LLM Firewall Open Source: Protect Your AI Apps from Injection

An LLM firewall open source sits between users and your LLM, filtering prompts and outputs. We compare llm-guard, NeMo Guardrails and superagent.

📜 Table of Contents

• The Contenders
• Comparison
• FAQ

Your LLM will happily reveal its system prompt, leak data, or follow injected instructions if you let raw user input reach it. An LLM firewall open source project adds a filter layer: sanitize input, detect injection, redact sensitive output - before and after every model call.

The Contenders

llm-guard (protectai/llm-guard, 3,202 stars) is the security toolkit focused on prompt injection detection, PII redaction, and output sanitization - drops the request or flags it. NVIDIA NeMo Guardrails (NVIDIA/NeMo-Guardrails, 6,880 stars) is the larger framework: rails are programmable rules that check input and output against your policy, with conversation flow control. superagent (superagent-ai/superagent, 6,700 stars) protects AI applications against prompt injection with a lightweight API.

Deployment pattern: place the firewall in front of your LLM gateway (e.g. LiteLLM), log every blocked request, and start with conservative rules - block obvious injection patterns first, then tune. False positives are the main cost; measure them with a sample of legitimate traffic.

Comparison

FAQ

Q: Is an LLM firewall enough for security?
A: No - it is one layer. Combine with least-privilege API keys, input length limits, and human review for high-risk actions.

Q: How do I reduce false positives?
A: Start with narrow rules, log everything, review weekly, and only auto-block the patterns you are confident about.