What abuse do AI companies face?

AI companies face the same abuse shapes as other SaaS, but the cost lands harder because every free account spends real inference compute. The most common forms are: Free trial abuse and trial cycling: one person recreates a trial after the last one expires; Multi-accounting: one person runs many free accounts in parallel; Signup farming: accounts are created in bulk to harvest free credits; Ban evasion: a removed account returns under a fresh identity. Each fake account is handed GPU hours, token quotas, or free-tier credits that cost the company money. ShieldLabs surfaces abuse patterns indicating one person operating many accounts, using persistent visitor and user identification that works across sessions regardless of email, IP, or browser.

How does abuse drain AI inference compute?

On an AI product, an abused free account costs inference, not just storage and bandwidth, so abuse hits unit economics directly. Every abused account can spend: GPU seconds on model inference; Model tokens on each prompt and completion; Image, video, or audio generations, each with a real per-call cost. A trial farm running a thousand free accounts is a thousand parallel compute allocations billed to the company, which is why AI companies feel abuse faster than ordinary SaaS. ShieldLabs scores the visitor and user before any compute is allocated, so a fake account can be held or downgraded before it spends a single GPU second.

How does ShieldLabs detect abuse for AI companies?

ShieldLabs runs through one JavaScript snippet on your signup or trial activation page and returns a risk score with the signals behind it on the first visit. On every signup it returns: A 0-100 risk score with a full signal breakdown; Persistent visitor and user identification that holds across sessions, cleared cookies, and rotated IPs; Anonymity signals such as VPN, proxy, Tor, anti-detect browser, IP reputation, and OS mismatch; Abuse patterns in analytics, surfaced when a new signup matches an identity already linked to a prior account. The risk score and signals are delivered via API and Webhooks, so your code can hold, challenge, or downgrade the account before any inference compute is provisioned. ShieldLabs gives you the signals and the patterns; your own rules decide what happens to each account.

Can ShieldLabs stop API key abuse?

ShieldLabs works at the signup and login layer, not on API traffic, so it flags the fake account before it is ever issued an API key. The split of responsibilities looks like this: ShieldLabs scores the visitor and user, so a farmed free account that exists only to harvest free API credits is surfaced before a key is created; Your API gateway handles API-call-level controls: rate limits, spend caps, and scoped keys. Most API key abuse on AI products starts with a farmed free account, so removing the upstream cause prevents most of the downstream abuse. ShieldLabs removes the upstream cause at signup; pair it with your gateway to enforce the downstream limits.

How is this different from rate limiting and CAPTCHA?

Rate limiting and CAPTCHA control how an account behaves; ShieldLabs identifies whether the account is a new person at all. The difference by layer: Rate limiting caps how fast one account can spend compute, but does nothing about one person opening a hundred accounts, each within its limit; CAPTCHA slows scripted signups but does not link a new account to a prior one, and it is increasingly solved or bypassed; ShieldLabs adds persistent identification that links the new signup to any prior account, even when the email, IP, and browser are all new. Rate limiting and CAPTCHA stay useful as complementary layers. ShieldLabs is the layer that tells you the new account is not a new person.

How do I integrate ShieldLabs into an AI product?

Integration takes about 5 minutes: add one JavaScript snippet to your signup, login, or trial activation page. The steps are: Install the snippet on the signup or trial activation flow; Pass an optional UserHID (a hashed identifier for the user's account) after the account is created, so signups link back to the user; Read the results: risk score, anonymity signals, and abuse patterns, via REST API and Webhooks for server-side flows. The free tier includes 5,000 one-time identifications with no credit card required, so you can test on real traffic before enabling production rules. ShieldLabs returns the first risk score on the first visit, so you see signal on live traffic from day one.

Does ShieldLabs work for LLM apps, AI dev tools, and image generation?

Yes. The mechanism is the same across AI sub-verticals because the abuse is the same: one person creates accounts to harvest free compute. How it shows up by sub-vertical: LLM chat apps and AI developer tools: trial cycling and multi-accounting to keep using the free tier or free token allowance; Image, video, and voice generation: signup farming, made especially expensive by the high per-generation cost; AI agent platforms and API products: farmed free accounts that exist only to harvest free API credits. ShieldLabs identifies the person behind the accounts at signup in every one of these cases, using the same persistent identifier across emails, IPs, and browsers.

Will ShieldLabs automatically block signups?

ShieldLabs blocks nothing automatically, it returns a risk score and a full signal breakdown, and your code decides what to do. A typical threshold setup looks like: Score 60 or above: trigger additional verification or grant a smaller free allocation; Score 85 or above: hold the signup for review or route it to a stricter trial path; Below your threshold: let the user through with no extra friction. The free tier includes 5,000 identifications, so you can tune these thresholds on real traffic before enabling rules in production. ShieldLabs keeps you in control: it surfaces the signals, you own the decision logic.

AI Fraud Prevention

Stop AI abuse before
it drains your inference compute

Keep GPU hours, token quotas, and free-tier credits on real users,
and stop one person from farming a hundred free accounts

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Stop abuse at signup

Catch fake signups and farmed accounts before they get in

Protect your inference compute

Keep GPU hours and token quotas spent on real users, not trial farms

Trust your growth metrics

Keep activation, retention, and conversion free of farmed accounts

Keep real users friction-free

Legitimate users sign up and start using your product without extra verification steps

How ShieldLabs protects AI products from abuse

ShieldLabs combines 100+ device, OS, browser, IP and network layer signals, surfacing anonymity signals and abuse patterns to stop a fake signup before it is granted free inference compute

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, incognito mode, rotated IP, and fresh accounts

Anonymous Traffic Detection

VPN, proxy, Tor, anti-detect browser, IP reputation and other anonymity signals

Ready Risk Score

A ready-to-use risk model that scores every signup 0–100 with signal breakdown

Detect abuse with in-depth pattern analytics

Pre-built patterns indicating multi-accounting, account farms, and other abuse patterns

Real-Time Analytics

Aggregate signup risk score, traffic source attribution, per-session signal breakdown, and active abuse patterns

Stop abuse on your AI product in 5 minutes

Easily integrate into any signup, login, or free trial activation flow

1
Sign up
Sign up and get 5,000 free identifications.
2
Install snippet
Install one JavaScript snippet on your signup or trial activation page.
3
View analytics
View detailed analytics on anonymity signals, risk scores, and potential abuse in your traffic.
4
Use API & Webhooks
Use the API and Webhooks to prevent abuse.

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "ip": "62.197.149.124",
  "country": "Lithuania",
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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Stop AI abuse beforeit drains your inference compute