How do I secure Meta Llama applications?

Integrate Wardstone Guard with your Meta Llama application to detect prompt injections, jailbreaks, harmful content, and data leakage. Wardstone validates inputs before they reach Llama and screens outputs before returning them to users.

What security risks does Meta Llama have?

White-Box Attacks: Open weights allow attackers to optimize adversarial inputs against the exact model architecture. Fine-Tuning Safety Degradation: Custom fine-tuning often inadvertently weakens built-in safety measures. No Provider Safety Net: Self-hosted models lack the safety filters that API providers implement.

Meta Llama + Wardstone

Secure Open-Weight AI

Secure your Llama 4 deployments with Wardstone Guard. Whether self-hosted or via cloud providers, protect open-weight models from adversarial attacks.

15 Supported Models· all protected with sub-30ms latency

Llama 4 Scout (109B)Llama 4 Maverick (400B)Llama 4 Behemoth (2T)Llama 3.3 70BLlama 3.2 90B VisionLlama 3.2 11B Vision+9 more

Try the Playground Visit Llama

Why Secure Llama?

White-Box Attacks

High Risk

Open weights allow attackers to optimize adversarial inputs against the exact model architecture.

Fine-Tuning Safety Degradation

High Risk

Custom fine-tuning often inadvertently weakens built-in safety measures.

No Provider Safety Net

Medium Risk

Self-hosted models lack the safety filters that API providers implement.

Security Considerations

1
Open weights mean attackers can study the model to craft targeted attacks
2
Fine-tuning can accidentally remove or weaken safety training
3
Self-hosting means no provider-side safety filters
4
Wardstone provides the safety layer that open-source models lack

How to Integrate

Add Wardstone to your inference stack
Install Wardstone SDK in your Llama-serving application.
Validate before inference
Screen all inputs before passing to your Llama model.
Post-process outputs
Scan model outputs for harmful content and data leakage.
Protect fine-tuned models
Apply Wardstone to custom fine-tuned variants that may have weakened safety.

Pricing Note

Llama is free to use. Your costs are infrastructure. Wardstone adds minimal overhead for maximum protection.

Secure Meta Llama with Wardstone

# Step 1: Check user input with Wardstone
curl -X POST "https://api.wardstone.ai/v1/detect" \
  -H "Authorization: Bearer YOUR_WARDSTONE_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text": "User message here"}'
 
# Response: { "prompt_attack": { "detected": false, ... } }
 
# Step 2: If safe, send to self-hosted Llama (vLLM/llama.cpp/TGI)
curl -X POST "http://localhost:8080/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-4-scout",
    "messages": [{"role": "user", "content": "User message here"}]
  }'
 
# Step 3: Check Llama response with Wardstone before returning to user

Common Use Cases

Self-hosted AI applications

On-premise enterprise deployments

Fine-tuned domain-specific models

Cost-optimized inference

Air-gapped environments

All Supported Llama Models

Wardstone Guard protects all Meta Llama models with the same comprehensive security coverage. Whether you're using the latest releases or legacy models still in production, every API call is protected.

Llama 4 Scout (109B)

Llama 4 Maverick (400B)

Llama 4 Behemoth (2T)

Llama 3.3 70B

Llama 3.2 90B Vision

Llama 3.2 11B Vision

Llama 3.2 3B

Llama 3.2 1B

Llama 3.1 405B

Llama 3.1 70B

Llama 3.1 8B

Code Llama 70B