AI agent detection, built into a real fraud engine

Some of your logins aren't human.

AI browser agents can fill your forms, click your buttons, and pass your login. Leonix tells you when — and scores the fraud risk in the same real-time decision, not a separate tool bolted on after.

See it live

Watch Leonix tell a person from an agent.

An unedited capture of the signals firing in real time — no staged data, the same detection running on test.leonix.io right now.

test.leonix.io
How we tell the difference

Six signals an AI agent can't fake at once.

Not a single anomaly score — layered environment and behavioral signals that have to agree before a session gets flagged as automated.

Headless & automation frameworks

Playwright, Puppeteer, Selenium, and similar tooling get caught before a single click happens.

Human-like AI browser agents

Curved mouse paths and human-paced typing don't get a pass either.

Software-rendered environments

Virtual GPUs and headless rendering stacks don't go unnoticed.

Instant, synthetic input timing

Filled faster than any real person could type or read.

Disclosed automation, evaluated too

Identifying yourself as automated doesn't mean a free pass.

Too-perfect interaction patterns

Too-precise clicks and keystrokes stand out on their own.

How it works

One decision, several layers deep.

Every signal is gathered and scored in the same request — the decision ships before the user's next click, not in a batch review the next morning.

Collect

Device fingerprint + behavioral telemetry from the widget

Correlate

Cross-check IP, geo, timezone, and device history

Score

Rule engine evaluates every signal in real time

Decide

Allow, challenge, or block — returned inline

Review

Flagged sessions land in the case queue for follow-up

And it doesn't stop at "is this an agent"

Once we know who's behind the session, the fraud engine kicks in.

Knowing it's a human doesn't mean the application is honest. Leonix runs the same real-time decision either way.

Synthetic identity detection

Catch fabricated and manipulated identities at the point of application — before an account opens, a loan funds, or a chargeback lands on your books.

Application stacking

Identify the same device fingerprint submitting applications across multiple platforms in a short window — a leading signal for bust-out fraud and promo abuse.

Account takeover

Detect new-device logins and session anomalies on user accounts — flag unauthorized access attempts before they clear.

Bot farms

Identify scripted and automated submissions in real time — eliminate low-quality volume before it reaches your review team.

Identity consistency

Cross-check device location, IP, and timezone against the applicant's claimed address — surface misrepresentation the moment the form is submitted.

Fraud ring linkage

Link devices, IPs, and email patterns across applications to surface coordinated rings before they exhaust your risk thresholds.

Why not just detect the agent and stop there

Knowing it's an agent is half the answer.

Agent detection alone tells you what — not whether it matters. Leonix was built as a fraud engine first; agent detection feeds the same decision instead of sitting next to it.

Agent detection alone
Flags the session as automated — then what? No risk score, no decision
Sits beside your fraud stack, not inside the decision that actually blocks anything
Treats every agent the same, whether it's a price scraper or an account takeover
A second tool, a second integration, a second team to keep it running
Leonix
Agent or not, the same request returns an allow / challenge / block decision
One integration — agent signals and fraud signals feed the same risk engine
Confidence-tiered: a detected agent still gets scored on what it's actually doing
One review queue, one vendor, one real-time decision
Architecture

Every tenant's data stays its own.

Leonix runs as an isolated decision layer per tenant — your rules, your thresholds, your review queue. Nothing about how one customer is scored leaks into how another one is.

Tenant-isolated by default — every request, log, and case is scoped to the calling tenant
Signals, not one score — device, behavior, network, and history are evaluated as independent layers
Nothing blocks on a hunch — post-decision behavioral review never touches the live verdict
collectdevice + behavior
correlateip · geo · tz · history
scorerule engine
decideallow
latencyinline, same request
Get started

Ready to protect your platform?

Get in touch and we'll integrate with your stack the same week.

Talk to us Try it live