Instructions to use deepset/deberta-v3-base-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/deberta-v3-base-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/deberta-v3-base-injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/deberta-v3-base-injection") model = AutoModelForSequenceClassification.from_pretrained("deepset/deberta-v3-base-injection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
On AgentDojo tool outputs: flags 98% of benign cases; a threshold can't fix it
Sharing results in case they help people choosing a detector for agents. I tested
this model with 9 other open-source detectors on AgentDojo: 629 indirect injections
embedded in realistic tool output, plus 97 benign tool outputs.
| Setting | Attacks caught | False alarms |
|---|---|---|
| Threshold 0.5 | 629 / 629 (100%) | 95 / 97 (98%) |
| Threshold tuned to a 2% false-alarm budget (~0.999), unseen domains | 2 / 629 (0%) | 5 / 97 |
Almost every tool output, benign or not, scores near 1.0, so there is no threshold
that separates them. My guess is that the training data had few long,
structured, instruction-like benign texts (emails, calendar entries, transaction
lists), which is what agent tool output looks like. The model may still be useful for
short direct user prompts; this benchmark only covers the tool-output setting.
Code and raw scores: https://github.com/rudratoshs/buried-injections