Check Point AI Guardrails vs LLM Guard

Check Point AI Guardrails analyzes large-language-model inputs and outputs for prompt injection, data leakage, policy bypass attempts, unsafe content, and related AI threats. It provides threat intelligence, analytics, and developer tooling for securing AI applications. Deployment can be on premises or in a private cloud, with processed personal data kept in the customer’s controlled environment except for agreed support or maintenance. Check Point Firewalls can apply the service to generative and agentic AI traffic, including content moderation and MCP tool responses. Listed supported services include OpenAI, Claude, Gemini, and Mistral AI through developer APIs. A Copilot Studio integration combines runtime guardrails with data-loss prevention and threat prevention. The cloud service may process prompts, messages, tool information, enabled uploads, model outputs, and security analytics. Pricing is on request; retention lasts through the subscription and three months afterward.

LLM Guard is a self-hosted security toolkit for checking interactions with large language models. It scans prompts and outputs for concerns such as prompt injection, secrets, personally identifiable information, toxicity, bias, malicious URLs, and factual consistency. It can sanitize interactions, detect harmful language, help prevent data leakage, and resist prompt injection attacks. The documentation includes examples for the OpenAI SDK, LangChain, LiteLLM, and LlamaIndex. In the OpenAI examples, prompts and responses are scanned; a streaming example also checks response chunks. LLM Guard can run as an API, and custom scanners can be added. Installation uses `pip install llm-guard` and requires Python 3.9 or higher. It is free and listed under the MIT License, with API, Linux, and self-hosted platforms. The repository was archived on July 9, 2026, is read-only, and is no longer actively developed or maintained. Production deployment assistance is directed to GitHub issues, Slack, and [email protected].