Make yourself the wrong target

Make yourself the wrong target.

Attackers do the math, and AI made attacking cheap. RipTide runs up their cost in time, tokens and exposure until moving on to someone else is the only rational move.

The problem

AI made attacking cheap. Make it expensive again.

An autonomous agent can probe target after target for the price of a few API calls. When attacking is that cheap, everyone is worth a try, including you.

Blocking an attacker costs them a second. Holding their attention costs them hours: every request against a decoy is compute, budget and time they don't spend on the systems that matter.

Every minute spent circling a RipTide decoy is a minute not spent on the systems that matter.

How RipTide catches it

Spend their budget, not yours.

All of it happens inside your own decoys. Nothing reaches out to anyone.

  1. 01

    Answer anything

    Every request gets a believable answer, chosen on the box in Trial or written by an LLM in Professional, so there's no dead end to tip the agent off.

  2. 02

    Lead nowhere

    Every path answers and none leads anywhere real, so an autonomous agent can spend its operator's tokens, compute and hours circling decoys.

  3. 03

    Stay convincing

    Wire-perfect personalities and consistent answers keep the agent sure it's making progress.

  4. 04

    Learn as they go

    Every minute they spend is logged as evidence and intelligence for your team.

Where their budget goes

The attacker pays. You collect.

What a long session on a decoy costs them, and what it gives you.

What an attacker spends on a RipTide decoy, and what the defender gains
They spendOnYou get
TokensReading decoy pages that keep answeringA record of everything it read.
ComputePlanning against systems that don't existIts plan, step by step.
HoursPaths that lead nowhereTime for your team to respond.
ExposureEvery tool and technique it triesIndicators for your threat feed.

The detections that do the work

Decoys that keep them busy.

Every decoy has zero legitimate users. So every touch is a finding.

See every detection

An answer for any path

Every plan

Probes for WordPress, .git, Jenkins, cloud metadata or Kubernetes get a believable reply instead of a 404: chosen on the box in Trial, written by an LLM in Professional.

Wire-perfect personalities

Stealth

Speaks nginx, Apache, IIS, Go and uvicorn, down to header order and error pages.

llms.txt & agent guidance

AI agents

llms.txt, .well-known agent files and notes in page source, written for AI readers. Each points to a path of its own, so the path an agent fetches shows which one it followed.

What lands in your SOC

A long night, for them.

The console shows how long an intruder spent on decoys and what they did there, so you can see the cost you imposed.

  • Session time and depth, per intruder
  • Every request answered, every request logged
  • Zero real systems touched along the way

Illustrative example. Canary credentials are non-privileged and exist only for detection. RipTide detects and alerts; it never takes destructive action against anyone's infrastructure.

Questions

Fair questions.

Is this hacking back?

No. Everything happens inside your own decoys. RipTide never reaches out to, or takes destructive action against, anyone's infrastructure. It only answers what it's asked.

What do all those replies cost us?

In Trial, replies are chosen on the sensor's own CPU, so there's no per-token bill. In Professional, LLM-written replies are capped per minute and per day and cached for an hour, so a noisy scanner can't run up the meter. Either way, one setting turns replies off.

Won't a skilled attacker notice?

Canned responses are how honeypots get spotted. RipTide answers any path believably and speaks real server dialects down to the headers, which makes a decoy much harder to tell from the real thing.

Make contact.

Tell us who keeps knocking. We'll show you how to make it expensive.

Book a briefing

Thirty minutes with the people who built it. Bring your hardest question.

  • Watch a live agent set off a detection
  • Map decoys to your crown jewels
  • Plan a first deployment in one sitting

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