The Rise of Smart Bots in eCommerce and Adobe Commerce Performance

Bots now make up more than half of web traffic. Thales’ 2026 Bad Bot Report found automated traffic reached 53% of all web traffic in 2025, with bad bots at 40%, up from 37% the year before. Radware’s 2026 E-Commerce Bot Threat Report found bad bots made up 43% of holiday shopping traffic in 2025, against 46% from human shoppers.

eCommerce traffic often looks healthy until the site slows down, product listing pages (PLPs) drag, and analytics stop making sense. In many cases, silent bot traffic that behaves like shoppers is the cause. This guide explains how smart bots work, how to confirm them, and how to defend an Adobe Commerce (Magento) store.

What Bots Really Are in Modern eCommerce Platforms

Bots are automated programs that perform actions on a site without humans involved. Some are good, like search crawlers. Others are harmful, hitting eCommerce sites for scraping, fake logins, fake orders, or heavy load.

Modern bots behave like real shoppers. They browse normally, pause between clicks, move across PLPs and search pages, and enter through many IPs. They look harmless at first but quietly slow the site and distort analytics.

The Bot Evolution Timeline in eCommerce Security

Bot behavior has become steadily more sophisticated.

Stage 1. Single IP Bots

One IP spamming contact forms or reloading pages again and again. Blocking them felt easy and straightforward.

Typical attacks
• Contact form spam
• Fake orders
• Repeated PLP or homepage hits

Stage 2. Subnet Bots

Botnets from the same subnet appeared, with clusters instead of one IP. They were harder to catch and still patterned.

• SQL injection attempts
• Cache poisoning
• High-frequency PLP requests

Stage 3. Rotating Proxy Bots

Every request came from a new IP, using VPNs, proxies, cloud servers, and rotated user agents. IP blocking stopped working.

• Filter spam on PLPs
• Session resets
• API spamming
• Login attempts

Stage 4. Smart Bots

The most dangerous stage. These bots understand human behavior. They scroll, pause, navigate, and trigger requests like real users. They use headless browsers and adjust their fingerprints.

Common targets
• PLP pages
• Search endpoints
• Login and Cart
• Non-cached URLs

These bots blend in unless deeper signals like New Relic patterns, JA3 fingerprints, or Fastly rules reveal them.

Stage 5: AI-Enabled Bots and Agents

AI makes it cheaper to build and adapt bots, and legitimate AI shopping agents increasingly mimic human behavior too. Telling wanted from unwanted automation depends on intent and policy more than on whether a visitor looks human.

Signals such as New Relic patterns, TLS fingerprints, and Fastly rules help reveal them.

Types of Smart Bots Targeting Adobe Commerce (Magento)

Credential stuffing bots

• Try stolen credentials

• Spread attempts across IPs

• Target login endpoints quietly

Inventory scraping bots

• Extract product data and stock

• Crawl PLPs and APIs aggressively

Price scraping bots

• Monitor competitor pricing

• Trigger load with repeated filter hits

Checkout bots

• Add and checkout instantly

• Used for limited edition products

PLP search bots

• Hit layered navigation and search filters

• Cause non cached load

Account takeover bots

• Perform slow and distributed login attempts

• Hard to catch without behavior analysis

How Smart Bots Impact eCommerce Performance and Business Metrics

Smart bots can quietly degrade performance across platforms like Adobe Commerce (Magento).

PLP slowdowns
• Bots trigger repeated non cached PLP and filter requests
• Causes Elasticsearch load spikes
• Directly lowers conversion rates

Search latency
• Bots abuse search endpoints with random or repeated keyword hits
• Slows autocomplete, search results, and navigation
• Impacts user experience and SEO

Checkout blockages
• Checkout bots add to cart instantly and stress payment validation
• Creates card testing waves and false declines
• Blocks genuine users during peak demand

These issues collectively increase operational cost, distort analytics, and hurt revenue.

How to Confirm If Your eCommerce Site Is Under Bot Attack

Many teams miss bot attacks because traffic looks normal. Practical indicators across Adobe Commerce (Magento) and other platforms include:

• Traffic that does not match conversions: High sessions with flat or falling orders
• Unusual PLP or search load: Filters hit at impossible frequency and many unique PLP URLs
• High CPU without matching sales: Backend spikes during low-traffic hours
• Authentication anomalies: Many 401 and 403 responses and login POST requests from distributed IPs
• Cart and checkout anomalies: Sudden add-to-cart bursts and odd card-test error patterns

When several signals appear together, expect a coordinated smart bot wave.

Why Smart Bots Are Hard to Detect on eCommerce Websites

Smart bots hide well because they do not act like basic scripts. They blend into normal traffic, spread their requests, and stay quiet enough to avoid attention.

• Proxy rotation: Constantly changing IPs through proxies and VPNs, so no single IP looks suspicious
• User agent rotation: Switching browser types and devices to look like different users
• Session behavior mimicry: Moving through PLPs and search in a human-like flow
• Cookie reuse: Accepting and reusing cookies, so sessions do not look empty
• Random delays: Adding pauses so timing feels natural
• Staying under thresholds: Keeping activity low and spread out to avoid alerts

These tricks help them blend into real traffic until deeper analysis catches them.

How Smart Bots Target and Impact Adobe Commerce Performance and Security

Smart bots target specific weak points in Adobe Commerce (Magento).

Entry points bots target
• Product Listing Pages, especially filters
• Login endpoints for distributed credential attacks
• Search endpoints that trigger heavy queries
• Add to Cart flows used by scalpers
• Checkout flows for card testing
• GraphQL and REST APIs for data extraction

Business impact

• Higher hosting and infrastructure load
• Slow PLP pages affecting conversions
• Distorted analytics from inflated traffic
• Broken A B tests due to polluted data
• Lower SEO rankings from slow pages
• Increased fraud attempts
• Bandwidth and CDN cost spikes
• Continuous Dev and Ops firefighting
• Creates instability across the site when left unchecked.

Left unchecked, bots create instability across the site.

Real Observability Signals for eCommerce Bot Detection

Application performance monitoring, such as New Relic, is often the first place unusual bot behavior shows up. Smart bots avoid big traffic spikes and leave subtle, distributed patterns.

PLP response time spikes
Bots hit PLPs and filters hard, triggering non cached and expensive Elasticsearch calls. New Relic shows rising response times even when traffic looks normal.

High non cached filter hits
Bots spam filter parameters, generating many unique PLP URLs. Each one bypasses cache and forces fresh rendering, increasing CPU and DB load.

Sudden CPU jumps
Infrastructure graphs reveal CPU spikes without matching orders or user activity. Bots spread load across many endpoints, causing wide CPU strain.

Strange login POST activity
Login endpoints show small, frequent POST bursts. Many 401 and 403 errors come from rotating IPs, indicating credential stuffing.

High failed login count
Failed logins climb quietly as bots rotate IPs, agents, and sessions. These patterns often stay under threshold based alerts.

These signals, when correlated in New Relic, help teams detect smart bot waves early and prevent system instability.

Detection Techniques That Work for eCommerce Bot Attacks

No single signal catches smart bots. Combine several:

• Log-based detection: Pattern recognition in access logs
• TLS fingerprinting (JA4): Chrome began randomizing TLS extension order in 2023, which made JA3 hashes unstable, and JA4 sorts the values so reordering does not change the result. A fingerprint can still be spoofed or match a real browser, so treat it as one input.
• Session-level behavior detection: Detect unrealistic navigation flow, such as requesting only HTML or hundreds of pages per minute
• CDN anomaly detection: Identify sudden changes in request shape
• Repeated filter combinations: Random filters hit repeatedly
• Identity signals: Login velocity, device reuse, and impossible travel

Building a Defense Architecture for Smart Bot Mitigation in Adobe Commerce (Magento)

Stopping smart bots requires a layered approach that works at different levels.

Layer 1. Fastly edge blocking
Block fingerprints and suspicious patterns before they hit Magento

Layer 2. Application level throttling
Delay or limit repeated behavior

Layer 3. New Relic anomaly alerts
Catch unusual load patterns early

Layer 4. Adobe Commerce configuration hardening
Reduce vulnerabilities and unprotected endpoints

Layer 5. Rate limiting and traffic shaping
Protect API and search endpoints

This layered shield is what finally stabilizes performance.

AI Bot Policy

AI crawlers and shopping agents need rules of their own. Akamai and Fastly both report rising AI bot traffic, and retail is among the most targeted sectors. Separate three groups and set a policy for each:

• Training crawlers: Decide whether you want your catalog used for model training. Allow, rate limit, or block with robots.txt and edge rules.
• Search and retrieval bots: These can send shoppers and mentions back to you, so blocking them can reduce AI search visibility. Our guide to AI search and eCommerce SEO explains the trade-off.
• Shopping agents: Allow the ones you want to serve, and keep them off sensitive flows such as login and checkout unless you support them.