NemoGuard JailbreakDetect Overview

Description

NemoGuard JailbreakDetect is a random forest model trained by NVIDIA on snowflake-arctic-embed-m-long embeddings to detect attempts to jailbreak large language models. At the time of release, it is the best known publicly available model for detecting LLM jailbreak attempts.

This container includes two models: the NemoGuard JailbreakDetect random forest classifier, and the snowflake-arctic-embed-m-long embedding model.

The container components are ready for commercial/non-commercial use.

Third-Party Community Consideration

This container includes a model that is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see snowflake-arctic-embed-m-long model card.

License/Terms of Use:

GOVERNING TERMS: Use of the NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; use of this model is governed by the NVIDIA Community Model License.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

Deployment Geography:

Global

Release Date:

Build.Nvidia.com [08/14/2025]

\nHugging Face [01/15/2025]

\nNGC [08/14/2025]

Program Classes:

For example: The NemoGuard JailbreakDetect Container includes the following models:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
NemoGuard JailbreakDetect random forest classifier	Intended to be deployed as a guardrail in an LLM system, to scan user-provided prompts for jailbreaking attempts prior to sending those prompts to an LLM.	Automatic
snowflake-arctic-embed-m-long embedding model	Provides input embeddings of user prompts which are then used by the NemoGuard JailbreakDetect random forest classifier above.	Automatic

Get Help

NVIDIA Developer Community Forum

For support, Visit the NVIDIA Developer Community Forum

NVIDIA NIM Documentation

Visit the NIM Documentation for general information about using NIM, including an overview and deployment guides. Refer to the NemoGuard NIM Documentation for more information about the JailbreakDetect NIM, including a quickstart guide and API reference.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Deployment Details:

Setup

One-time access setup, as needed:

\ndocker login nvcr.io # ensure you login with the right key. Sometimes, if you've used another key in the past, this will just succeed without asking you for your new key. In this case, delete the config file it caches creds in, and try again.\n\n# Username: $oauthtoken \n# Password: \n\">

export NGC_API_KEY=<YOUR NGC API KEY>  \ndocker login nvcr.io  # ensure you login with the right key. Sometimes, if you've used another key in the past, this will just succeed without asking you for your new key. In this case, delete the config file it caches creds in, and try again.\n\n# Username: $oauthtoken  \n# Password: <NGC_API_KEY>  \n

Serving the model as a NIM

We provide the JailbreakDetect model as an NVIDIA NIM, so you can simply pull the image from docker and run it.

\nexport NIM_IMAGE='nvcr.io/nim/nvidia/nemoguard-jailbreak-detect:1.10.1' \nexport MODEL_NAME='nemoguard-jailbreak-detect' \ndocker pull $NIM_IMAGE \n\">

#!/bin/bash\n\nexport NGC_API_KEY=<your NGC personal key with access to the \"nim/nvidia\" org/team>  \nexport NIM_IMAGE='nvcr.io/nim/nvidia/nemoguard-jailbreak-detect:1.10.1'  \nexport MODEL_NAME='nemoguard-jailbreak-detect'  \ndocker pull $NIM_IMAGE  \n

And go!

docker run -it --name=$MODEL_NAME   \n    --gpus=all --runtime=nvidia   \n    -e NGC_API_KEY=\"$NGC_API_KEY\"   \n    --expose 8000   \n    $NIM_IMAGE  \n

Running inference with the NIM

The running NIM container exposes a standard REST API and you can send POST requests to the v1/classify endpoint as JSON to get model responses.

$ curl --data '{\"input\": \"hello this is a test\"}' --header \"Content-Type: application/json\" --header \"Accept: application/json\" http://0.0.0.0:8000/v1/classify  \n

This will return a JSON dictionary with the model’s prediction of whether or not the provided input is a jailbreaking attempt.

{\"jailbreak\": false, \"score\": -0.9921652427737031}  \n

Reference(s):

Additional details about the model, including comparisons to other public models are available in the accompanying paper, accepted to the 2025 AAAI workshop on AI for Cyber Security (AICS).

Container Version(s):

NemoGuard-JailbreakDetect-v1.10.1: Jailbreak detection model using Snowflake-arctic-embed-m embeddings

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 developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

End of Support — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"

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\n\nThis container includes two models: the NemoGuard JailbreakDetect random forest classifier, and the snowflake-arctic-embed-m-long embedding model.
\n\nThe container components are ready for commercial/non-commercial use.
\n\n## Third-Party Community Consideration
\nThis container includes a model that is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long) model card.\n\n### License/Terms of Use: \nGOVERNING TERMS: Use of the NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).
\n\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.
\n\n### Deployment Geography:\n\nGlobal
\n\n### Release Date:
\n\n[Build.Nvidia.com](https://build.nvidia.com/nvidia/nemoguard-jailbreak-detect) [08/14/2025]
\n[Hugging Face](https://huggingface.co/nvidia/NemoGuard-JailbreakDetect) [01/15/2025]
\n[NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/nemoguard-jailbreak-detect) [08/14/2025]
\n\n## Program Classes:
\n\nFor example: The NemoGuard JailbreakDetect Container includes the following models: \n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :---- | :---- | :---- |\n| [NemoGuard JailbreakDetect](https://huggingface.co/nvidia/NemoGuard-JailbreakDetect) random forest classifier | Intended to be deployed as a guardrail in an LLM system, to scan user-provided prompts for jailbreaking attempts prior to sending those prompts to an LLM. | Automatic |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long) embedding model | Provides input embeddings of user prompts which are then used by the NemoGuard JailbreakDetect random forest classifier above. | Automatic |\n\n## Get Help\n### NVIDIA Developer Community Forum\n\nFor support, Visit the [NVIDIA Developer Community Forum](https://forums.developer.nvidia.com/)\n\n### NVIDIA NIM Documentation\nVisit the [NIM Documentation](https://docs.nvidia.com/nim/) for general information about using NIM, including an overview and deployment guides. Refer to the [NemoGuard NIM Documentation](https://docs.nvidia.com/nim/nemoguard-jailbreakdetect/latest/index.html) for more information about the JailbreakDetect NIM, including a quickstart guide and API reference.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Deployment Details: \n\n### Setup\n\nOne-time access setup, as needed:\n\n``` \nexport NGC_API_KEY= \ndocker login nvcr.io # ensure you login with the right key. Sometimes, if you've used another key in the past, this will just succeed without asking you for your new key. In this case, delete the config file it caches creds in, and try again.\n\n# Username: $oauthtoken \n# Password: \n```\n\n### Serving the model as a NIM\n\nWe provide the JailbreakDetect model as an NVIDIA NIM, so you can simply pull the image from docker and run it.\n\n``` \n#!/bin/bash\n\nexport NGC_API_KEY= \nexport NIM_IMAGE='nvcr.io/nim/nvidia/nemoguard-jailbreak-detect:1.10.1' \nexport MODEL_NAME='nemoguard-jailbreak-detect' \ndocker pull $NIM_IMAGE \n```\n\nAnd go!\n\n``` \ndocker run -it --name=$MODEL_NAME \n --gpus=all --runtime=nvidia \n -e NGC_API_KEY=\"$NGC_API_KEY\" \n --expose 8000 \n $NIM_IMAGE \n```\n\n### Running inference with the NIM\n\nThe running NIM container exposes a standard REST API and you can send POST requests to the v1/classify endpoint as JSON to get model responses.\n\n``` \n$ curl --data '{\"input\": \"hello this is a test\"}' --header \"Content-Type: application/json\" --header \"Accept: application/json\" http://0.0.0.0:8000/v1/classify \n```\n\nThis will return a JSON dictionary with the model’s prediction of whether or not the provided input is a jailbreaking attempt.\n\n``` \n{\"jailbreak\": false, \"score\": -0.9921652427737031} \n```\n\n## Reference(s):\n\nAdditional details about the model, including comparisons to other public models are available in the [accompanying paper](https://arxiv.org/abs/2412.01547), accepted to the 2025 AAAI workshop on AI for Cyber Security (AICS).
\n\n## Container Version(s): \nNemoGuard-JailbreakDetect-v1.10.1: Jailbreak detection model using Snowflake-arctic-embed-m embeddings
\n\n## Ethical Considerations: \nNVIDIA 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 developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n**End of Support** — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"","displayName","NemoGuard JailbreakDetect",["D",1756401881039],"publisher","NVIDIA","latestVersion","versions",[20,266,262,264,260,257],"imageUrl","/api/image/nim/nvidia/containers/nemoguard-jailbreak-detect","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",10611214943,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[323],"displayLabels",[317,318],"policyLabels",[],"systemLabels",[309],"products",[],"hasNimMetadata",{"_280":310,"_311":310,"_312":313,"_314":315},"signed images","value","filter",{"_316":315,"_311":310},"category","system","field",{"_280":321,"_311":321,"_312":322,"_314":83},{"_280":319,"_311":319,"_312":320,"_314":83},"NSPECT-14WH-U832",{"_316":83,"_311":319},"Experimental",{"_316":83,"_311":321},{"_280":324,"_311":325,"_312":326,"_314":327},"NVIDIA NIM","nimmcro_nvidia_nim",{"_316":328,"_311":325},"feature","nvidia_nim",{"_166":1300,"_168":1301},"ngcApps",[1253],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1235":1236,"_1237":1238,"_1239":1240,"_1241":1242,"_1243":340,"_198":366,"_1244":130,"_1245":53,"_1246":86,"_1247":86,"_1248":1249,"_1250":86,"_1251":1252},"amplitude",{"_1232":1233,"_877":-7,"_1234":28},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1226":1227,"_1228":1229,"_1230":1231},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787590166955,"1.12.0","defaultSearchSuggestions",{"_1117":114,"_1118":1119,"_1120":1121,"_1122":1123},"announcements",[],"consentState",{"_403":1107,"_1108":86,"_1109":28,"_1110":28,"_1111":1112},"consentConfig",{"_385":386,"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404},"showContactAdmin",1787590166956,[],[],[380,381,382,383,384],{"_42":62,"_44":67,"_46":161,"_48":-5,"_50":-5,"_52":53},{"_42":62,"_44":65,"_46":161,"_48":-5,"_50":-5,"_52":53},{"_42":57,"_44":58,"_46":161,"_48":-5,"_50":-5,"_52":53},{"_42":54,"_44":55,"_46":161,"_48":-5,"_50":-5,"_52":53},{"_42":43,"_44":45,"_46":47,"_48":49,"_50":56,"_52":53},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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