Rune vs LLM Guard: AI Security Tools Compared
Self-hosted scanner library vs framework-native runtime security
LLM Guard (by Protect AI) and Rune are both Python libraries for securing AI applications, but they differ in architecture and scope. LLM Guard provides a collection of input and output scanners that you call explicitly on text before and after LLM interactions. Rune wraps your existing agent client and scans transparently at the framework level — intercepting tool calls, agent messages, and client interactions.
LLM Guard takes a modular scanner approach: you pick which scanners to run (toxicity, bias, prompt injection, PII, etc.) and call them explicitly in your code. It uses a mix of NLP models and rules for detection. The library runs entirely locally with no cloud dependency.
Rune takes a different approach: instead of explicit scanner calls, it wraps your LLM client or agent framework and scans everything automatically. This means tool calls, inter-agent communication, and streaming responses are all covered without changing your agent's code.
Rune
Rune is a runtime security SDK that embeds into your AI agent framework. It wraps your LLM client (OpenAI, Anthropic) or hooks into middleware (LangChain, CrewAI, MCP) to scan every interaction. Multi-layer detection pipeline, YAML policy engine, and a real-time cloud dashboard for monitoring and alerting.
LLM Guard
LLM Guard is an open-source Python library from Protect AI that provides modular input and output scanners for LLM applications. Scanners include prompt injection detection, PII redaction, toxicity filtering, language detection, ban topics, and more. It runs entirely locally using NLP models (some scanners download models from HuggingFace). No cloud dependency. Protect AI also offers a commercial platform called Guardian for enterprise features.
Feature-by-Feature Comparison
Detection
| Feature | Rune | LLM Guard |
|---|---|---|
| Prompt injection | Pattern + semantic + optional LLM judge | ML model-based (DeBERTa fine-tuned) |
| PII detection | Regex-based scanning | NER model + regex (Presidio-based) |
| Toxicity/bias scanning | Policy-based content rules | ML model-based (dedicated classifiers) |
| Tool call scanning | Scans tool names, parameters, results | Text-only scanners — no tool awareness |
Architecture
| Feature | Rune | LLM Guard |
|---|---|---|
| Integration approach | Wraps LLM client — automatic scanning | Explicit scanner function calls |
| Cloud dependency | Scanning local; dashboard is cloud | Fully local — no cloud component |
| Model dependencies | No ML models for L1/L2; optional for L3 | Downloads HuggingFace models (several GB) |
| Multi-agent support | CrewAI, inter-agent flow monitoring | Not supported |
Operations
| Feature | Rune | LLM Guard |
|---|---|---|
| Monitoring dashboard | Real-time event stream and alerts | None built-in — DIY logging |
| Policy engine | YAML policies with action rules | Code-based scanner configuration |
| Open source | SDK open source; dashboard cloud | Fully open source (MIT license) |
When to Choose Rune
Transparent framework integration
Rune wraps your existing client — no explicit scanner calls to add throughout your code. LLM Guard requires you to call scan functions before and after every LLM interaction manually.
Tool and agent awareness
Rune sees tool calls, function parameters, and inter-agent communication. LLM Guard only scans text strings — it doesn't know what your agent is doing with tools.
Operational dashboard included
Rune includes real-time monitoring, alerting, and policy management. LLM Guard provides scanners only — you need to build your own monitoring infrastructure.
When to Choose LLM Guard
Fully local, fully open source
LLM Guard runs entirely locally with no cloud dependency whatsoever. If you can't have any data leaving your infrastructure — even asynchronous event shipping — LLM Guard is a better fit.
Rich NLP-based content scanning
LLM Guard has dedicated ML models for toxicity, bias, language detection, and sentiment analysis. If you need deep content quality scanning beyond security threats, LLM Guard's scanner library is broader.
Best For
Choose Rune if...
Teams building agents with tool access who want transparent security scanning, operational dashboards, and multi-agent support without changing their agent code.
Choose LLM Guard if...
Teams that need fully self-hosted, ML-based content scanning with no cloud dependency, especially for content safety (toxicity, bias) beyond security threats.
Frequently Asked Questions
Is LLM Guard harder to integrate than Rune?
LLM Guard requires explicit function calls around every LLM interaction — scan input, then call LLM, then scan output. Rune wraps your client once and everything is automatic. For complex agents with many tool calls, this difference is significant.
Does LLM Guard require downloading large models?
Yes. LLM Guard uses HuggingFace models for many scanners, which can total several gigabytes. Rune's L1 and L2 layers use pattern matching and lightweight semantic analysis — no large model downloads required.
Can I use LLM Guard for prompt injection detection specifically?
Yes, LLM Guard has a prompt injection scanner using a fine-tuned DeBERTa model. For prompt injection specifically, both tools are effective. Rune adds tool call scanning and multi-layer detection that goes beyond text classification.
Which is better for production monitoring?
Rune, by a significant margin. Rune includes a real-time dashboard with event streams, alerts, and policy management. LLM Guard provides scanner results but no built-in monitoring — you'd need to build that yourself.
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