prompt application firewall

Stop AI Attacks Before They Hit You

Real-Time Prompt Application Firewall

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Your LLM app
❯ POST /api/v1/paf
in:"▋"
┌─ how it works ─┐

A prompt passes through many filters

Each stage inspects the prompt from a different angle. A request must survive all of them before reaching your LLM — one red flag anywhere stops it cold.

Vectorize
Embed the prompt into semantic space
Static Analysis
Pattern & signature matching against known attacks
LLM Analysis
Neural classifier scores jailbreak intent
More Filters...
Additional stages in the pipeline
Your LLM
Only safe prompts arrive

❯ avg. latency < 500ms across all stages

┌─ the threat ─┐

What's a Prompt Injection?

Malicious text crafted to hijack your LLM. A single injection can leak data, execute code, escalate privileges, and worse — all through ordinary-looking input.

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01/05
┌─ capabilities ─┐

Everything you need to secure your LLM

Built for production from day one — fast, developer-friendly, and transparent.

Real-Time Threat Blocking

Neutralize malicious prompts before they ever reach your model.

Quick Integration

Route prompts through PAF in a few lines of code — minutes, not days.

Low-Latency Filtering

Average request under 500ms. Your users never feel the firewall.

Staging Friendly

Validate integrations with a test API key — no production cost.

Sandbox Playground

Try attack vectors against your config in an isolated environment.

Team Collaboration & RBAC

Invite your team, assign roles, and manage access at scale.

Usage-Based Pricing

Pay only for what you use. Flat fee + usage — no hidden costs.

PAF Logs

soon

Review detailed request logs and blocked-attack audit trails.

┌─ integration ─┐

Add PAF in few LoC

Send prompts to our API before forwarding them to your model. If matches is empty, the prompt is safe.

app/api/chat/route.ts
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// CheckIcon prompts before sending them to your LLM
async function isSafe(prompt: string): Promise<boolean> {
  const res = await fetch("https://farfa.tech/api/v1/paf", {
    method: "POST",
    headers: { "X-API-Key": process.env.PAF_API_KEY },
    body: JSON.stringify({ text: prompt }),
  })
  const { matches } = await res.json()
  return !matches?.length
}

// → matches = [] means safe to forward to your model
⎇ main
Ln 12

Use paf_sk_test_ for staging · paf_sk_ for production

┌─ real vulnerabilities ─┐

This isn't theoretical

Real CVEs assigned to prompt injection and LLM command execution in production tools. The threat is already here.

criticalCVE-2025-53773

Command Injection in GitHub Copilot

Improper neutralization of special elements used in a command ('command injection') in GitHub Copilot and Visual Studio allows an unauthorized attacker to execute code locally.

NVD
0%
"of AI systems assessed in security audits showed exposure to prompt injection vulnerabilities"
SQ Magazine

Let's secure your LLM

Route your prompts through PAF and block attacks before they reach your model. Set up in minutes.

Start Here
┌─ faq ─┐

Common questions

Yes, we train and host models at the moment. The challenge is that they must be able to detect dangerous prompts under a second — so they must be very fast and accurate. They all run on dedicated Farfatech infrastructure in Canada, with encryption at rest and in transit.