Bot & AI Agent Detection
Detect bots and AI agents in real time
LRDefender distinguishes human browsers from automation by combining runtime behavior, rendering integrity, and environment consistency. That includes AI agents driving a real browser, the traffic that looks most human, so you stop scripted abuse without annoying real users.
Behavioral
Mouse & interaction ML
Real-time
Allow, challenge, block
3
Detection layers
0
CAPTCHAs required
Live demo
Your cursor, scored
Measured on this visit, never simulated
move your pointer here, the trail is your real path
0
Pointer events
0
Trusted events
0
Velocity changes
Hard signals, read instantly
Keep moving your pointer, or type, to enable the live score.
The counters are real event counts, the hard signals are plain property reads, and the verdict comes from the live bot check API. Nothing here is simulated.
The Problem
Bots no longer announce themselves
Residential proxies, patched headless Chrome, and LLM-driven form filling pass basic checks. Traditional bot tools chase signatures; product teams need continuous proof of human control.
The Solution
Defense in depth on the actual client
LR Guard and LR Trace observe how code runs, how the GPU paints, and how sessions evolve, three complementary layers, hard automation signals, ML fusion on interaction behavior, and consistency heuristics, that must align for a request to look human. Block, challenge, flag, or redirect instantly, or route the score into your own throttling and content logic.
Runtime integrity probes
Detect patched automation frameworks, inconsistent WebGL stacks, and impossible combinations of hardware claims.
Behavioral cadence analysis
Score pointer paths, scroll physics, and keystroke entropy against human baselines, with thresholds tunable per tenant.
Headless and automation tells
Surface subtle mismatches between input events and rendering that scripted agents struggle to replicate.
Session continuity without cookies alone
Bind automation attempts across fresh IPs and incognito windows using stable device anchors.
Real-browser AI agent detection
Agents that drive a genuine browser leave automation side channels and an interaction record that does not add up. LRDefender scores both, and the probe set is a per-tenant setting you switch on when you want it.
Policy actions that fit product
Use the score and verdict in your own logic: serve limited inventory, delay risky APIs, or trigger step-up auth, without a one-size CAPTCHA.
How It Works
Three steps to protection
Instrument high-value pages
Drop LR Guard on signup, search, pricing, and inventory endpoints where automation hurts margins most.
Layer signals at the edge
LRDefender evaluates probes plus Trace identity in one pass, returning allow, challenge, or block to your app server or middleware.
Close the loop with retraining
Production traffic is continuously sampled back into retraining with guardrails against drift, so detection keeps pace with the traffic you actually receive.
Starve scrapers without starving growth
Replace brittle blocklists with layered client intelligence, trial LRDefender on a single surface and measure human conversion alongside bot drop-off.