Nearly 40% of ecommerce traffic today is bots.
Ecommerce traffic often looks healthy until the site slows down, PLP pages drag, and analytics stop making sense. Many times the real issue is silent bot traffic that behaves like shoppers and quietly overloads the system.
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
Looking back at bot behavior over the years feels like watching a creature learn new tricks and grow stronger.
Stage 1. Single IP Bots
These were the simplest. 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
Soon botnets from the same subnet started appearing. Instead of one IP, now there were clusters. Harder to catch, but still patterned.
Typical attacks
• SQL injection attempts
• Cache poisoning
• High frequency PLP requests
Stage 3. Rotating Proxy Bots
This is where it got serious. Every request came from a new IP. These bots used VPNs, proxies, cloud servers, and rotated user agents. IP blocking no longer worked.
Typical attacks
• 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.
Types of Smart Bots Targeting Magento and Adobe Commerce
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 create silent but severe performance degradation across ecommerce platforms like Magento and Adobe Commerce.
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 ecommerce teams miss bot attacks because traffic looks normal. Here are practical indicators used across Adobe Commerce, Magento, and Shopify ecosystems.
Traffic patterns that don’t match conversions
• High sessions but flat or dropping orders
Unusual PLP or search load
• Filters hit at impossible frequency
• Many unique PLP URLs
High CPU usage without matching sales
• Backend servers spike during low business hours
Authentication anomalies
• Large number of 401 and 403 responses
• Login POST requests from distributed IPs
Cart and checkout anomalies
• Sudden add to cart bursts
• Strange card-test error patterns
If multiple signals appear together, your site is likely under a coordinated smart bot wave.
Why Smart Bots Are Hard to Detect on Ecommerce Websites
Smart bots hide well because they do not behave like basic scripts. They blend into normal traffic, spread their requests, and stay quiet enough to avoid attention.
Proxy rotation
Bots change IPs constantly using proxies and VPNs, so no single IP looks suspicious.
User agent rotation
They switch between browser types and devices to look like different real users.
Session behavior mimic
They move through PLPs and search pages in a human like flow, making the session look real.
Cookie based evasion
Bots accept and reuse cookies so they do not appear as new or empty sessions.
Random delays
They add small pauses between actions so the timing feels natural.
Avoiding thresholds
Bots keep their activity low and spread out so they never trigger 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 and each hit creates a direct performance and business impact.
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.
Real Observability Signals in New Relic for Ecommerce Bot Detection
New Relic is usually the first place where unusual bot behavior shows up. Smart bots avoid big traffic spikes and instead 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
A mix of tools and observations finally reveals smart bot activity.
Log based detection
• Pattern recognition in access logs
JA3 fingerprinting
• Bots rotate everything but their TLS fingerprint
Session level behavior detection
• Detect unrealistic navigation flow
CDN anomaly detection
• Identify sudden changes in request shape
Repeated filter combinations
• Bots hit random filters repeatedly
Building a Defense Architecture for Smart Bot Mitigation in Magento and Adobe Commerce
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.
Conclusion
Smart bots are no longer a minor nuisance. They behave like real users, quietly overload critical ecommerce flows, and destabilize Adobe Commerce when ignored. Detecting them early through patterns, fingerprints, and observability signals is key. A layered defense at the edge, application, and monitoring levels is what keeps the platform stable and the business protected.
