CYFIRMA’s assessment indicates that the rapid adoption of artificial intelligence (AI) is significantly transforming the threat landscape facing the e-commerce sector. AI-powered bots are enabling threat actors to conduct highly automated, adaptive, and scalable attacks that increasingly bypass traditional security controls by mimicking legitimate user behavior and continuously adapting to defensive measures.
India’s expanding digital commerce ecosystem, driven by growing internet penetration, widespread digital payment adoption, and high-volume online shopping events, presents an attractive target for financially motivated cybercriminals. Threat actors leverage AI-powered bots to automate activities such as credential stuffing, account takeover, payment fraud, inventory hoarding, fake account creation, gift card abuse, and large-scale web scraping, allowing attackers to maximize operational efficiency while reducing the likelihood of detection.
These attacks create significant financial, operational, and reputational risks to e-commerce organizations by increasing fraud losses, disrupting online services, degrading customer experience, and placing additional demands on security and operational resources. As AI capabilities continue to evolve, bot-driven attacks are expected to become increasingly sophisticated, making traditional rule-based detection mechanisms less effective.
To address this evolving threat, organizations should adopt a layered defence strategy that combines advanced bot management, behavioral analytics, strong identity and access controls, API security, fraud detection, and continuous threat intelligence. A proactive and adaptive security approach will be essential to mitigating AI-powered bot attacks and maintaining the resilience, integrity, and trustworthiness of digital commerce platforms.
India’s e-commerce sector has experienced rapid growth over the past decade, driven by increased internet penetration, widespread smartphone adoption, affordable mobile connectivity, and the expansion of digital payment systems. Millions of consumers now rely on online platforms for retail purchases, food delivery, grocery shopping, travel bookings, and other digital services. This growth has significantly expanded the attack surface available to cybercriminals.
Traditional attacks against e-commerce platforms primarily focused on exploiting software vulnerabilities or stealing customer information. However, threat actors are increasingly targeting business processes such as account authentication, checkout workflows, promotional campaigns, and payment systems through automated bot attacks. These attacks enable adversaries to conduct large-scale credential stuffing, account takeover, inventory hoarding, coupon abuse, and payment fraud with minimal manual effort.
Recent advancements in artificial intelligence (AI) have further transformed the threat landscape. AI-powered bots can interpret webpage content, adapt to interface changes, mimic legitimate user behavior, and execute complex tasks with limited human intervention. As a result, organizations face more sophisticated and scalable threats that can bypass traditional bot detection mechanisms.
India’s digital commerce ecosystem offers an attractive target for cybercriminals due to its large customer base, high transaction volumes, and frequent promotional events. Seasonal sales, flash discounts, and limited-time offers generate significant online activity, providing opportunities for automated bots to purchase high-demand products before legitimate customers can complete transactions.
The widespread use of digital payment methods, including UPI, digital wallets, and stored payment cards, also increases the potential financial rewards for attackers. Compromised customer accounts may provide access to payment information, loyalty rewards, saved addresses, and purchase histories that can be monetized through fraud or resold in underground marketplaces.
In addition, many Indian e-commerce platforms rely on interconnected ecosystems involving sellers, logistics providers, payment processors, and third-party service providers. The complexity of these environments increases the number of potential attack vectors and creates additional opportunities for adversaries to exploit weak or poorly secured components.
Automated attacks have evolved considerably from simple scripts designed to perform repetitive tasks. Early bots relied on predefined instructions and were easily disrupted by website updates or basic security controls.
Modern browser automation frameworks such as Selenium, Puppeteer, and Playwright enabled attackers to automate complex user interactions, including login processes, shopping cart management, and checkout workflows. While more effective than traditional scripts, these tools still required regular updates to remain functional.
The adoption of AI has significantly enhanced these capabilities. AI-powered bots can analyze webpage layouts, recognize interface elements, adapt to changes in application workflows, and simulate human browsing behavior. Some attacks now incorporate autonomous AI agents capable of making decisions during an attack, reducing the need for manual intervention and increasing operational efficiency.
| Category | Statistic | Why it is Relevant |
| E-commerce Market | India’s e-commerce market is projected to reach US$325–350 billion by 2030, making it one of the world’s fastest-growing digital commerce markets. | A rapidly expanding market presents an increasingly attractive target for financially motivated threat actors. |
| Digital Payments | UPI processed more than 10.5 billion monthly transactions, growing at over 90% CAGR since 2017. | High transaction volumes increase opportunities for account takeover, payment fraud, and automated attacks. |
| Internet Adoption | More than 86% of Indian households are connected to the internet. | Expands the attack surface for online retailers and digital platforms. |
| Bot-driven Attacks | An Indian security report observed a 48% increase in bot-driven attacks during 2024, with 9 out of 10 websites experiencing bot activity. | Indicates that automated attacks are becoming increasingly common across Indian-facing websites. |
| Holiday Season Activity | Bot attacks increased by 132% during holiday and festive shopping periods. | Particularly relevant to Indian e-commerce events such as Diwali sales, Great Indian Festival, and Big Billion Days. |
| Platform Fraud | 57% of fraud incidents reported by Indian organizations involved digital platforms, including e-commerce platforms. | Demonstrates that platform-based fraud has become a dominant concern for Indian businesses. |
| Payment Fraud | 92% of customer fraud reported in India involved payment fraud, including credit cards and digital wallets. | AI-powered bots frequently target payment workflows, stored cards, and digital wallets. |
AI-powered bots are automated software programs that leverage artificial intelligence (AI), machine learning (ML), and increasingly, large language models (LLMs) to perform tasks that traditionally required human interaction. Unlike conventional bots that execute predefined scripts and follow fixed rules, AI-powered bots can analyze their environment, make decisions based on real-time inputs, and modify their behavior to achieve specific objectives.
In the context of cyber threats, these bots are used to automate malicious activities, such as credential stuffing, account takeover (ATO), web scraping, inventory hoarding, gift card fraud, and payment abuse. Their ability to mimic legitimate customer behavior makes them significantly more difficult to detect and block than traditional automated bots.
| Traditional Bots | AI-Powered Bots |
| Follow predefined scripts and fixed rules. | Continuously adjust behavior based on environmental feedback and previous outcomes. |
| Execute repetitive tasks with little variation. | Employ machine learning to optimize attack strategies and improve success rates over time. |
| Produce predictable traffic patterns that are relatively easy to detect. | Generate human-like interactions, making detection significantly more difficult. |
| Require manual updates when websites or defences change. | Automatically adapt to changes in target applications and security controls. |
| Limited decision-making capabilities. | Capable of autonomous decision-making based on real-time analysis. |
| Primarily automate simple repetitive tasks. | Combine behavioral emulation, generative AI, and intelligent automation to execute complex attack workflows. |
The rapid growth of e-commerce has created an attractive environment for cybercriminals seeking scalable and profitable attack opportunities. Online retailers process millions of customer interactions daily, manage valuable personal and financial information, and rely heavily on digital services to conduct business. These characteristics make e-commerce platforms prime targets for AI-powered bot attacks, which can automate fraud, data theft, and service abuse at unprecedented speed and scale.
Unlike traditional cyberattacks that often require manual intervention, AI-powered bots enable attackers to conduct continuous, adaptive, and highly automated campaigns. By leveraging machine learning, behavioral emulation, and generative AI, these bots can mimic legitimate customers, evade security controls, and simultaneously target thousands of user accounts or transactions.
Financially Motivated Cybercriminals: Financially motivated cybercriminals represent the largest group targeting e-commerce organizations. Their primary objective is to generate revenue through activities such as credential theft, payment fraud, account takeover (ATO), gift card abuse, and refund fraud. These actors increasingly leverage AI-powered bots to automate attacks, maximize efficiency, and evade detection. They often rely on previously leaked credentials, stolen payment card data, and residential proxy services to conduct large-scale campaigns while minimizing the likelihood of detection.
Organized Fraud Groups: Organized fraud groups operate as well-coordinated criminal enterprises with specialized roles and resources. These groups commonly deploy bot farms and distributed automation infrastructure to exploit promotional campaigns, limited-time discounts, product launches, and inventory shortages. Their operations may involve hundreds or thousands of coordinated bot instances capable of purchasing inventory within seconds of product availability. Profits are typically generated through product resale, fraudulent refunds, or exploitation of promotional incentives.
Initial Access Brokers (IABs): Initial Access Brokers specialize in obtaining unauthorized access to user or corporate accounts rather than directly exploiting those accounts themselves. They harvest credentials through phishing campaigns, infostealer malware, credential stuffing attacks, or previously disclosed data breaches. Once validated, these credentials are sold or auctioned on underground marketplaces to other threat actors, including ransomware groups, fraud operators, and financially motivated cybercriminals. AI-powered bots assist these actors by rapidly validating large credential datasets against multiple online services.
Bot-as-a-Service (BaaS) Providers: Bot-as-a-Service providers develop, maintain, and commercialize sophisticated bot frameworks that enable less technically skilled individuals to conduct advanced cyberattacks. Like legitimate Software-as-a-Service (SaaS) business models, these criminal services provide subscription-based access to AI-enhanced bots, residential proxy integration, CAPTCHA-solving capabilities, account management features, and automated attack modules. This service model lowers the technical barrier to entry and has significantly expanded the accessibility of sophisticated bot attacks.
Credential Stuffing: Credential stuffing is one of the most prevalent bot-driven attacks targeting e-commerce platforms. Attackers obtain username and password combinations from previous data breaches and automatically test these credentials against retailer login portals. Because many users reuse passwords across multiple online services, a single breached credential set can successfully compromise accounts on unrelated platforms.
AI-powered bots significantly enhance credential stuffing by learning from unsuccessful login attempts and modifying their attack strategies in real time. Rather than submitting requests at fixed intervals from a single IP address, these bots dynamically adjust request frequency, rotate residential proxy IPs, randomize browser fingerprints, and emulate realistic user behavior to reduce the likelihood of detection.
Account Takeover (ATO): Account takeover occurs when attackers successfully gain unauthorized access to legitimate customer accounts using stolen credentials, phishing, session hijacking, or credential stuffing attacks. Once authenticated, attackers can exploit the trust associated with legitimate accounts to conduct fraudulent activities without immediately triggering security alerts.
AI-powered bots enable attackers to automate account validation, identify accounts with stored payment methods or loyalty balances, and prioritize high-value targets. Some bots also monitor compromised accounts over extended periods to avoid suspicious activity and maximize financial gains.
Inventory Hoarding and Scalping: Inventory hoarding involves the automated purchase or reservation of high-demand products immediately after they become available. AI-powered bots enable attackers to monitor inventory levels, predict product release times, and coordinate purchases across numerous customer accounts simultaneously.
Machine learning algorithms can analyze historical release schedules, purchasing patterns, and competitor activity to optimize purchase timing. This enables attackers to acquire significant portions of available inventory within seconds, leaving legitimate customers unable to complete purchases.
Gift Card Fraud: Gift cards represent an attractive target because they function as transferable digital assets that are difficult to recover once redeemed. Attackers use AI-powered bots to systematically test stolen or generated gift card numbers, identify valid balances, and automate redemption before legitimate customers can use them.
Bots can rapidly cycle through thousands of gift card combinations while adjusting request rates to avoid triggering fraud detection systems. Successfully redeemed gift cards are frequently resold through underground marketplaces or converted into physical merchandise for resale.
Web Scraping and Competitive Intelligence: Web scraping involves the automated extraction of publicly accessible information from e-commerce websites. While legitimate businesses may use scraping for market research, malicious actors employ AI-powered bots to collect pricing information, product catalogues, inventory levels, promotional offers, and customer reviews at large scale.
Machine learning techniques enable bots to navigate complex websites, bypass basic anti-scraping controls, and continuously monitor pricing changes. Competitors may use this information to dynamically adjust pricing strategies, while criminal groups may exploit it to identify high-value products or optimize fraud campaigns.
Fake Account Creation: Generative AI has significantly improved attackers’ ability to create convincing synthetic identities. AI-powered bots can automatically generate realistic names, email addresses, phone numbers, profile images, and supporting content, allowing them to register large numbers of fraudulent customer accounts.
These synthetic accounts are subsequently used to exploit promotional offers, manipulate referral programs, submit fake reviews, conduct refund fraud, or support money laundering operations. Because the generated identities appear increasingly realistic, distinguishing them from legitimate users has become more difficult.
Payment Fraud and Card Testing: Card testing is a technique in which attackers validate stolen payment card information by performing numerous low-value transactions against online merchants. AI-powered bots automate this process by distributing transactions across multiple merchants, payment gateways, IP addresses, and customer accounts, reducing the likelihood of triggering fraud detection systems.
Once valid payment cards are identified, they may be used for larger fraudulent purchases or sold within underground criminal marketplaces.
AI-powered bot attacks extend beyond technical security incidents and can have significant financial, operational, reputational, and regulatory consequences for e-commerce organizations. By automating malicious activities at scale and continuously adapting to security controls, these attacks can disrupt business operations, increase fraud-related losses, and erode customer trust. As AI technologies continue to evolve, the potential impact of bot-driven attacks is expected to become more severe and difficult to mitigate.
Revenue Loss: AI-powered bots can directly reduce revenue by preventing legitimate customers from completing purchases, exploiting promotional campaigns, and conducting fraudulent transactions. During inventory hoarding attacks, bots rapidly purchase high-demand products, leaving genuine customers unable to access limited stock. This not only results in lost sales opportunities but may also drive customers toward competitors. Additionally, bots can abuse discount codes, referral programs, and promotional offers, causing organizations to incur unexpected financial losses while undermining marketing initiatives designed to attract legitimate customers.
Fraudulent Transactions: Compromised customer accounts frequently contain stored payment methods, shipping addresses, and loyalty rewards that attackers can exploit for unauthorized purchases. AI-powered bots enable threat actors to automate credential validation, identify high-value accounts, and execute fraudulent transactions within minutes. Payment fraud, card testing attacks, refund fraud, and gift card abuse can collectively result in significant financial losses, increased chargebacks, and higher payment processing costs.
Increased Operational Costs: Responding to bot attacks often requires substantial investment in cybersecurity technologies, fraud prevention solutions, infrastructure scaling, and incident response activities. Organizations may also incur additional costs associated with forensic investigations, customer support, password reset campaigns, legal services, and system remediation. Over time, repeated bot attacks can significantly increase the overall cost of operating an e-commerce platform.
Service Degradation: Large-scale bot campaigns generate significant volumes of automated traffic that can consume application resources and degrade website performance. Increased server utilization, database queries, and API requests may result in slower page load times, checkout failures, and intermittent service disruptions for legitimate customers. During major shopping events or promotional campaigns, these disruptions can directly affect sales and customer satisfaction.
Inventory Disruption: AI-powered bots can rapidly reserve or purchase limited inventory, creating artificial product shortages. This practice, commonly associated with inventory hoarding and scalping, prevents legitimate customers from purchasing popular products and disrupts normal inventory management processes. Retailers may also experience inaccurate inventory forecasting, increased order cancellations, and customer complaints related to product availability.
Infrastructure Strain: Continuous automated requests generated by malicious bots place considerable strain on web servers, content delivery networks (CDNs), APIs, and backend systems. Organizations may need to provision additional computing resources or increase cloud infrastructure capacity to maintain service availability during bot attacks. These additional resource requirements can substantially increase operational expenses while affecting overall system performance.
Customer Dissatisfaction: Customers expect online shopping platforms to provide secure, reliable, and uninterrupted services. AI-powered bot attacks that result in account compromise, failed purchases, fraudulent transactions, or prolonged service disruptions can significantly diminish the customer experience. Victims of account takeover or payment fraud may lose confidence in the organization’s ability to protect their personal information and financial assets.
Brand Damage: Public disclosure of security incidents involving large-scale bot attacks or customer account compromises can negatively affect an organization’s reputation. Media coverage, social media discussions, and negative customer reviews may reduce public confidence in the brand, particularly if the organization is perceived as having inadequate security controls or a delayed incident response.
Reduced Customer Retention: Loss of customer trust frequently translates into reduced customer loyalty and increased customer attrition. Consumers who experience fraud or account compromise may choose to discontinue using the affected platform and migrate to competitors offering stronger security measures. Declining customer retention can have long-term implications for revenue growth and overall business performance.
AI-powered bot attacks are rapidly reshaping the threat landscape for the e-commerce sector by enabling cybercriminals to automate, scale, and adapt malicious activities with unprecedented efficiency. By leveraging artificial intelligence, machine learning, and generative AI, threat actors can conduct sophisticated attacks, such as credential stuffing, account takeover, inventory hoarding, payment fraud, fake account creation, and web scraping, while effectively evading traditional security controls.
As the adoption of AI continues to accelerate, the sophistication and volume of bot-driven attacks are expected to increase, posing significant financial, operational, reputational, and regulatory risks to e-commerce organizations. Defending against these threats requires a proactive, layered security strategy that combines advanced bot management, behavioral analytics, AI-driven threat detection, robust identity and access controls, API security, and continuous threat intelligence. Organizations that invest in adaptive security capabilities and continuously monitor the evolving threat landscape will be better positioned to detect, mitigate, and respond to AI-powered bot attacks while maintaining customer trust and business resilience.
