A framework that finds and masks personal data in text, images and tables. It detects the data, then redacts or replaces it so it can be safely sent to a model.
All repositories
Securityguardrails-ai
guardrails
A Python framework that checks LLM inputs and outputs with ready validators from Guardrails Hub and helps produce structured answers. It catches risks before a reply reaches the user.
Who it is for
For Python developers who need checks and a predictable format for model output.
How to start
- Install and configure:
pip install guardrails-ai, thenguardrails configure - Install a validator from the Hub, for example
pip install guardrails-ai-regex-match - Build a Guard from validators and wrap your model call with it
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
+137Sep 6 — Oct 6
Author's description
Adding guardrails to large language models.
More in «Security»
A vulnerability scanner for LLMs: it runs a model through a set of probes for prompt injection, data leakage, toxicity and other failures. Works as a command-line tool.
NVIDIA's toolkit for programmable rails in LLM-based conversational systems. Rules live in config and control what the bot talks about and does.
Microsoft's framework for proactively finding risks in generative AI systems. It helps automate red teaming against models and applications.
Meta's generative AI safety project: the Llama Guard and Prompt Guard filter models, the Code Shield scanner and the CyberSec Eval test suites. It pairs defense with attack-based testing.
A security scanner for AI agents, MCP servers and skills. It checks configs and SKILL.md files for risky spots.