Online shopping has become a daily scrolling app for users across the globe. Some users visit them to check whether the price has slashed, while others visit for immediate purchase. According to Statista's report, the e-commerce market will show an annual growth rate (CAGR from 2025 to 2029) of 8.02%. The projected market of e-commerce will be 5,887.00 billion USD by 2029.
Because of the growing number of users of these e-commerce apps, the platform has become the primary target for cybercriminals. Most e-commerce businesses leverage Artificial Intelligence (AI) and Machine Learning (ML) models to identify purchasing frauds and automate payment processing algorithms. However, through data poisoning attacks, cybercriminals can raise concerns regarding how to pinpoint online fraud among business stakeholders and information security executives.
Let’s deliver a complete walkthrough on data poisoning in AI's learning models, its implications in e-commerce, what attacks cybercriminals can deploy on payment systems, and how to mitigate such attack vectors with best practices.
What is Data Poisoning?
Data poisoning is an attack vector wherein the attacker injects manipulated or polluted data within the AI's training dataset. The AI we see today uses machine learning models where it trains on millions of data to understand the use of its algorithm. Attackers use data poisoning to push misleading or incorrect data within the training dataset.
It modifies the existence of the legitimate Machine Learning model or alters different data pointers - leading to a potential impact on the working system. Some well-known techniques attackers use in data poisoning attacks are label flipping, classification altering, feature manipulation, backdoor attacks, and data pollution through injection.
Categories of Data Poisoning Attacks
We can classify the data poisoning attacks into two types. These are:
Direct data poisoning attacks: Such attacks are called targeted attacks, wherein the attacker modifies the datasets associated with the ML model. It led to a change in the behavior of the ML model in a different way for specific inputs while preserving the model's performance otherwise. In this data poisoning attack approach, the goal of the cybercriminal is to misclassify or misinterpret particular data without disturbing the other functionalities. As a result, when the attacker interacts with the system or AI model in a real-world scenario, it may allow them to perform some prohibited tasks.
Indirect data poisoning attacks: They are non-targeted attacks and are not specific to benefit a single attacker's group or alter a particular functionality. Instead, such an attack distorts the entire ML model by injecting noisy data or irrelevant facts or values within the datasets used for training the AI model. Through this data poisoning attack technique, the cybercriminal reduces the effectiveness or working potential of the AI model.
Different Ways of Poisoning Training Datasets
If enterprises and security experts can comprehend the strategy behind how attackers poison the training datasets, it can empower the organization to safeguard against such attacks. Identifying the attack approach can also help security experts take proactive measures to devise suitable mitigation plans. Here are the three different ways attackers execute a data poisoning attack:
Injecting noisy data: Attackers often perform data poisoning by contaminating the actual dataset with unreal or deceptive data points, along with the real ones. It can lead to inaccurate training and predictions of the AI models. For example, in e-commerce systems, manipulating the AI recommendation system can lead to a loss of business revenue. The recommendation system might exhibit incorrect customer ratings, making the app's customers judge the product or brand.
Modifying existing datasets: Attackers might also alter the genuine data points with inaccurate data. It will mislead the ML algorithm without adding noisy data. One common example is when the attacker changes the values in a financial transaction dataset. It may compromise the AI-powered fraud detection systems, develop anomalies in automatically analyzing cyber attacks (increasing the chances of false negatives), or create miscalculations around profits or losses.
Deleting actual data points: Attackers also perform data poisoning by removing critical data points from a dataset, which helps in AI model training. It creates a gap that can lead to poor model generation. Through this attack approach, cybercriminals can modify an AI system that will become blind to particular attack patterns, bypassing the attacker to perform fraudulent actions on the e-commerce or other systems.
How does Data Poisoning target E-commerce?
E-commerce applications and businesses leverage artificial intelligence and machine learning algorithms in several ways. From understanding customers, delivering personalized recommendations, dynamic pricing, virtual assistants, chatbots, and financial fraud detection and prevention, e-commerce businesses can implement AI/ML in many ways.
However, the concern pivots when businesses use AI and ML in financial security solutions. E-commerce systems use ML-based security solutions to identify fraud, detect transaction anomalies, and prevent chargebacks. Attackers target such business tools and technologies that use ML-powered security. Here are some of the systems where attackers can perform data poisoning:
1. Fraud Detection Solution
Attackers can manipulate fraud detection AI models by contaminating the datasets. They change the data or delete some data points containing historical transactions. Such misleading data injection tricks the AI model into misclassifying the fraudulent transaction as legitimate. Also, over the course of time, the AI model learns to ignore the fraudulent transaction. That makes any e-commerce app-based transaction vulnerable to cyber threats.
2. Payment gateway compromise
Payment gateways are intermediary technologies between an e-commerce app and a transaction firm or payment service. These payment gateways use fraud-scoring algorithms to flag users. Performing data poisoning attacks on these fraud-scoring AI systems can lead to lower fraud detection thresholds and increase the chances of transaction risks, making high-risk transactions appear safe. By tampering with AI data models, fraudsters can easily perform suspicious payments without getting noticed.
3. Financial identity verification
Modern e-commerce apps use biometric authentication and other MFAs to verify users before making payments. Through data poisoning attacks, cybercriminals can manipulate identity data by injecting fake user identity verification to pass authentication checks during financial transactions in e-commerce apps. Apart from spoofing legitimate users' identities, they can also inject fake user behavior to mimic real customers' using bots.
Apart from all these major attack vectors, attackers can use data poisoning to distort credit scores, mislead transaction anomalies, alter loyalty and reward mechanisms, and influence chargeback prevention mechanisms. Thus, data poisoning attacks on AI/ML systems can pose a stringent threat to e-commerce apps' payment systems.
Consequences of Data Poisoning Attack on E-commerce
There are various drawbacks an e-commerce business has to face with such an attack.
Financial loss: With anomalies in AI/ML systems in the app's payment system, the platform has to face chargebacks and loss in business revenue.
Trust issues among customers: Repeating fraudulent incidents portrays a bad brand reputation among customers, reducing customer trust and confidence.
Undervalue AI/ML usage: E-commerce apps spend a lot of money to build AI/ML systems for financial security. However, data poisoning attacks on these payment systems undervalue their use and minimize the return on investment (ROI).
Compliance risks and lawsuits: Data breaches and adulteration can violate data regulations and complaints for the e-commerce business. It can also drag the firm to face lawsuits.
Strategies to Mitigate Data Poisoning Attacks
Let us explore some mitigation techniques enterprises should adopt to prevent data poisoning attacks on payment systems.
Data validation and filtering: Enterprises should perform frequent data quality checks to identify and fix anomalies. Implementing robust data validation and filtering through hashing and digital signatures can ensure data integrity.
Robust fraud detection ML layer: Payment systems in e-commerce apps should also use an additional AI/ML algorithm trained to identify adversarial attacks. Such a security layer should expose data contamination to improve resilience against data poisoning.
Regular audits: Datasets used in ML model training to detect payment frauds go through an ongoing update. Therefore, periodic auditing of datasets is essential to remove corrupted or noisy data injected by attackers.
Blockchain-based transactions: Developing secure transaction histories can prevent unauthorized altering of e-commerce data. Again, keeping a frequent auto-backup of legitimate datasets through an immutable ledger is essential. It can help maintain logs of which datasets are being tampered with by attackers and when.
Conclusion
We hope this article provided a crisp idea of data poisoning and how it has become a growing threat to AI/ML models. Attackers are trying to exploit ML models to bypass payment systems that use AI to detect fraud in financial transactions. Therefore, enterprises should take proactive mitigation measures through technologies like blockchain, hashing, and continuous audit or monitoring frameworks to stay ahead of such cybercriminals. To read more such articles or know more about our solutions visit us directly or contact us.
