4 opened over 2 years ago by
AndrewACN

Hi, why i get bad results with your model ?

5
#2 opened over 2 years ago by
novak2
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