Rune vs Rebuff: Prompt Injection Detection Compared
Open-source injection detector vs comprehensive agent security platform
Rebuff and Rune both protect AI applications from prompt injection, but at very different scales. Rebuff is a focused, open-source prompt injection detection library that uses multiple detection methods: heuristics, LLM-based analysis, and a vector database of known attacks. Rune is a comprehensive runtime security SDK for AI agents that covers injection plus data exfiltration, tool manipulation, credential exposure, and policy violations.
Rebuff was one of the early open-source projects to tackle prompt injection detection specifically. It combines rule-based heuristics, an LLM classifier, and a vector database that stores known injection patterns for similarity matching. It's lightweight and focused on doing one thing well.
Rune builds on a similar multi-layer detection philosophy but expands scope significantly: framework-native integration, tool call scanning, multi-agent monitoring, YAML policy engine, and a real-time cloud dashboard. Rune is designed for production agent deployments where you need operational visibility alongside detection.
Rune
Rune is a production-grade runtime security SDK for AI agents. It wraps your LLM client or agent framework, scanning every interaction through a multi-layer pipeline. Beyond injection detection, it covers data exfiltration, tool manipulation, credential exposure, and policy violations. Includes a real-time dashboard for monitoring and alerting.
Rebuff
Rebuff is an open-source Python library for detecting prompt injection. It uses a multi-layer approach: heuristic rules, LLM-based classification, and vector database similarity matching against known injection patterns. It's designed to be self-hosted and requires an LLM API key and a vector database (Pinecone) for full functionality. Rebuff is a community project with limited active maintenance.
Feature-by-Feature Comparison
Detection
| Feature | Rune | Rebuff |
|---|---|---|
| Prompt injection detection | Pattern + semantic + optional LLM judge | Heuristics + LLM + vector database |
| Data exfiltration detection | URL, PII, and credential scanning | Injection-only — no exfiltration |
| Tool call scanning | Scans all tool interactions | Not supported |
Architecture
| Feature | Rune | Rebuff |
|---|---|---|
| External dependencies | No required external services for L1/L2 | Requires LLM API + Pinecone vector DB |
| Framework integration | Native wrappers for 5 frameworks | Generic Python function calls |
| Production readiness | Maintained, documented, supported | Community project, limited maintenance |
Operations
| Feature | Rune | Rebuff |
|---|---|---|
| Monitoring dashboard | Real-time event stream and alerts | None — logging only |
| Policy engine | YAML policies with configurable actions | Threshold-based configuration |
| Open source | SDK open source; dashboard cloud | Fully open source |
When to Choose Rune
Production-grade with active maintenance
Rune is actively maintained, documented, and supported. Rebuff is a community project with limited ongoing development. For production workloads, active maintenance matters.
Comprehensive threat coverage
Rune detects injection, exfiltration, credential exposure, tool manipulation, and policy violations. Rebuff only detects prompt injection. Real-world attacks often combine multiple techniques.
No external service dependencies for core scanning
Rune's L1/L2 scanning works with no external services. Rebuff's full functionality requires an LLM API key and a Pinecone vector database — adding cost and complexity.
When to Choose Rebuff
Fully open source and self-hosted
Rebuff is entirely open source with no cloud component. If you want to fork, modify, and self-host everything with full code access, Rebuff provides that flexibility.
Vector database approach to injection detection
Rebuff's use of a vector database for similarity matching against known injections is a unique approach. If you want to build and curate your own injection pattern database, Rebuff's architecture supports this.
Best For
Choose Rune if...
Teams building production AI agents who need comprehensive security scanning, framework integration, and operational dashboards.
Choose Rebuff if...
Developers experimenting with prompt injection detection who want a fully open-source library to learn from or fork.
Frequently Asked Questions
Is Rebuff still actively maintained?
Rebuff has limited active maintenance as a community project. For production workloads, you'll want an actively maintained solution like Rune with ongoing security updates and support.
Does Rebuff require a vector database?
For its full detection pipeline, yes. Rebuff uses Pinecone for storing and matching against known injection patterns. Without it, you lose one detection layer. Rune's core scanning requires no external services.
Can I switch from Rebuff to Rune easily?
Yes. Replace Rebuff's detect_injection() calls with Rune's framework wrapper. Rune's integration is typically simpler since it wraps your client automatically rather than requiring explicit function calls.
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