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    Copyright © 2026 NVIDIA Corporation

    NVIDIA

    nemoguard-jailbreak-detect

    Downloadable

    Industry leading jailbreak classification model for protection from adversarial attempts

    • LLM Safety
    • NIM
    • Prompt Injection
    • Safety and Moderation
    • llm security
    • nemo guardrails
    • nemotron
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    API ReferenceAPI Reference
    Accelerated by DGX Cloud

    Model Overview

    Description:

    This open Nemotron safety model, NemoGuard JailbreakDetect, was developed to detect attempts to jailbreak large language models. This model is ready for commercial use.

    This model is supported in the open NVIDIA NeMo Guardrails library, designed to simplify scalable AI guardrail orchestration for safeguarding agentic AI applications.

    License/Terms of Use:

    NVIDIA Open Model License

    Reference(s):

    Improved Large Language Model Jailbreak Detection via Pretrained Embeddings

    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. Either nv-embedqa-e5-v5 or 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):

    • Not Applicable (N/A)

    Supported Hardware Microarchitecture Compatibility:

    • x86
    • x64

    [Preferred/Supported] Operating System(s):

    • Windows
    • MacOS
    • Linux

    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

    • [Automated]

    Labeling Method by dataset

    • [Automated]

    Properties:
    520 entries, all comprised of jailbreak attempts.

    Wildjailbreak

    Link: https://huggingface.co/datasets/allenai/wildjailbreak
    Data Collection Method by dataset

    • Hybrid: Automated, Synthetic

    Labeling Method by dataset

    • [Automated]

    Properties:
    6387 total entries: 5721 benign prompts, 666 jailbreak attempts

    jackhao/jailbreak-classification

    Link: https://huggingface.co/datasets/jackhhao/jailbreak-classification
    Data Collection Method by dataset

    • [Automated]

    Labeling Method by dataset

    • [Automated]

    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.

    ModelF1 ScoreFalse Positive RateFalse Negative Rate
    NemoGuard JailbreakDetect0.96010.00420.0435

    Inference:

    Engine: N/A
    Test Hardware:

    • RTX A6000
    • A100

    Ethical Considerations:

    NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications.
    When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

    For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

    On this page

    1. Description
      1. License/Terms of Use
    2. Reference(s)
    3. Model Architecture
    4. Input
    5. Output
    6. Software Integration
    7. Model Version(s)
    8. Training Dataset
      1. Advbench
      2. Wildjailbreak
      3. jackhao/jailbreak-classification
    9. Testing Dataset
    10. Evaluation Dataset
    11. Inference
    12. Ethical Considerations