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.
💡 What You Will Learn
An LLM firewall open source sits between users and your LLM, filtering prompts and outputs. We compare llm-guard, NeMo Guardrails and superagent.
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
| Tool | Focus | Stars |
|---|---|---|
| llm-guard | Injection + PII | 3,202 |
| NeMo Guardrails | Policy rails | 6,880 |
| superagent | Injection protection | 6,700 |
| Guardrails AI | Output validation | 7,258 |
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.
