Bots have been a part of the internet since its earliest days. Search engines would not exist without crawlers, and customer service would be significantly slower without chatbots. At the same time, malicious bots represent one of the most serious threats to businesses — they generate fake traffic, steal data, carry out DDoS attacks, and automate fraud on a massive scale. In this article, we explain exactly what a bot is, what types of bots operate on the web, why malicious bots pose a growing threat to organizations, and how to effectively protect against them.
What Is a Bot? Definition and How It Works
A bot (short for “robot”) is a computer program designed to automatically perform repetitive tasks on the internet without human intervention. Bots operate much faster than humans — they can process thousands of queries per second, analyze vast amounts of data, and make decisions based on programmed rules or artificial intelligence algorithms.
The operating principle of a bot is based on a cycle consisting of three elements: receiving input data (e.g., website content, a user message, an API signal), processing according to programmed logic, and executing an action (answering a question, saving data, sending an HTTP request). Simple bots run linear scripts — they execute a fixed sequence of steps. Advanced bots use machine learning, natural language processing (NLP), and adaptive techniques that allow them to mimic human behavior with increasing precision.
Not every bot is a threat. In reality, many bots perform critical functions in the internet ecosystem. The problem arises when bots are used for malicious purposes — and the scale of this phenomenon is growing year after year.
Good Bots — Positive Automation
A significant portion of bots on the internet serve useful functions, without which the modern internet could not operate effectively. Here are the most important categories of good bots.
Search Engine Crawlers (Web Crawlers)
Crawlers, also known as spiders, are bots that systematically browse websites to index their content. Googlebot, Bingbot, and Yandex Bot visit billions of pages, analyze their content, and build the index that enables users to find information through search engines. Without crawlers, SEO would be pointless, and the internet would be an unsearchable mass of documents.
Crawlers respect the robots.txt file, which defines which sections of a website may be indexed. They operate according to established rules — they identify themselves in the User-Agent header, adhere to delays between requests (crawl delay), and do not excessively burden servers.
Chatbots and Virtual Assistants
Chatbots are bots that communicate with users in natural language. From simple rule-based systems (responding based on keywords) to advanced AI models utilizing large language models (LLMs) — chatbots serve customers, answer questions, assist with purchases, and automate business processes. In 2026, AI chatbots handle an estimated over 70% of initial customer interactions in the e-commerce sector.
Monitoring Bots
Monitoring bots check the availability, performance, and security of IT systems. Uptime bots ping servers every few seconds to verify their availability. Security bots scan websites for vulnerabilities and weaknesses. Price bots monitor product price changes on e-commerce platforms. These tools are fundamental to IT and e-commerce operations.
Social Media Bots
Social media platforms use bots for content moderation, spam detection, automatic tagging, and content recommendation. Companies use bots to schedule and publish posts, analyze sentiment, and monitor brand mentions.
Malicious Bots — A Growing Threat
On the other side of the spectrum are bots created for harmful purposes. Their sophistication is growing at a pace that should concern every organization with an online presence.
Scraper Bots (Web Scraping Bots)
Scraper bots automatically extract content from websites — product descriptions, prices, contact information, articles, and reviews. While web scraping itself is not illegal in every jurisdiction, malicious scrapers steal intellectual property, copy entire product catalogs from competitors, and collect personal data without consent. The e-commerce industry loses billions of dollars annually due to unauthorized price scraping, which allows competitors to instantly react to price changes.
Credential Stuffing Bots
Credential stuffing is one of the most dangerous types of bot attacks. Bots use lists of stolen login-password pairs (originating from previous data breaches) and automatically test them across dozens of services. Since many users use the same passwords across different services, the attack success rate ranges from 0.1-2% — which, with millions of attempts, translates to thousands of compromised accounts. According to an Okta report, credential stuffing accounted for 34% of all login attempts in the retail sector in 2025.
Spam Bots
Spam bots automatically distribute unwanted emails, comments on forums and blogs, fake reviews, and messages on social media. Their purpose is product promotion, phishing, malware distribution, or opinion manipulation. Spambots are also responsible for creating fake accounts on social media platforms — it is estimated that on Twitter/X alone, fake accounts represent 5-15% of all profiles.
DDoS Bots
DDoS bots are infected devices forming a botnet — a zombie network capable of simultaneously flooding a target with millions of requests. DDoS attacks can disable a website, application, or an entire company’s infrastructure for hours or days. Modern botnets, such as successors to Mirai, encompass millions of IoT devices — cameras, routers, printers — whose owners have no idea their equipment is participating in attacks. According to Cloudflare, the number of DDoS attacks in 2025 increased by 58% compared to the previous year.
Click Fraud Bots
Bots engaged in click fraud automatically click on online advertisements, generating fake traffic and draining competitors’ advertising budgets. Juniper Research estimates that click fraud losses amounted to over 100 billion USD globally in 2025. Advanced bots can mimic human browsing patterns — moving the cursor, scrolling the page, clicking on various elements — making them difficult to detect.
Vulnerability Scanning Bots
Malicious scanners automatically search the internet for systems with unpatched security vulnerabilities. Once a vulnerable system is found, the information is passed to cybercriminals who exploit it for intrusion. These bots scan thousands of ports, test known exploits, and identify unsecured databases, administration panels, and open APIs.
The Scale of the Problem — Bots in Numbers
Statistics about bot traffic on the internet are alarming, and every cybersecurity team should be aware of them.
According to the Imperva Bad Bot 2025 report, bots account for nearly 50% of all internet traffic. This means that every other request hitting a server may come from a program, not a human. Malicious bots make up approximately 30% of global traffic — the highest percentage since Imperva began measurements in 2013.
The structure of malicious bot traffic breaks down as follows:
- Advanced bots — 51% of malicious bot traffic. They mimic human behavior, use headless browsers, rotate IP addresses, and solve CAPTCHAs. They are the most difficult to detect.
- Moderate bots — 28%. They use headless browsers with basic JavaScript, but their patterns are more repetitive.
- Simple bots — 21%. Scripts connecting from a single IP, easy to identify by HTTP headers and lack of JavaScript rendering.
The most targeted industries are finance (37% of traffic is malicious bots), e-commerce (30%), telecommunications (25%), and healthcare (22%). API endpoints are particularly vulnerable — bots generate over 30% of API traffic, representing a 40% year-over-year increase.
These numbers have a direct impact on costs. Gartner estimates that by the end of 2026, organizations will spend over 3 billion USD globally on bot management solutions — nearly double the amount spent in 2023.
Bot Threats to Businesses
Malicious bots generate multidimensional risks for organizations. Understanding them is crucial for justifying investment in protection.
Financial Losses
Credential stuffing leads to customer account takeovers and financial fraud. Click fraud drains advertising budgets. Price scraping undermines pricing strategies. Scalping bots purchase limited products in a fraction of a second, preventing real customers from buying and destroying the shopping experience.
Infrastructure Degradation
Even bots without a destructive purpose (e.g., aggressive scrapers) can burden servers to their performance limits. Increased hosting costs, website slowdowns for real users, and higher bandwidth consumption — all of this translates to real operational costs.
Analytics Distortion
Bot traffic contaminates analytical data. If 30% of website traffic comes from bots, metrics such as bounce rate, time on site, conversion, and demographic data become unreliable. Marketing and business decisions made based on contaminated data lead to flawed strategies.
Legal and Regulatory Threats
Data breaches caused by credential stuffing attacks are subject to GDPR regulations and can result in fines of up to 4% of annual revenue. Companies are responsible for protecting customer data, regardless of whether the breach resulted from a bot attack or another method.
Reputation Loss
Customer account takeovers, service unavailability due to DDoS, data breaches — each of these incidents caused by bots erodes the trust of customers and business partners. Rebuilding reputation after a security incident takes months or years.
Bot Management — Comprehensive Protection
Effective protection against malicious bots requires a multi-layered approach combining technology, processes, and continuous monitoring. Below, we present the key elements of a bot management strategy.
Web Application Firewall (WAF)
A WAF is the first line of defense against malicious bots. Modern WAF solutions analyze HTTP/HTTPS traffic in real time, identify suspicious patterns, and block malicious requests before they reach the application. WAF can detect known bot signatures, analyze HTTP headers, verify request integrity, and apply application-specific rules.
Key WAF functions in the context of bot defense:
- Filtering based on IP reputation (lists of known botnets and proxies)
- HTTP header validation (User-Agent, Referer, Accept)
- Request structure anomaly detection
- Integration with threat intelligence feeds
WAF works best when combined with a dedicated network firewall that filters traffic at lower layers of the OSI model.
Rate Limiting and Throttling
Rate limiting restricts the number of requests that a single client can send within a specified time frame. It is a simple yet effective technique against basic bots and brute-force attacks. Configuration includes:
- Global limits — maximum number of requests per IP per minute/hour
- Per-endpoint limits — more restrictive limits for sensitive resources (login, API, checkout)
- Progressive throttling — gradually slowing down responses instead of hard blocking, making it harder for bots to identify the protection mechanism
- Adaptive rate limiting — dynamically adjusting limits based on the current traffic profile
CAPTCHA and Behavioral Challenges
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a mechanism that verifies whether the user is a human. The evolution of CAPTCHA includes:
- Text CAPTCHA — distorted letters to type. Outdated — bots solve them with over 90% accuracy.
- reCAPTCHA v2 — “check that I’m not a robot” plus image recognition. Effective but slows down users.
- reCAPTCHA v3 — background behavioral analysis without user interaction. It assigns a risk score (0.0-1.0) based on interaction patterns with the website.
- hCaptcha — a privacy-focused alternative with a growing market share.
Behavioral challenges analyze how users interact with a website — mouse movements, scrolling patterns, keystroke dynamics. Bots, even advanced ones, exhibit patterns different from humans that behavioral systems can identify.
Browser and Device Fingerprinting
Device fingerprinting creates a unique identifier based on browser and device parameters — screen resolution, installed plugins, system fonts, time zone, WebGL configuration, Canvas API, and dozens of other attributes. This technique allows tracking bots even when they rotate IP addresses. Bots using headless browsers (Puppeteer, Playwright, Selenium) have characteristic fingerprints that differ from real browsers.
Behavioral Analysis and Machine Learning
Advanced bot management systems use machine learning models to analyze traffic patterns in real time. Analyzed signals include:
- Page visit sequences (real users browse pages in a logical order, bots often visit pages randomly or too sequentially)
- Time between requests (too regular = bot)
- Interactions with JavaScript elements (bots often do not render JS or do so in a characteristic way)
- Session patterns (duration, browsing depth, navigation paths)
ML models learn from historical traffic data, building a profile of normal user behavior and identifying anomalies that indicate bot activity.
Honeypots and Traps
Honeypots are hidden resources (links, forms, fields) invisible to humans but detectable by bots. A link hidden in CSS that is not visible on the page but gets visited by a bot scanning HTML immediately identifies malicious automation. Similarly, a hidden form field (honeypot field) that a bot fills in but a human never sees allows filtering out automated submissions.
SOC Monitoring and Incident Response
Even the best technologies cannot replace continuous monitoring conducted by a qualified team. SOC services provide round-the-clock traffic observation, rapid anomaly identification, and immediate response to bot incidents. SOC analysts correlate data from WAF, SIEM systems, application logs, and threat intelligence feeds, creating a complete picture of bot threats.
At nFlo, we serve over 200 clients and have completed over 500 cybersecurity projects. Our experience shows that effective bot protection requires combining technology with human expertise — tools alone, without proper configuration and continuous tuning, will not provide sufficient protection.
Protecting APIs from Bots
API endpoints represent a particularly attractive target for malicious bots. Unlike websites, APIs are inherently designed for machine-to-machine communication, making it harder to distinguish legitimate API clients from malicious bots. Key API protection practices include:
- Authentication and authorization — API keys, OAuth 2.0 tokens, mutual TLS for inter-service communication.
- Rate limiting per API key — individual limits for each API consumer, with granularity at the endpoint level.
- Schema validation — validating request structure according to OpenAPI/Swagger definitions, rejecting malformed requests.
- Anomaly monitoring — detecting unusual API access patterns (sudden spikes, access to undocumented endpoints, request sequences characteristic of automated API discovery).
The Future: AI-Powered Bots
The development of artificial intelligence is fundamentally changing the bot landscape — both good and malicious ones.
AI in the Service of Attackers
Generative AI enables the creation of bots that mimic human behavior with unprecedented accuracy. Bots equipped with LLMs can conduct natural conversations on customer service chats, generate convincing social media comments, write personalized phishing messages, and solve image-based CAPTCHAs. Voice and video deepfakes open new possibilities for bots carrying out social engineering attacks.
Bot creation automation is also becoming simpler. No-code and low-code tools enable the creation of advanced bots without deep programming knowledge, lowering the barrier to entry for cybercriminals. The dark web offers Botnet-as-a-Service, where renting a botnet for a DDoS attack costs just a few dozen dollars per hour.
AI in the Service of Defenders
On the defense side, AI also brings breakthrough capabilities. Next-generation bot management systems utilize:
- Real-time behavioral models — analyzing hundreds of signals simultaneously, with detection accuracy exceeding 99%.
- Adaptive challenges — dynamically adjusting verification challenges to the risk level of a given session.
- Predictive blocking — predicting bot attacks based on patterns observed across a global sensor network.
- Automated rule creation — AI systems generating WAF rules based on newly detected attack patterns.
Regulations and Standards
The growing scale of the bot problem is attracting regulatory attention. The European Union, through the AI Act, introduces requirements for AI system transparency, including the obligation to inform users about interactions with chatbots. American state regulations (California Bot Disclosure Law) require disclosure that the conversation partner is a bot. These regulations affect the design of both good and malicious bots.
Getting Started — Practical Steps for Businesses
Implementing protection against malicious bots does not have to be a one-time multimillion-dollar project. Below, we present a pragmatic phased approach.
Phase 1: Audit and diagnosis — identify what percentage of traffic on your resources consists of bots. Analyze server logs, WAF data, and web analytics for anomalies. Determine which resources are the most frequent targets.
Phase 2: Basic protection — deploy a WAF with basic bot management configuration, rate limiting on critical endpoints (login, registration, API), and CAPTCHA on forms.
Phase 3: Advanced detection — add device fingerprinting, behavioral analysis, and honeypots. Integrate data from various protection layers into a SIEM system.
Phase 4: Continuous monitoring and tuning — bots evolve, and protection must keep pace. Regularly analyze rule effectiveness, test new attack vectors, and update detection models. Consider entrusting monitoring to an external SOC team.
Summary
A bot is a tool — inherently neutral. Crawlers build the internet, chatbots streamline customer service, and monitoring bots help maintain system availability. At the same time, malicious bots represent one of the fastest-growing threats in cybersecurity — they account for nearly 30% of global internet traffic, carrying out credential stuffing, DDoS, scraping, and click fraud attacks on a massive scale.
Effective defense requires a multi-layered approach: WAF, rate limiting, CAPTCHA, behavioral analysis, fingerprinting, and continuous SOC monitoring. In the era of AI-powered bots, simply deploying tools is not enough — continuous adaptation, rule tuning, and security team expertise are essential.
If you want to understand what percentage of traffic on your resources consists of bots and how to effectively protect against them, contact the nFlo team. We will help conduct an audit, select the right solutions, and implement protection tailored to the specifics of your organization.
