prompt-protection vs. the alternatives

This page is written by the author of one of the tools, so read it with that in mind. The aim is to route you to the right tool for your constraints — which is frequently not this one. Where another project is the better fit, it says so.

The one distinction that decides most of it

Before any feature table, one axis separates these tools more than any other: does detection run in your process, or does it call a service?

In-process (prompt-protection, LLM Guard, NeMo Guardrails) means no network hop, no per-request fee, no third party seeing user text, and detection that works offline — bounded by what runs locally.

Service-based (Lakera Guard, Rebuff's hosted mode) means a model-backed classifier that reasons about intent and updates centrally as new attacks appear — at the cost of latency, spend, and sending every prompt to a vendor.

That trade-off usually decides the question before feature counts enter into it.

At a glance

The projects

Rebuff

Layered by design — a heuristic pass, a dedicated LLM detector, a vector store of previously seen attacks, and a canary-token trick for detecting leaks. That vector-store layer is genuinely clever: it learns from attacks it has already seen, which a static rule set cannot.

Choose Rebuff over this if you want a self-improving detector and can run the LLM and vector-DB dependencies, or want the hosted version. Choose prompt-protection if you are on Node with no appetite for a vector DB and a model call in your hot path, and you want deterministic sub-millisecond checks.

LLM Guard

The most feature-complete open-source option, and it is not close. A broad set of input and output scanners — prompt injection, toxicity, PII with anonymisation, secrets, relevance, and more — backed by real ML models rather than only regex.

Choose LLM Guard if you are on Python and want the widest, strongest coverage and can absorb the model-inference latency and footprint. It is the more powerful tool. Choose prompt-protection if you are on Node/TypeScript (LLM Guard is Python-only), or you need a near-zero-latency, zero-dependency check in a browser or edge runtime where loading ML models is not an option.

Lakera Guard

A commercial, hosted API with a strong research team behind the detection models, continuous updates, and a managed service you do not operate. If you want someone whose full-time job is keeping up with novel attacks, this is the category.

Choose Lakera if you want managed, professionally maintained, model-grade detection and are comfortable sending prompts to a third party and paying per call. Choose prompt-protection if data residency, offline operation, or cost rule out a hosted service, or you want a free first layer before deciding whether a paid service is warranted.

NVIDIA NeMo Guardrails

A different shape of tool — a framework for programmable conversational rails using its Colang language, covering topic control and dialogue flow, not only injection. Considerably more capable and considerably more to learn.

Choose NeMo if you need rich conversational guardrails and dialogue management and can invest in the framework. Choose prompt-protection if you want a focused injection/jailbreak/exfiltration scanner you wire in with a single function call.

Where prompt-protection is the right pick

Not as the strongest detector on this page — LLM Guard and the hosted services detect more. It is the right pick when the constraints matter as much as the detection:

• You are on Node.js or TypeScript. Most serious open-source competitors are Python-only. Running one from a Node service means a subprocess or a sidecar; this is an npm install.
• Latency is in the budget. Detection is synchronous and sub-millisecond. No model to load, no service to call.
• Privacy or residency is non-negotiable. Regulated data that cannot go to a third-party API; the check runs in your process and nothing leaves.
• The supply chain is scrutinised. Zero runtime dependencies — nothing transitive to audit or get flagged.
• You need it in the browser or at the edge. Runs client-side (the demo is the library in your browser) and in Workers/edge runtimes where a Python ML stack cannot go.
• You want a free first layer. Cheap deterministic pass in front; escalate the survivors to a model — including via the bundled Claude/OpenAI adapters — only when warranted.

The honest limitation, restated

prompt-protection is pattern-based. It will not catch a novel attack phrased in a way its rules do not anticipate, and it cannot reason about intent the way a model-backed detector can. The obfuscation handling is strong and the known-shape coverage is good, but a determined attacker who rewrites their payload semantically gets through. If your threat model includes sophisticated adaptive adversaries and you can afford the latency and cost, a model-based detector — LLM Guard locally, or a hosted service — will serve you better, and the two compose well: fast deterministic rules first, model second.

Using them together

These are not mutually exclusive; the strongest posture layers them.

import { analyzePrompt } from 'prompt-protection';

// Layer 1 — cheap, deterministic, in-process. Kills the obvious volume.
const local = analyzePrompt(userInput, { threshold: 35 });
if (local.action === 'block') return reject();

// Layer 2 — only the survivors reach the expensive/hosted detector,
// so you pay for the model on a fraction of traffic.
if (local.action === 'flag') {
  const verdict = await hostedDetector.check(userInput);
  if (verdict.malicious) return reject();
}

The cheap layer absorbs the bulk of attack traffic at no marginal cost; the expensive layer only runs where the fast pass was uncertain. You get most of the coverage of the stronger tool at a fraction of its per-request cost.