Open-Weight LLM · Private & Custom AI
wildguard
Safety classifier for private AI systems: catch unsafe outputs before they reach users, built into your ops stack.
WildGuard is a 7B safety classifier purpose-built to detect unsafe content across multiple risk categories. For ops and private-AI teams, it's a guardrail layer—run it on your infrastructure to filter generated content, moderate user inputs, or audit internal AI workflows without relying on external APIs.
Model facts
Private deployment
Run wildguard in your own environment
Self-host on a single GPU (V100 or better). Deploy as a lightweight inference service (vLLM, TGI) behind your custom AI or agent pipeline. Data stays in your environment—classify sensitive outputs from agents, support bots, or document systems without exposing them to third-party moderation APIs. Gated model requires HF token approval.
Operational AI use cases
Support ticket flagging & escalation
Run WildGuard on inbound customer emails or chat before routing to human agents. Flag toxic, harmful, or sensitive content automatically; escalate high-risk tickets to supervisors. Reduces time spent triaging noise.
Internal knowledge base & LLM output filtering
When your internal AI assistant or chatbot generates responses from private documents, pipe outputs through WildGuard to catch inadvertent disclosures, offensive language, or policy violations before they're served to employees.
Compliance & audit logging for regulated workflows
Run safety checks on financial advice, legal summaries, or healthcare Q&A generated by internal LLMs. Log flagged instances for compliance audits; keep all data on-premises for regulatory review.
Custom AI
As a base for custom AI
Moderate fit as a base model. WildGuard is task-specific (safety classification), not a general-purpose LLM. Use it as a *component* in custom pipelines—wrap it in orchestration logic to build multi-stage content moderation systems, or fine-tune on domain-specific unsafe patterns (e.g., financial fraud language, medical misinformation). Not suitable for general text generation or reasoning tasks.
In the operating system
Where it fits
Guardrail / filtering layer in an ops AI stack. Sits downstream of agents and LLMs (in the output critique phase) or upstream (input validation). Complements knowledge retrieval and workflow orchestration layers; pairs well with agentic frameworks that need safety gates.
Data control & security
Self-hosting keeps classification decisions and flagged content in your environment—no external moderation vendor sees your data. Architecture choice, not a model guarantee. No encryption, compliance certifications, or audit logs built in; you own operational security (network isolation, access controls, logging). Valuable for orgs handling PII, trade secrets, or regulated content.
Hardware footprint
Estimate: 14–16 GB VRAM (fp16), 28–32 GB (fp32). Single A100-40GB or dual L40S. Inference latency ~100–200ms per sample on modern GPU. Batch inference recommended for support/ops bulk workflows.
Integration
Expose via FastAPI, TGI, or vLLM REST endpoint. Pipe LLM outputs (text) to WildGuard; receive risk scores and category labels. Integrate with orchestration layers (LangChain, LlamaIndex, custom agents) to auto-reject or flag high-risk completions. No pre-built connectors to Slack, Jira, or CRMs—build routing logic in your workflow engine.
When it's not the right fit
- —Your org needs real-time, sub-50ms moderation across 1000+ concurrent requests without significant infra investment.
- —You operate in domains with custom safety definitions WildGuard wasn't trained on (e.g., niche industry jargon, proprietary risk taxonomy).
- —You require HIPAA/SOC2 compliance docs or formal SLAs—WildGuard is open-weight, community-supported; no vendor backing.
Alternatives to consider
Meta Llama Guard 2 (8B)
Larger safety classifier; more nuanced risk taxonomy. Slightly higher latency, more VRAM. Also Apache 2.0, gated. Comparable ops fit.
OpenAI Moderation API (cloud)
Closed-source, no self-hosting. Fast, battle-tested, but data leaves your environment. Lower ops friction, higher dependency risk.
Perspective API (Google)
Free, cloud-based toxicity scorer. Simpler use cases, not safety-classification-focused. No private deployment option.
FAQ
Can I fine-tune WildGuard for my industry's specific risks?
Apache 2.0 permits it. You'd need to adapt training data and retraining pipeline. Requires deep learning expertise and labeled examples. Reach out to LLM.co if you need guidance on custom safety classifiers.
What happens if I want to commercialize a product using WildGuard?
Apache 2.0 allows commercial use. You must include copyright/license notices. No restrictions on selling applications that incorporate it. Verify attribution requirements with the license text.
How do I deploy this privately without internet connectivity?
Download model weights locally, run inference on an air-gapped GPU. Requires no external APIs or model calls. You own data; ensure your infra (network, access control) matches compliance needs.
Does WildGuard detect all types of harmful content?
Unknown without full model card and benchmarks. Trained on AllenAI's WildGuardMix dataset. Likely covers common categories (violence, sexual, illegal activity). Edge cases, adversarial inputs, or niche harms may slip through. Test against your threat model.
Build Private Safety into Your AI Stack
WildGuard is a powerful piece. But guardrails are just one layer. Talk to LLM.co about wiring safety classifiers, agents, and knowledge retrieval into a coherent ops AI system that runs on your infrastructure.