Model Overview
Description:
NemoGuard JailbreakDetect was developed to detect attempts to jailbreak large language models.
This model is ready for commercial use.
License/Terms of Use:
NVIDIA Open Model License
Reference(s):
Model Architecture:
Architecture Type: Random Forest
Network Architecture: N/A
Input:
Input Type(s): Text Embedding
Input Parameters: 768 dimensional vector
Input Format(s): Vector
Other Properties Related to Input: Must be an output from the corresponding embedding model,
snowflake-arctic-m-long.
Output:
Output Type(s): Classification, Probability
Output Format: Bool, Float
Output Parameters: 1D
Other Properties Related to Output: N/A
Software Integration:
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s):
Model Version(s):
NemoGuard-JailbreakDetect-v1.0: Jailbreak detection model using Snowflake-arctic-embed-m embeddings
Training, Testing, and Evaluation Datasets:
Training Dataset:
A combination of three open datasets, mixed together, de-duplicated, and reviewed for data quality.
Jailbreak data was augmented with the use of
garak.
The datasets used are outlined below:
Advbench
Link: https://github.com/thunlp/Advbench
** Data Collection Method by dataset
** Labeling Method by dataset
Properties:
520 entries, all comprised of jailbreak attempts.
Wildjailbreak
- Hybrid: Automated, Synthetic
** Labeling Method by dataset
Properties:
6387 total entries: 5721 benign prompts, 666 jailbreak attempts
jackhao/jailbreak-classification
** Labeling Method by dataset
Properties:
1306 total entries: 640 benign prompts, 666 jailbreak attempts
Testing Dataset:
A stratified subset (20%) of the aggregate dataset was used for testing.
Evaluation Dataset:
Evaluated on
JailbreakHub.
| Model | F1 Score | False Positive Rate | False Negative Rate |
|---|
| NemoGuard JailbreakDetect | 0.9601 | 0.0042 | 0.0435 |
Inference:
Engine: N/A
Test Hardware:
Ethical Considerations:
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