Text Classification
Transformers
ONNX
Safetensors
English
deberta-v2
prompt-injection
injection
security
llm-security
Generated from Trainer
text-embeddings-inference
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
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