# LLM Guard: The Security Toolkit for LLM Interactions

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LLM Guard is an open-source security toolkit developed by Protect AI, designed to fortify the safety of Large Language Models. It offers comprehensive protection against various threats, including prompt injection, data leakage, and harmful language, ensuring secure and reliable LLM interactions.

GitHub: https://github.com/protectai/llm-guard
OSRepos URL: https://osrepos.com/repo/protectai-llm-guard

## Summary

LLM Guard is an open-source security toolkit developed by Protect AI, designed to fortify the safety of Large Language Models. It offers comprehensive protection against various threats, including prompt injection, data leakage, and harmful language, ensuring secure and reliable LLM interactions.

## Topics

- llm-security
- prompt-injection
- large-language-models
- security-tools
- llmops
- Python
- AI Security
- Open Source

## Repository Information

Last analyzed by OSRepos: Fri Jun 26 2026 00:37:47 GMT+0100 (Western European Summer Time)
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## Content

## Introdução

LLM Guard by Protect AI is a comprehensive open-source security toolkit designed to fortify the safety of Large Language Models (LLMs). It provides essential capabilities for sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks. By integrating LLM Guard, developers can ensure that their interactions with LLMs remain safe and secure, protecting against various adversarial threats.

## Instalação

Getting started with LLM Guard is straightforward. You can easily install the package using pip:

sh
pip install llm-guard


Please ensure you are using Python version 3.9 or higher for optimal compatibility.

## Exemplos

LLM Guard is designed for easy integration into your projects. You can explore practical examples to see how it works:

*   **ChatGPT and LLM Guard**: Learn how to integrate LLM Guard with OpenAI's API for enhanced security.
*   **Deploy LLM Guard as API**: Discover how to deploy LLM Guard as a standalone API for your applications.

For more detailed guides and tutorials, refer to the [official documentation](https://protectai.github.io/llm-guard/ "LLM Guard Documentation" target="_blank").

## Porquê usar LLM Guard?

LLM Guard offers a robust solution for mitigating the unique security challenges associated with LLMs. It features an extensive array of prompt and output scanners that address various vulnerabilities:

*   **Prompt Scanners**: Includes tools for anonymization, banning specific code or competitors, detecting gibberish, identifying prompt injections, and scanning for secrets, sentiment, and toxicity.
*   **Output Scanners**: Provides capabilities for detecting bias, deanonymizing data, checking for malicious URLs, ensuring factual consistency, and scanning for sensitive information or toxicity in LLM responses.

By leveraging these scanners, LLM Guard ensures robust security for your LLM applications, protecting against both malicious inputs and undesirable outputs.

## Links

*   **GitHub Repository**: [https://github.com/protectai/llm-guard](https://github.com/protectai/llm-guard "LLM Guard GitHub Repository" target="_blank")
*   **Official Documentation**: [https://protectai.github.io/llm-guard/](https://protectai.github.io/llm-guard/ "LLM Guard Documentation" target="_blank")
*   **Playground**: [https://huggingface.co/spaces/ProtectAI/llm-guard-playground](https://huggingface.co/spaces/ProtectAI/llm-guard-playground "LLM Guard Playground" target="_blank")