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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—so you stop scrapers and scripted agents without annoying real users.

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99.8%

Bot detection rate

Real-time

Edge blocking

5

Detection layers

0

CAPTCHAs required

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.

Headless and semi-headless stacks spoof common JS APIs well enough to fool naive telemetry.
Scrapers throttle requests and rotate fingerprints, evading coarse IP and rate limits.
AI agents complete multi-step flows with human-like pacing, defeating timing-only heuristics.
CAPTCHAs convert poorly and are increasingly solved by third-party services—security theater for buyers.
Security logs flood with uncategorized automation because client scripts only see part of the story.

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—five complementary layers that must align for a request to look human. Block, throttle, or serve degraded content instantly.

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 for your app surfaces.

Headless and remote-desktop 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.

Policy actions that fit product

Serve limited inventory to suspected bots, delay risky APIs, or require re-auth—without a one-size CAPTCHA.

How It Works

Three steps to protection

1

Instrument high-value pages

Drop LR Guard on signup, search, pricing, and inventory endpoints where automation hurts margins most.

2

Layer signals at the edge

LRDefender evaluates probes plus Trace identity in one pass—return allow, challenge, or block to your CDN or app server.

3

Close the loop with abuse labels

Tag confirmed scraper sessions and feed outcomes so models tighten on the tactics you actually see.

Products Used

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.

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