Llama Guard

Open LLM-based safeguard for classifying unsafe content in human-AI conversations.

4.6 (5)

Avaliado por Daniel Nikulshyn·Atualizado maio de 2026

Open Source Content Moderation LLM Self-Hosted AI Safety Developer Tools Guardrails

Visão geral

Llama Guard is a safety classifier built on top of Meta's Llama models, designed to evaluate both user prompts and model responses for potentially harmful content. It outputs a safety label along with the specific policy categories that were violated, making it useful as a guardrail layer around chatbots and other generative AI systems. The model is trained against a configurable taxonomy covering categories such as violence, sexual content, hate, self-harm, and criminal advice. Because the taxonomy is provided in the prompt itself, developers can adapt or extend the policy without retraining, tailoring moderation to their specific application or jurisdiction. Distributed with open weights, Llama Guard can be self-hosted alongside an LLM pipeline to filter inputs and outputs in real time, offering an alternative to closed moderation APIs for teams that need transparency, customization, or on-premise deployment.

Funcionalidades principais

LLM-based input and output moderation
Multi-category harm classification
Prompt-configurable policy taxonomy
Open-source weights from Meta
Compatible with Llama and other LLM stacks
Returns safe/unsafe label with violated categories

Casos de uso

Chatbot input and output moderation

Wrap a production chatbot with Llama Guard to screen user prompts and model responses, blocking unsafe content before it reaches end users.

Custom policy enforcement

Adapt the prompt-based taxonomy to match an application's specific policies or jurisdictional requirements without retraining the safety model.

Self-hosted compliance layer

Deploy open weights on-premises to audit and moderate LLM traffic in regulated environments where data cannot leave internal infrastructure.

Red-teaming and dataset filtering

Use Llama Guard to label conversation datasets for unsafe categories, supporting safety evaluations, fine-tuning data curation, and red-team analysis.

Prós e contras

Prós

Open weights enable self-hosting and auditing
Customizable safety taxonomy via prompt
Classifies both user inputs and model outputs
Integrates easily into existing LLM pipelines

Contras

Requires GPU resources to run efficiently
May produce false positives or miss nuanced harms
Setup and tuning expertise needed
English-centric performance

Avaliações

4.6

Média de 5 avaliações.

Entra para deixar uma avaliação.

Tomáš Novák

Use it every day

Honestly didn't expect to like it this much. Compatible with Llama and other LLM stacks is exactly what I needed, and integrates easily into existing LLM pipelines. but I reach for it almost every day now and it just clicks.

Ingrid Bauer

Solid for our team

We rolled this out across the team last quarter and open weights enable self-hosting and auditing. LLM-based input and output moderation fits neatly into how we already work, and compatible with Llama and other LLM stacks removed a step we used to do by hand. but it has held up under daily use.

Tariq Aziz

Use it every day

Honestly didn't expect to like it this much. Compatible with Llama and other LLM stacks is exactly what I needed, and open weights enable self-hosting and auditing. but I reach for it almost every day now and it just clicks.

Daniel Schmidt

Years in this space

I've evaluated a lot of these over the years. What stands out here is compatible with Llama and other LLM stacks — handled better than most — and open weights enable self-hosting and auditing. Requires GPU resources to run efficiently is my one real gripe. Worth the time if this is your use case.

Aaliyah Johnson

Compared a few options

Evaluated this against two competitors. Where it wins: lLM-based input and output moderation and integrates easily into existing LLM pipelines. Where it lags: english-centric performance. On balance the feature set — especially lLM-based input and output moderation — justifies the 4 stars for our use case.