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What is LLM Guard?

LLM Guard provides a comprehensive array of safety measures, such as sanitization, detection of harmful language, prevention of data leaks, and protection against prompt injection attacks, to guarantee that your interactions with large language models remain secure and protected. Designed for easy integration and deployment in practical settings, it operates effectively from the outset. While it is immediately operational, it's worth noting that our team is committed to ongoing improvements and updates to the repository. The core functionalities depend on only a few essential libraries, and as you explore more advanced features, any additional libraries required will be installed automatically without hassle. We prioritize a transparent development process and warmly invite contributions to our project. Whether you're interested in fixing bugs, proposing new features, enhancing documentation, or supporting our cause, we encourage you to join our dynamic community and contribute to our growth. By participating, you can play a crucial role in influencing the future trajectory of LLM Guard, making it even more robust and user-friendly. Your engagement not only benefits the project but also enriches the overall experience for all users involved.

What is Jozu?

Jozu serves as an AI-powered platform dedicated to enhancing the security of supply chains by confirming the integrity of artifacts before they are executed, overseeing agent activities in real-time, and keeping a detailed log of all subsequent actions. The Jozu Hub functions as a self-contained repository for models, agents, MCP servers, and skills, guaranteeing that each artifact is integrated with cryptographic signatures, attestations, comprehensive scans, policy compliance, and audit records. This platform's security assessment, specifically designed for AI applications, tackles numerous threats such as hidden executable code within model packages, compromised weights, data poisoning, prompt injection, insecure tools, and licensing breaches. Users can establish policies once, which are then distributed as signed OCI artifacts, with enforcement occurring during the actions of pulling, promoting, admitting, or executing these artifacts. Furthermore, Jozu Agent Guard collaborates seamlessly with workloads across servers, desktops, edge devices, and isolated systems, applying local filtering for prompts and input-output, implementing access controls for tools, requiring approvals, and enforcing policies in real-time. By adopting this all-encompassing strategy, Jozu significantly boosts security while also providing a resilient framework for managing and protecting AI-related artifacts throughout their entire lifecycle. Ultimately, this ensures that users can trust the integrity and compliance of their AI systems.

Media

Media

Integrations Supported

Python

Integrations Supported

Amazon EKS
Amazon Elastic Container Registry (ECR)
Azure Kubernetes Service (AKS)
Databricks
Docker
GitHub Actions
GitLab
Google Kubernetes Engine (GKE)
Harbor
Hugging Face
JFrog Artifactory
Jenkins
Kubeflow
Kubernetes
MLflow
Model Context Protocol (MCP)
Red Hat OpenShift
Sonatype Nexus Repository
VMware Tanzu

API Availability

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

LLM Guard

Company Website

llm-guard.com

Company Facts

Organization Name

Jozu

Date Founded

2023

Company Location

United States

Company Website

jozu.com

Categories and Features

AI Guardrails

Not specified

AI Security

Not specified

Categories and Features

AI Guardrails

Not specified

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