The Production-Ready LLM Guard Alternative for AI Agent Security
LLM Guard is a solid open-source starting point. Rune is what you upgrade to for production agent security.
Why Teams Look for LLM Guard Alternatives
Slowing maintenance — last PyPI release months ago
LLM Guard's GitHub activity and PyPI release cadence have slowed significantly since mid-2025. Security tools that stop updating become liabilities — new jailbreak techniques like crescendo attacks and multi-turn injection evolve weekly, and stale pattern databases miss them.
No agent awareness — text-in, text-out only
LLM Guard scans raw strings through individual scanner classes. It has no concept of tool calls, function arguments, inter-agent delegation, or multi-step agent workflows. When an attack arrives through a tool's return value (indirect injection), LLM Guard can't distinguish it from legitimate data.
No dashboard, alerting, or analytics
LLM Guard is a Python library that returns scan results in-process. There's no managed dashboard, no event history, no alerting, and no analytics. You have to build logging, monitoring, and incident response infrastructure yourself — which most teams never get around to.
ML classifiers add 50-200ms per scan
LLM Guard's transformer-based scanners (PromptInjection, BanTopics, Toxicity) load ML models into memory and run inference on every call. Measured overhead is 50-200ms per scanner depending on input length. For agents making 8-12 tool calls per session, this compounds to 0.5-2.4 seconds of added latency per conversation turn.
No data exfiltration or secret detection
LLM Guard focuses on input sanitization and output toxicity. It doesn't detect data exfiltration patterns (base64-encoded data in URLs), leaked API keys, database connection strings, or sensitive fields appearing in tool arguments. These are distinct threat categories that require purpose-built scanners.
How Rune Solves These Problems
Managed platform with real-time dashboard on every tier
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. No need to build monitoring infrastructure from scratch.
Framework-native middleware for 6 agent frameworks
Drop-in middleware for LangChain, OpenAI, Anthropic, CrewAI, MCP, and OpenClaw. `shield = Shield(client)` — three lines, zero changes to agent logic. Scans tool calls, tool outputs, and inter-agent messages automatically.
Sub-10ms overhead with multi-layer detection
Layer 1 (regex + patterns): <3ms, catches known injection templates. Layer 2 (vector similarity): 5-10ms, detects semantically similar attacks. Layer 3 (LLM judge): only fires for ambiguous cases (~5% of traffic). Median total: 4-8ms for 95% of requests — 10-50x faster than LLM Guard's ML classifiers.
Full threat spectrum beyond input sanitization
Beyond injection: data exfiltration detection (encoded data in URLs, sensitive fields in tool args), PII scanning (SSN, credit card, email patterns), secret detection (API keys, JWTs, connection strings), and privilege escalation monitoring. LLM Guard covers injection and toxicity; Rune covers the full agent threat model.
YAML policy engine for custom rules
Define organization-specific security policies: restrict which tools an agent can call, set rate limits on sensitive operations, require approval for high-risk actions. Policies are version-controlled and auditable. LLM Guard offers no equivalent — you get individual scanner toggles, not a policy engine.
Quick Comparison
| Feature | Rune | LLM Guard |
|---|---|---|
| Platform type | Managed platform with dashboard + SDK | Open-source Python library |
| Agent framework support | LangChain, OpenAI, Anthropic, CrewAI, MCP, OpenClaw | Generic text scanning — no framework awareness |
| Latency overhead (median) | 4-8ms (regex + vector, local) | 50-200ms (transformer inference per scanner) |
| Detection layers | 3 layers: regex, vector similarity, LLM judge | Single ML classifier per scanner |
| Dashboard & alerting | Real-time dashboard on all tiers (including free) | None — library returns in-process only |
| Data exfiltration detection | Dedicated scanner for encoded data, URL params, tool args | Not supported |
| Secret detection | API keys, JWTs, connection strings, private keys | Not supported |
| Custom policy engine | YAML policies (tool restrictions, rate limits, custom rules) | Individual scanner toggles only |
| Maintenance & updates | Continuous detection model updates | Slowing release cadence since mid-2025 |
| Self-hosted option | SDK runs locally (metadata-only telemetry to dashboard) | Fully self-hosted (no external calls) |
You Should Switch If...
- You're moving agents to production and need monitoring and alerting
- You need native support for LangChain, CrewAI, or MCP frameworks
- You need continuously updated detection against new attack patterns
- You want a managed platform instead of maintaining a self-hosted library
- Latency matters and ML-classifier overhead is impacting your agents
How to Switch from LLM Guard to Rune
- 1Install the Rune SDK: pip install runesec
- 2Replace LLM Guard scanner calls with Rune Shield middleware
- 3Map existing LLM Guard configurations to Rune YAML policies
- 4Remove LLM Guard from dependencies (pip uninstall llm-guard)
- 5Verify detection coverage with test attack payloads
Frequently Asked Questions
Is Rune self-hosted like LLM Guard?
Rune's SDK runs locally in your application process — all scanning happens on your infrastructure using local pattern databases and embeddings. Raw prompts and responses never leave your servers. The difference: Rune streams structured metadata (event type, threat category, scan result) to a hosted dashboard for monitoring and alerting. LLM Guard is fully self-hosted with no external calls, but you lose all observability unless you build it yourself.
Does Rune have a free tier since LLM Guard is open source?
Yes. Rune's free tier includes 10,000 events/month with all detection layers and the full dashboard enabled. LLM Guard is fully open source with no event limits, but you're responsible for hosting, monitoring, and keeping scanner models updated. Most teams find the monitoring gap is what hurts them in production — not the licensing cost.
LLM Guard has PII detection — does Rune match that?
Rune detects PII patterns (SSN, credit card, email, phone, address) in both model outputs and tool arguments — a surface LLM Guard can't see. Rune also adds data exfiltration detection (encoded data in URLs, sensitive fields in API calls) and secret detection (API keys, JWTs, connection strings), which LLM Guard doesn't cover.
What's the honest case for staying with LLM Guard?
If you need fully self-hosted scanning with zero external network calls and your compliance requirements prohibit even metadata leaving your infrastructure, LLM Guard is a reasonable choice. It's also free with no event limits. The trade-off: you get no dashboard, no alerting, slowing maintenance, and no agent-level scanning. For many teams, the monitoring gap becomes the bigger risk in production.
Other Alternatives
Lakera Guard Alternative
Lakera Guard was acquired by Palo Alto Networks and shifted enterprise. Rune is the independent, developer-first alternative.
NeMo Guardrails Alternative
NeMo Guardrails requires learning Colang and adds LLM-call latency. Rune offers native framework integration with sub-10ms overhead.
Guardrails AI Alternative
Guardrails AI validates outputs. Rune secures the entire agent pipeline — inputs, outputs, tool calls, and inter-agent communication.
Related Resources
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