How do scammers bypass security systems? (Examples of real attacks, system vulnerabilities)

[Student]

What is carding and why is it important?​

Carding is a type of cyber fraud involving the theft and illegal use of credit or debit card information. Fraudsters (carders) gain access to card numbers, expiration dates, CVV codes, and other details to make purchases, transfers, or sell these details on the black market. This isn't just theft—it's an entire ecosystem that includes card validation testing, bypassing fraud detection systems, and monetization. As of 2025, carding remains one of the leading threats in e-commerce, causing billions of dollars in losses annually. For educational purposes, it's important to understand how it works at a high level to better protect yourself, but remember: any attempts to replicate it are illegal and punishable by law.

Carding evolves with technology, from simple theft to the use of AI, bots, and social media to spread knowledge. Fraudsters often share methods in closed forums or Telegram channels where they discuss how to bypass anti-fraud systems (fraud detection systems). The main stages are: data acquisition, validation (testing), exploitation, and laundering.

How fraudsters obtain card data​

Map data is stolen through a combination of technical and social methods. Here are the high-level methods:

• Data breaches: Large-scale hacks of company databases storing millions of cards. Vulnerabilities include weak encryption and out-of-date software.
• Phishing and its variations: Deceptive websites or emails impersonating banks or stores. Fraudsters use legitimate services (such as Google Translate) to bypass filters.
• Skimming: Devices at ATMs or POS terminals that copy data from a magnetic stripe. In 2025, this evolved into NFC skimming via mobile wallets (Apple Pay, Google Wallet).
• Darknet purchases: Ready-made card dumps sell for pennies. Vulnerabilities: lack of strict verification in P2P networks.

Example: In 2024–2025, attacks on retailers will increase, with bots testing thousands of cards in minutes, exploiting leaks from past breaches.

How scammers bypass carding security systems​

Security systems include CVV checks, address verification (AVS), multi-factor authentication (MFA), transaction limits, AI-based behavior monitoring, and velocity checks. Fraudsters bypass these systems using automation and obfuscation. Here are the key methods at a high level:

1. Card testing (carding attacks or card cracking): Fraudsters make small purchases (under $10) on multiple websites to check the validity of cards without attracting attention. Bots automate the process by changing IP addresses through a proxy/VPN to bypass geolocation or speed restrictions.
   • Vulnerabilities: Weak CAPTCHA, no device fingerprinting, ineffective rate limiting.
   • Examples: In 2025, bots are used for "gift card cracking"—testing gift cards with weak security. Fraudsters mask their IP addresses, create fake accounts, and use different delivery addresses to evade detection. One real-life example: attacks on e-commerce, where bots conduct thousands of transactions, leading to chargebacks and penalties from Visa/Mastercard.
2. Bypassing MFA and authentication: MFA (SMS codes, tokens) is bypassed through SIM swapping (intercepting the phone number), social engineering (persuading the victim to share the code) or consent phishing (fake OAuth pages).
   • Vulnerabilities: Dependence on SMS (vulnerable to interception), weak tokens, lack of hardware keys.
   • Examples: In 2025, NFC carders steal data through phishing and add cards to their mobile wallets, bypassing the PIN. A real-life example: scammers impersonate couriers (DHL), tricking them into providing data and MFA codes to add cards to Apple/Google Wallet. Another example: attacks on ATMs using cardboard blockers to steal cash after entering the PIN.
3. Use of bots and automation: Bots imitate human behavior to evade AI detection. They change devices, browsers, and geolocation.
   • Vulnerabilities: Traditional filters (CAPTCHA, IP blocks) are ineffective against advanced bots; lack of behavioral analysis.
   • Examples: In 2024–2025, bots will be used for distributed guessing (generating card numbers based on BIN patterns). A real-life example: attacks on stores where bots test cards, leading to losses from chargebacks. Fraudsters are also using AI for voice cloning in vishing attacks to deceive banks.
4. Social engineering in carding: Impersonating bank employees to obtain data or codes. Includes cold calls offering to "deliver a new card."
   • Vulnerabilities: Lack of awareness, lack of verification.
   • Examples: In 2025, scammers call elderly people impersonating a bank and exchange real cards for fake ones. Another: using stolen cards to pay victims' bills to disguise the fraud.
5. Monetization and laundering: After validation, cards are used to purchase gift cards or resale items. Bypassing the process through fake accounts and proxies.
   • Vulnerabilities: Weak checks in gift card systems, lack of global monitoring.
   • Examples: "Gift card draining" – theft of gift card activations in stores. A real-life example: scammers duplicate cards and drain balances from another state.

Real-life examples of attacks in 2024–2025​

• E-commerce attacks: Bots test thousands of cards, causing chargebacks. Example: Losses from card testing in retail, where fraudsters bypass velocity checks.
• NFC and mobile wallets: Phishing to steal data and add it to Wallet. Example: Attacks via fake DHL websites.
• Social attacks: Impersonating couriers or banks. Example: Delivering fake cards to the elderly. Or using AI for voice cloning in ATO (account takeover).
• QR codes and new vectors: Substitution of QR codes in stores to intercept payments.

System Vulnerabilities and Trends to 2025​

• Technical: Open APIs, weak encryption, reliance on SMS-MFA.
• Human: Lack of education, trust in calls/emails.
• Trends: Growth of synthetic identity fraud (creation of fake identities), use of AI for adaptive attacks, integration with other fraudulent methods (ATO, phishing). Bots are predicted to become even smarter, mimicking behavior to evade machine learning detection.

Recommendations for protection (for education)​

• For users: Use hardware MFA (not SMS), monitor transactions, and use antivirus software with anti-phishing features. Pay through Goods & Services (e.g., PayPal) for security.
• For businesses: Implement device fingerprinting, behavioral ML, velocity rules, and 3D Secure. Monitor the dark web for threats.
• General: Regular audits, phishing education. If you suspect fraud, report it to your bank or the police immediately.

This overview is based on general knowledge and cybersecurity reports. For a more in-depth analysis, consult sources like FICO or Kaspersky. Remember: the goal is prevention, not intervention.