The Actively Maintained Rebuff Alternative for AI Agent Security
Rebuff pioneered multi-layer injection detection but is no longer actively maintained. Rune picks up where Rebuff left off — and goes further.
Why Teams Look for Rebuff Alternatives
Effectively abandoned — last meaningful commit in 2023
Rebuff's GitHub repository has had no significant updates since late 2023. The hosted API (rebuff.ai) is offline. New prompt injection techniques like crescendo attacks, multi-turn injection, and indirect injection via tool outputs have emerged since — none are covered by Rebuff's frozen detection patterns.
Injection-only scope — no broader threat coverage
Rebuff only detects prompt injection. It doesn't cover data exfiltration (encoded data in URLs), PII leaking in model outputs, secret exposure (API keys in responses), privilege escalation through tool abuse, or command injection. As agent threats diversify, injection-only tools cover a shrinking slice of the attack surface.
No managed platform — library only, no monitoring
Rebuff is a Python library that returns detection results in-process. There's no dashboard, no event history, no alerting, and no analytics. You can't answer 'what attacks have my agents seen this week?' without building your own logging and monitoring infrastructure.
No agent framework support or tool call awareness
Rebuff works at the text level — you pass a string, you get a classification. It has no concept of LangChain chains, CrewAI crews, MCP tool calls, or multi-step agent workflows. The attack surfaces that matter most for modern agents (tool arguments, inter-agent messages) are invisible to it.
Canary token approach has known bypasses
Rebuff's novel contribution was canary token leak detection — embedding hidden tokens in prompts to detect extraction. This is clever but has known bypasses: attackers can paraphrase content, extract meaning without copying tokens, or use tool calls to exfiltrate data through side channels that bypass text-level canary checks.
How Rune Solves These Problems
Actively maintained with continuous detection updates
Rune's detection patterns are continuously updated as new attack techniques emerge. New jailbreak patterns, multi-turn injection techniques, and tool-level attacks are added to the detection corpus weekly. You're always protected against the latest techniques — not frozen at a 2023 snapshot.
Full threat spectrum — not just injection
Beyond injection: data exfiltration detection (base64-encoded data in URLs, sensitive fields in tool args), PII scanning (SSN, credit card, email), secret detection (API keys, JWTs, connection strings), and privilege escalation monitoring. One platform covering the full agent threat model that Rebuff's injection-only approach can't address.
Managed platform with real-time dashboard
Every Rune plan — including the free 10K events/month tier — includes the full dashboard with real-time event stream, threat analytics, false positive management, and alerting. See what your agents are doing and what's being blocked, without building monitoring from scratch.
Framework-native middleware for 6 agent frameworks
Drop-in middleware for LangChain, OpenAI, Anthropic, CrewAI, MCP, and OpenClaw. Scans tool arguments before execution, tool return values for exfiltration, and inter-agent messages for injection — attack surfaces that Rebuff's text-level scanning never sees.
Sub-10ms multi-layer detection replaces Rebuff's approach
Rune's L1 regex (<3ms) + L2 vector similarity (5-10ms) + L3 LLM judge (ambiguous cases only) is a more robust version of Rebuff's multi-layer concept. Median overhead: 4-8ms for 95% of requests. Data exfiltration detection replaces canary tokens with broader, bypass-resistant detection.
Quick Comparison
| Feature | Rune | Rebuff |
|---|---|---|
| Maintenance status | Actively maintained — continuous updates | Abandoned — last meaningful update 2023 |
| Threat coverage | Injection, exfiltration, PII, secrets, escalation | Injection only |
| Hosted API availability | Dashboard + API fully operational | rebuff.ai is offline |
| Managed platform | Real-time dashboard on all tiers (including free) | Library only — no dashboard, no monitoring |
| Agent framework support | 6 frameworks with tool call scanning | Generic text-level detection only |
| Detection approach | Regex + vector similarity + LLM judge (continuously updated) | Heuristics + LLM + vector + canary tokens (frozen) |
| Data exfiltration detection | Dedicated scanner (encoded data, URL params, tool args) | Canary tokens only (known bypasses) |
| Latency overhead | 4-8ms median (local, multi-layer) | 100-500ms (LLM-based detection layers) |
You Should Switch If...
- You need actively maintained detection against evolving attack patterns
- You've outgrown a library and need a managed platform with monitoring
- You need protection beyond just prompt injection
- You're deploying agents with tool calls and multi-step workflows
- You want native framework integration instead of manual text scanning
How to Switch from Rebuff to Rune
- 1Install the Rune SDK: pip install runesec
- 2Replace Rebuff detection calls with Rune Shield middleware
- 3Migrate any custom canary token logic to Rune's data exfiltration detection
- 4Remove Rebuff from dependencies
- 5Verify detection with test injection and exfiltration payloads
Frequently Asked Questions
Is Rebuff still safe to use in production?
We'd advise against it. Rebuff hasn't been updated since 2023, and the hosted API (rebuff.ai) is offline. Its detection patterns don't cover post-2023 injection techniques like crescendo attacks, multi-turn injection, or tool-level exploitation. The existing detection still catches basic injection patterns, but the gap widens every month. For production agents, use an actively maintained tool.
Does Rune replace Rebuff's canary token approach?
Yes — with a more robust mechanism. Rebuff embedded hidden canary tokens in prompts to detect extraction. This is clever but has known bypasses (paraphrasing, tool-based side channels). Rune's data exfiltration scanner detects encoded data in URLs, sensitive fields in tool arguments, and exfiltration patterns in model outputs — covering the same ground without the bypass vulnerabilities of token-based approaches.
Rebuff was open source and free — what does Rune cost?
Rune's free tier includes 10,000 events/month with all detection layers and the full dashboard. No credit card required. Rebuff was also free and open source, but the trade-off was zero maintenance, no monitoring, and a frozen detection corpus. For most teams, the monitoring gap is a bigger risk than the licensing cost.
What credit does Rebuff deserve?
Rebuff was genuinely innovative. It pioneered the multi-layer detection approach (heuristics + LLM analysis + vector similarity) and the canary token concept for leak detection. Both ideas influenced how later tools — including Rune — approach detection. It's unfortunate the project wasn't maintained, because the core ideas were sound.
Other Alternatives
Lakera Guard Alternative
Lakera Guard was acquired by Palo Alto Networks and shifted enterprise. Rune is the independent, developer-first alternative.
Prompt Armor Alternative
Prompt Armor detects injection. Rune secures your entire agent — inputs, outputs, tool calls, and inter-agent communication.
LLM Guard Alternative
LLM Guard is a solid open-source starting point. Rune is what you upgrade to for production agent security.
Related Resources
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