Nfc Based Secure Payment Authentication System Python — Complete Documentation & Simulation

Overview & System Context

Nfc Based Secure Payment Authentication System Python: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~8510 Words · Real Software Screenshots: 3 1. Executive Overview & Technical Background The Flipper Zero is a portable multi-functional security device developed for interaction with access control systems. The device is able to read, copy, and emulate RFID and NFC tags, radio remotes, iButtons, and digital access keys. It also has a GPIO interface. It was first announced in August 2020 through the Kickstarter crowdfunding campaign, which raised $4.8 million. The first devices were delivered to backers 18 months after completion of the crowdfunding campaign. The device's user interface embodies a pixel art dolphin virtual pet. The interaction with the virtual pet is the device's core game mechanic.

The usage of the device's functions defines the appearance and emotions of the pet. In the built-in game, the main mechanism to "upgrade" the dolphin is to use the various hacking tools. While harmless uses (like as a remote control for a television, or carbon dioxide sensor) exist, some of the built-in tools have potential criminal uses, including RFID skimming, Bluetooth spamming (spamming a Bluetooth connection, crashing a person's phone), and emulation of RFID chips such as those found in identification badges, using the built-in radio cloner to open garage doors, unlocking cars, and functioning as a wireless BadUSB.

Main PCB (motherboard) – contains core components, including the main processor (STM32WB55), GPIO breakout pins, LCD, Sub-GHz chip and its antenna, Bluetooth antenna, microSD card slot, battery controller, USB Type-C port, and membrane switches for the D-pad. All additional PCBs connect to the main PCB via flexible cables. Infrared and iButton PCB – a small board equipped with an infrared receiver (TSOP-75338) and three infrared LEDs for transmitting infrared signals.

Includes three pogo pins for iButton (1-Wire) tags and a piezo buzzer (BCE-MX8530A) for audio feedback. NFC PCB – contains the NFC chip (STM ST25R3916) along with analog circuitry for 125 kHz RFID. Dual-Band RFID Antenna PCB – features two passive coil antennas: one for 13.56 MHz NFC communication and another for 125 kHz RFID systems. BlueTooth: STMicroelectronics STM32WB55, a dual-core ARM microcontroller that has an integrated Bluetooth Low Energy (BLE) radio Flipper Zero is based on a dual-core ARM architecture STM32WB55 microcontroller, which has 256 KB of RAM and 1 MB of Flash storage. The first core is a 64 MHz Cortex-M4 which runs the main firmware. The second core is a 32 MHz Cortex-M0 which runs STMicroelectronics proprietary firmware that implements the Bluetooth Low Energy protocol. Secret keys stored in the Secure Enclave of STM32WB55 are used to decrypt cryptographic keys on the fly, which are then applied to decode Sub-GHz protocols. This mechanism allows the device to handle encrypted communication for Sub-GHz protocols. However, the encryption used is not entirely secure and primarily serves as a form of obfuscation rather than robust protection. Its purpose is to make reverse engineering more challenging, but it does not provide absolute security.

The Flipper Zero firmware is based on the FreeRTOS operating system, with its own software abstraction over the hardware layer. The firmware is mostly written in the C programming language, with occasional use of C++ in third-party modules. The system uses multitasking in combination with an event-driven architecture to organize the interaction of applications and services executed in a single address space and communicating through a system of queues and events. The system can be executed from both random-access memory (RAM) and read-only memory (ROM). Execution from RAM is used to deliver over-the-air (OTA) firmware updates. The firmware consists of the following components: Figure 1: GitHub - Sharath16113/Secured-ATM-Transaction-using-NFC-Technology ...

FuriCore – provides an API for interaction with the scheduler and multithreading. FuriCore abstracts and extends the functionality of the FreeRTOS scheduler and adds additional system primitives. FuriHal – provides an API for interaction with hardware. Services and applications – the main functionality of the device. Sub-GHz, Infrared, RFID, NFC, etc are applications for user interaction. Graphical user interface (GUI), command-line interface (CLI), Notification, Storage, etc are additional APIs for applications development. A set of libraries and drivers – covers various communication protocols, device drivers, file system drivers, and developer tools. User and system data is stored in built-in flash memory, which is based on the LittleFS library. Interaction with the file system on the SD card is implemented using the FatFs library. The build system is based on the SCons tool with additional tooling written in Python. For compilation, the system uses its own open toolchain based on GNU Compiler Collection.

Keynote: Presentations remote Keyboard: Double as a keyboard for a computer Media: Controls media on a computer, camera remote control for a phone Mouse: Double as a mouse for a computer TikTok Controller: Control TikTok app on a phone Mouse Jiggler: Duplicate mouse movements on a computer to keep computer showing as active at all times PTT : use the flipper as a push to talk (PTT) controller / wireless PTT remote In September 2023 the ability to launch Bluetooth Low Energy spam attacks with a Flipper Zero was demonstrated by a security researcher known as 'Techryptic'. A custom Flipper Zero firmware was developed shortly afterward that could launch spam attacks against Android devices and Microsoft Windows computers. An Android app to launch BLE attacks was developed shortly afterwards. At the 2023 Midwest FurFest attendees reported severe disruption of Square payment readers and an insulin pump controller crashed due to the BLE spam. A researcher known as Remy said to Bleeping Computer: "For BTLE enabled medical equipment, at minimum a disruption results in a degraded quality of life for those affected," adding "Some conditions may not be life threatening to have disruptions. Others may not be so lucky." As a result, a Python script was developed, called the Wall of Flippers project for Linux and Windows to detect BLE spam attacks coming from Flipper and Android devices.

Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal. The Payment Card Industry Data Security Standard (PCI DSS) is the data security standard created to help financial institutions process card payments securely and reduce card fraud. Credit card fraud can be authorised, where the genuine customer themselves processes payment to another account which is controlled by a criminal, or unauthorised, where the account holder does not provide authorisation for the payment to proceed and the transaction is carried out by a third party. In 2018, unauthorised financial fraud losses across payment cards and remote banking totalled £844.8 million in the United Kingdom.

Banks and card companies prevented £1.66 billion in unauthorised fraud in 2018. That is the equivalent to £2 in every £3 of attempted fraud being stopped. Credit card fraud can occur when unauthorized users gain access to an individual's credit card information in order to make purchases, other transactions, or open new accounts. A few examples of credit card fraud include account takeover fraud, new account fraud, cloned cards, and cards-not-present schemes. This unauthorized access occurs through phishing, skimming, and information sharing by a user, oftentimes unknowingly. However, this type of fraud can be detected through means of artificial intelligence and machine learning as well as prevented by issuers, institutions, and individual cardholders. According to a 2021 annual report, about 50% of all Americans have experienced a fraudulent charge on their credit or debit cards, and more than one in three credit or debit card holders have experienced fraud multiple times.

This amounts to 127 million people in the US who have been victims of credit card theft at least once. Regulators, card providers and banks take considerable time and effort to collaborate with investigators worldwide with the goal of ensuring fraudsters are not successful. Cardholders' money is usually protected from scammers with regulations that make the card provider and bank accountable. The technology and security measures behind credit cards are continuously advancing, adding barriers for fraudsters attempting to steal money.

Figure 2: NFCPay – Contactless Payment System Full Solution 1.2.0 - Codezon Card information is stored in a number of formats. Card numbers – formally the Primary Account Number (PAN) – are often embossed or imprinted on the card, and a magnetic stripe on the back contains the data in a machine-readable format. Fields can vary, but the most common include the Name of the cardholder; Card number; Expiration date; and Verification CVV code. In Europe and Canada, most cards are equipped with an EMV chip which requires a 4 to 6 digit PIN to be entered into the merchant's terminal before payment will be authorized. However, a PIN is not required for online transactions. In some European countries, buyers using a card without a chip may be asked for photo ID at the point of sale. In some countries, a credit card holder can make a contactless payment for goods or services by tapping their card against an RFID or NFC reader without the need for a PIN or signature if the cost falls under a pre-determined limit. However, a stolen credit or debit card could be used for a number of smaller transactions prior to the fraudulent activity being flagged. Card issuers maintain several countermeasures, including software that can estimate the probability of fraud. For example, a large transaction occurring a great distance from the cardholder's home might seem suspicious. The merchant may be instructed to call the card issuer for verification or to decline the transaction, or even to hold the card and refuse to return it to the customer.

Skimming is the theft of personal information that has been used in an otherwise normal transaction. The thief can procure a victim's card number using basic methods such as photocopying receipts or more advanced methods such as using a small electronic device (skimmer) to swipe and store hundreds of victims' card numbers. Common scenarios for skimming are taxis, restaurants or bars where the skimmer has possession of the victim's payment card out of their immediate view. The thief may also use a small keypad to unobtrusively transcribe the three or four-digit card security code, which is not present on the magnetic strip. Call centers are another area where skimming can easily occur. Skimming can also occur at merchants when a third-party card-reading device is installed outside a card-swiping terminal. This device allows a thief to capture a customer's card information, including their PIN, with each card swipe. Skimming is difficult for the typical cardholder to detect, but given a large enough sample, it is fairly easy for the card issuer to detect. The issuer collects a list of all the cardholders who have complained about fraudulent transactions, and then uses data mining to discover relationships among them and the merchants they use. Sophisticated algorithms can also search for patterns of fraud. Merchants must ensure the physical security of their terminals, and penalties for merchants can be severe if they are compromised, ranging from large fines by the issuer to complete exclusion from the system, which can be a death blow to businesses such as restaurants where credit card transactions are the norm. Instances of skimming have been reported where the perpetrator has put over the card slot of an automated teller machine, a device that reads the magnetic strip as the user unknowingly passes their card through it. These devices are often used in conjunction with a miniature camera to read the user's personal identification number at the same time. This method has been used in many parts of the world including South America and Europe.

Information sharing is the transfer or exchange of data between individuals, companies, organizations, and technologies. Advances in technology, the internet, and networks have accelerated the growth of information sharing. Information can be transmitted and treated at speeds faster than ever before. Individuals may lack a sense of how much sensitive personal information they may themselves share on a given day. For example, when purchasing goods online, an individual provides their name, email address, home address, and credit card information, all of which may be stored and shared with third parties for purposes of tracking the individual's future purchases. Organizations may go to great lengths to keep individuals' personal information secure in their databases, but historically hackers have often still managed to compromise organizations' security and gain access to immense amounts of data. One of the largest data breaches occurred in 2013 when hackers targeted the point-of-sale system of American discount retailer Target, compromising the personal data of roughly 40 million shoppers.

CNN Business reported that "they either slipped malware into the terminals where customers swipe their credit cards, or they collected customer data while it was on route from Target to its credit card processors." In just one single purchase at the register, masses of personal data is collected which when stolen has major ramifications. The financial market infrastructure and payment system will continue to be a work-in-progress as it continually battles with security hackers.

In Australia, credit card fraud is considered a form of identity crime. The Australian Transaction Reports and Analysis Centre has established standard definitions in relation to identity crime for use by law enforcement across Australia: Figure 3: GitHub - Flexsidee/nfc-card-payment: A web-based IoT project for ... The term identity encompasses the identity of natural persons (living or deceased) and the identity of bodies corporate Identity fabrication describes the creation of a fictitious identity Identity manipulation describes the alteration of one's own identity Identity theft describes the theft or assumption of a pre-existing identity (or significant part thereof), with or without consent and whether, in the case of an individual, the person is living or deceased Identity crime is a generic term to describe activities/offences in which a perpetrator uses a fabricated identity, a manipulated identity, or a stolen/assumed identity to facilitate the commission of a crime(s).

Millennials are the biggest victims of all fraud, including credit and debit card fraud, digital wallet, digital payment, banking and tax fraud. Followed by them are the GenXers and then the GenZers. Millennials spend the most time trying to recover money lost due to fraudulent charges, disputing fraudulent charges, and checking accounts for fraudulent or unusual activity out of any of the generational groups. GenZers experienced fraud most often through digital payment apps such as PayPal, Venmo and Square. The other generations experienced most of their issues through credit card fraud. Baby Boomers were found to have the lowest instances of fraudulent charges, and also spent the least amount of time trying to recover money due to fraudulent charges or to dispute these charges.

"The Federal Trade Commission ("FTC") and the Consumer Financial Protection Bureau ("CFPB") produced reports on the connection between minority populations and consumer issues. Each report came to the same conclusion: unfair and deceptive practices have unique and disproportionate impacts on communities of color. These findings suggest that more needs to be done to protect these communities from fraud." On top of this, hackers specifically target communities of color for reasons such as their need for additional income or credit, or their tendency to use certain types of financial products.

Additional report findings: While Black and Latino consumers are more likely to experience fraud, Latino communities predominantly underreport compared with Black and White communities. Latino and Black consumers report different rates of fraud concerning distinct categories of problem. The FTC found that their complaint database showed Black, and to a lesser extent Latino, communities experience higher rates of problems with credit bureaus and debt collections than White communities. White and Latino communities experience higher rates of impersonator scams than Black communities. Also, according to FTC payment method data, Black and Latino communities use credit cards, with their accompanying legal protections, at a substantially lower rate than in White communities.

Using the nfcpy library for NFC programming in Python. Covers tag discovery, NDEF operations, card emulation, and LLCP peer-to-peer. nfcpy by Stephen Tiedemann is the most mature Python library for NFC reader hardware, supporting contactless cards, tag operations, and peer-to-peer sessions on Linux and macOS. It communicates with USB, SPI, and UART-connected NFC reader ICs including the PN532, PN533, RCS380, and ACR122U.

nfc.ContactlessFrontend accepts: - 'usb' — auto-detect first USB NFC reader - 'usb:072f:2200' — specific vendor/product IDs - 'tty:S0:pn532' — serial UART device - 'i2c:0' — I2C bus (Raspberry Pi) The PN532 over I2C is the most common setup for Raspberry Pi projects. See NFC Arduino and Raspberry Pi for wiring. The tag.ndef attribute is None if the tag has no NDEF capability container or is not NDEF-formatted. All record types map to Python objects via the companion ndeflib package ( pip install ndeflib ).

For operations beyond NDEF — reading raw memory blocks, setting password-protection , or writing otp bytes — access the tag's underlying type object: Always test password configurations with a test tag before deploying. Incorrect access-control-bits can permanently lock a tag.

llcp and snep are used for peer-to-peer data exchange and legacy Android Beam compatibility. The interval parameter (seconds) controls how quickly the reader polls for new tags after the previous one is removed. Use 0.1 s for rapid batch processing. See also: NFC Arduino and Raspberry Pi | ESP32 NFC Development | NDEF Specification Deep Dive | NFC Tag Types Explained Our guides cover a range of experience levels. Getting Started guides are written for beginners with no prior NFC knowledge.

Programming guides target developers integrating NFC into mobile apps or embedded systems. Security guides are for engineers designing secure NFC deployments for payments, access control, or authentication. Most guides require only an NFC-enabled smartphone (iPhone 7+ or any modern Android device) and a few NFC tags (NTAG213 or NTAG215 recommended for beginners, available for under $1 each). Advanced guides may reference USB NFC readers like the ACR122U or Proxmark3 for development and testing.

Yes. Programming guides include code examples for Android (Kotlin/Java with the Android NFC API), iOS (Swift with Core NFC), and web-based tools (Web NFC API for Chrome on Android). All code samples are tested and include inline comments explaining each step. This project demonstrates a contactless payment workflow using NFC (Near Field Communication) technology. It showcases how NFC-enabled cards or tags can be detected, read securely, and used to simulate or process payment transactions.

NFC is a short-range wireless technology that enables secure communication between two devices placed within a few centimeters. It is widely used in: It is an excellent introduction for developers exploring payment systems or embedded contactless applications. It forms a strong foundation for more advanced payment or authentication applications.

This project is for learning and educational use only . If you want to reuse or integrate this work, please contact me first . 2. System Architecture & Core Working Principles A practical project demonstrating NFC-based payment processing. It includes card detection, secure data reading, transaction simulation, and communication with NFC-compatible devices. Designed to showcase how near-field communication can enable fast, contactless payment experiences.

The nfcpy module implements NFC Forum specifications for wireless short-range data exchange with NFC devices and tags. It is written in Python and aims to provide an easy-to-use yet powerful framework for applications integrating NFC. The source code is licensed under the EUPL and hosted on GitHub . Release versions are published on PyPI for pip install -U nfcpy . Documentation for latest development and active release versions can be found on Read the Docs . The GitHub issues tracker is the place for bug reports or related questions. Other questions may have been or get answered on the project's Launchpad answers page or stackoverflow .

The nfcpy module implements NFC Forum specifications for wireless short-range data exchange with NFC devices and tags. It is written in Python and aims to provide an easy-to-use yet powerful framework for applications integrating NFC. The source code is licensed under the EUPL and hosted at GitHub . The latest release version can be installed from PyPI with pip install -U nfcpy .

Near Field Communication (NFC) operates at 13.56 MHz over distances up to ~4 cm. In payments, it enables tap-to-pay at POS terminals, transit fare gates, and mobile wallets. The technology is governed by ISO/IEC 14443, ISO/IEC 18092, and NFC Forum specifications âII with EMVCo layering the payment protocol on top. A full contactless transaction from tap detection to authorisation response. Each step maps to specific EMV data elements and protocol commands defined in EMVCo Contactless Books A through D.

Terminal RF field activates continuously. When a card or device enters the field, the passive card draws power via inductive coupling and responds with an ATQA (Answer To Request, Type A) or ATQB signal. If multiple cards are present, the anti-collision algorithm resolves them to a single target before proceeding. Terminal reads the Payment System Environment (PPSE) âII tag 9F60 âII to retrieve the list of AIDs (Application Identifiers) supported by the card. Terminal matches this list against its own supported AID list and selects the highest-priority matching AID. This is the Entry Point defined in EMVCo Contactless Book B.

Terminal sends a GPO command containing terminal data: date, time, amount, TVR (Terminal Verification Results) initialised to zeros. The card responds with: Terminal issues READ RECORD commands for each file and record specified in the AFL. Data retrieved includes: PAN (tag 5A ), expiry date ( 5F24 ), cardholder name ( 5F20 ), CVM list ( 8E ), CDOL1/CDOL2 ( 8C / 8D ), and issuer application data ( 9F10 ).

Terminal validates the card's authenticity using one of three methods, depending on what the card's AIP declares: Terminal evaluates the card's CVM list against terminal capabilities and the transaction amount: Terminal performs its own risk checks before generating a cryptogram. Evaluates: velocity counters (how many consecutive offline transactions), floor limit comparison (is the amount above the configured floor limit?), and random online selection triggers (some fraction of sub-floor-limit transactions are forced online for monitoring purposes).

Terminal builds the GENERATE AC command using CDOL1 data (amount, terminal data, TVR) and instructs the card which cryptogram to generate: The ARQC is computed using an EMV session key derived from the card's master key via UDK derivation (ATC-based). The session key is unique to this transaction.

Terminal sends an ISO 8583 0100 authorization request to the acquirer. DE55 carries the full EMV data block including the ARQC, AIP, TVR, ATC, unpredictable number, and transaction data. The issuer validates the ARQC against its own session key derivation, then returns a 0110 response with ARPC in DE55 and an authorisation code. If the card supports it, the issuer can send issuer scripts in the authorisation response (tag 71 / 72 ) âII for example to reset PIN try counters or update application counters. For CDA transactions, the card validates the ARPC using its own key and returns a TC (Transaction Certificate) confirming the authorisation. Transaction is now complete and approved.

Use these tools to work with the cryptographic primitives and data formats used in NFC / EMV contactless transactions. The global contactless payment ecosystem, projected to reach $12.5 trillion by 2030, relies on Near Field Communication (NFC) and dynamic tokenization for security. While these technologies reduce physical card fraud, they introduce a complex digital attack surface involving radio frequency interception, token replay attacks, and backend API vulnerabilities that malicious actors can exploit.

`proxmark3 -p /dev/ttyACM0 –lf search` – Scans for low-frequency (125 kHz) RFID tags. `proxmark3 -p /dev/ttyACM0 –hf 14a search` – Actively scans for high-frequency (13.56 MHz) NFC cards. `proxmark3 -p /dev/ttyACM0 –hf 14a sim -t 7 -s 2006 -k AABBCCDD1122` – Simulates a payment card with specified UID and key. Step-by-step guide: The Proxmark3 is an advanced tool for radio frequency security analysis. First, connect the device and ensure drivers are installed. Use the `lf search` command to identify older RFID systems, then `hf 14a search` to detect modern NFC payment cards. The simulation command allows security teams to clone and test card behavior in a controlled environment, revealing authentication weaknesses in payment terminals.

Step-by-step guide: This Python script using the `pyscard` library communicates directly with EMV payment cards. The SELECT command chooses the Payment System Environment, while GET_PROCESSING_OPTIONS retrieves critical transaction data including the Application Interchange Profile (AIP) and Application File Locator (AFL). Security researchers can analyze this output to understand the card’s capabilities and identify potential data exposure points.

`tcpdump -i any -A port 443 | grep -i “token”` – Captures potential token data in plaintext. `mitmproxy –mode transparent –showhost -p 8080` – Sets up a transparent proxy to intercept HTTPS traffic. `ngrep -q -W byline host tapandpay-api.com | grep -E “(token|nonce)”` – Network grep for payment tokens. Step-by-step guide: While tokenization replaces card numbers with dynamic values, these tokens can still be intercepted during transmission. Using `tcpdump` provides a broad capture of network traffic, while `mitmproxy` allows detailed inspection of encrypted sessions when combined with certificate pinning bypass techniques. `ngrep` offers targeted filtering for specific payment API endpoints.

`reg add “HKLM\SOFTWARE\Policies\Microsoft\POS” /v “DisableUnusedRFID” /t REG_DWORD /d 1 /f` – Windows POS registry entry to disable unused RFID features. `iptables -A OUTPUT -p tcp –dport 443 -d payment-gateway.com -j ACCEPT && iptables -A OUTPUT -p tcp –dport 443 -j DROP` – Restricts terminal to only authorized payment endpoints. `auditctl -a always,exit -F arch=b64 -S open,openat -F dir=/etc/pos -F success=1` – Linux audit rule for POS configuration directory access monitoring.

Step-by-step guide: Payment terminals often run vulnerable configurations. The Windows registry command disables unnecessary RFID interfaces that could be exploited. The iptables rules create a strict egress filter, preventing terminal communication with unauthorized domains. The auditctl command monitors access to critical point-of-sale configuration directories for anomaly detection.

`nuclei -t tokens/token-generic-detection.yaml -u https://api.paymentprovider.com -severity medium,high,critical` – Automated token endpoint scanning. `sqlmap -u “https://api.payments.com/v1/tokens?user_id=123” –batch –level=3` – Tests for SQL injection in token retrieval endpoints. Step-by-step guide: Tokenization backend APIs are high-value targets. The curl command simulates legitimate tokenization requests for testing. Nuclei templates provide automated vulnerability scanning specifically for token-related endpoints, while sqlmap tests for injection flaws that could expose token mapping databases or allow token manipulation.

`hcxdumptool -i wlan0 -o capture.pcapng –enable_status=1` – Captures WiFi handshakes for proximity analysis. `timing_attack_emv –target-terminal POS001 –analyze-latency` – Custom tool to detect relay attacks through transaction timing. `nfc-relay-patch –install –kernel-version $(uname -r)` – Linux kernel patch to enforce NFC transaction timeout limits. Step-by-step guide: NFC relay attacks extend the communication range between card and terminal using proxy devices.

Security teams can use `hcxdumptool` to monitor for suspicious RF activity near payment terminals. Timing analysis tools detect the latency introduced by relay equipment, and kernel-level patches can enforce strict timeout policies that break relay chain continuity. `grep -r “Token\.generate” src/ –include=”.java” | grep -v “SecureRandom”` – Scans codebase for insecure token generation.

Step-by-step guide: This Java example demonstrates proper token generation using cryptographically secure random number generation with context binding. The grep command helps identify insecure token generation patterns in source code during security reviews. Each token should be bound to specific transaction contexts (merchant, amount, geography) to prevent reuse in unauthorized contexts.

The rapid adoption of contactless payments has created a massive, interconnected attack surface where vulnerabilities in one component (terminal, API, token service) can compromise the entire transaction chain. While individual taps generate unique tokens, the backend systems that manage token lifecycle—generation, mapping, validation, and expiration—represent concentrated risk. Our analysis indicates that most implementations prioritize transaction speed over comprehensive security, leaving authentication flaws, inadequate monitoring, and weak encryption in token exchange protocols. As contactless expands beyond cards to wearables and IoT devices, the attack surface will diversify faster than security teams can effectively harden their systems.

Within 2-3 years, we anticipate a cascade failure in contactless payments stemming from token vault breaches combined with relay attacks, potentially compromising millions of transactions simultaneously. This will trigger regulatory intervention mandating quantum-resistant cryptography, hardware-secured token binding, and real-time transaction anomaly detection, fundamentally reshaping payment security requirements and increasing implementation costs by 30-40% for financial institutions.

3. Software Implementation, Frameworks & Protocols Reported By: Nikhil Kassetty – Hackers Feeds Extra Hub: Undercode MoN Basic Verification: Pass I We do not sell your personal data. If you wish to exercise your rights under applicable privacy laws, please visit our Do Not Sell My Personal Information page.

This project is strictly for educational purposes and adheres to ethical hacking principles. It is not intended for unauthorized or malicious activities. The author is a student of ethical hacking and cybersecurity, committed to enhancing security awareness and developing secure systems. An ethical hacking project focused on exploring vulnerabilities in NFC-enabled ATM cards. It analyzes security flaws and attack vectors, demonstrating potential exploits in NFC-based financial systems for educational purposes. This project is part of my cybersecurity studies to enhance understanding of NFC security risks.

This project is an educational initiative designed for ethical hacking and cybersecurity purposes. It demonstrates how NFC (Near Field Communication) technology can be leveraged in an ATM-like system to detect, validate, and log NFC card data securely. The purpose of this project is entirely educational and ethical, aimed at helping students and professionals understand cybersecurity vulnerabilities and solutions.

Contributions are welcome to improve the project! Please ensure all updates maintain the ethical intent of the project. "An ethical hacking project focused on exploring vulnerabilities in NFC-enabled ATM cards. It analyzes security flaws and attack vectors, demonstrating potential exploits in NFC-based financial systems for educational purposes. This project is part of my cybersecurity studies to enhance understanding of NFC security risks." Recently, I became interested in the security of various NFC/RFID contactless access control systems. You have probably seen some in action, for example smart door locks which you can unlock using a small key fob. Even credit cards and recent smartphones use the same technology to pay by touching the register terminal. I am by no means an expert in these technologies, but I wanted to document what I have learned.

Maybe you have seen some form of two-factor authentication hardware, like a YubiKey . I like these small USB/NFC keys, and use them to secure my passwords and online accounts. During my work on KeePassXC (stay tuned for a post about this in the future), I learned quite a bit about the inner workings of the Yubikey and how its two-factor challenge-response functionality works. But to understand why the system is as it is, we first have to consider what constraints and security considerations apply.

Let’s suppose we want to build a smart door lock. Some kind of electronic device that unlocks a door, if and only if the correct keycard is presented to it. This serves as a easy to understand system, but all these methods can be use for more abstract things like computer authorization systems as well. Alright, we purchase a microcontroller and a NFC reader module along with some compatible NFC cards. For the purpose of this post, I went with an Arduino Uno and a PN532 breakout module . The module offers a fully programmable NFC interface and is connected to the Arduino via the SPI port. No actual lock motor or anything is connected, the serial interface will have to be enough.

For this example, we will look at keys which are single-factor (no additional PIN entry or anything), due to convenience. Most bullet points are kind of obvious, assertions you would expect from a quality mechanical door lock as well. The limitation to open-source software is a conscious decision. There are commercial solutions that advertise a high degree of encryption and security, but I chose to implement my own. I believe in Kerckhoffs’s principle (a cryptographic system should be secure if everything about it, except the key, is known), and I also want to learn about the technology and underlying mechanisms instead of deploying a readymade solution.

Eavesdropping on the wireless communication is a legitimate concern when using NFC, although the distances are pretty short secret data can still be leaked, when using the right equipment. A cheap solution is to use a software defined radio (eg. an Airspy Mini ), and a suitable decoder (eg. NFC Laboratory or GNURadio ). The last point is a bit more subjective – at some point, you can just break the door using a tank. There has to be a conceptual line which we consider the limit of physical attack on the lock. For this setup, let’s assume the electronics of the lock are on the outside or at least accessible without issue. This means that a potential attacker is able to extract and modify code on the microcontroller, but not without ripping the lock from the door and bringing it into a lab – no shorting the door in this scenario, that is beyond scope. Keep in mind that this is a virtual door which serves as an analogue to some abstract IT system.

The keycards we chose are cheap popular RFID tags. More precisely, one of the most popular and used tag type on the planet, ISO/IEC 14443 Type A (Mifare) NFC tags. These tags carry essentially a static, non-changeable, globally unique identification number. This ID is usually made up from 4 or 7 bytes. In addition, these tags have space for some amount of freely writeable data (usually 1 kilobyte). This second data block my be used to store anything, eg. a NDEF container which contains a URL to your blog. Once a user scans the tag using a smartphone, the website will be opened.

It is important to note that both the ID and the user data are free to be read by anyone that has the keycard and a compatible reader, i.e. they offer no means of restricting read access. Obviously, we do not want our keys to be cloneable, so we do not use the user data section to hold the key information, which could be easily copied to a second card. Instead, our smart door lock just has to read the unique ID of a keycard, and compare it to the one it was configured with beforehand. If the IDs match, the user presented the correct keycard and the door may open. Pretty easy to implement.

Now, you might think, wow, non-changeable and even globally unique? That are quite strong assertions! And you are correct, neither of them hold true in the real world. First of all, there are cards that exhibit a rather interesting behavior concerning their IDs – due to a “undocumented bug” the ID can be changed as often as you want. These cards are commonly referred to as “first generation magic” and are available online (eg. at KSEC or AliExpress) for a few bucks. Later on, the “second generation magic” cards hit the market, where a changeable ID data block is openly advertised and supported. The first generation cards require special tools like a Proxmark in order to exploit the “bug”, but the second generation magic cards can be configured using even common smartphone apps. In conclusion, the IDs of the tags can be changed and copied to other cards.

In addition, once you know the ID, you can emulate a virtual keycard. A rooted Android phone or a Proxmark can act in a way such that the lock thinks it is presented a keycard with the emulated ID. For more info, see this presentation . As for the global uniqueness – think about how many potential IDs even exist. In the case of the still commonly used four byte variant, that’s 2^32 potential IDs, or just a bit fewer than 4.3 billion. Sounds a lot, but it is really not. Consider how many humans and systems use keycards, and factor in that enough card manufactures are complaining that they are running out of 4-byte IDs – and they are not necessarily coordinating with each other. The next best thing, GUIDs (which are not even guaranteed to have no collisions) use 16 bytes.

Protection: Failed, pretty hard. These types of keycards can be cloned, and even emulated without problems. Feasibility : High. Cards are easily cloned, even without the person carrying them noticing. Protection: Moderate. As the key UID is stored in clear text inside the nonvolatile memory of the chip, it can be usually extracted once an attacker gets their fingers on the chip. There are ways to make it harder to extract data from a chip, for example disabling the programming and data interface, deleting all the memory once tampering or reprogramming is detected, or by providing special inaccessible memory (“secure enclaves”).

The chip we chose (Arduino Uno / Atmega328p) offers a moderate amount of protection: It can be configured in read-back protection mode. This mode requires a total erase of the chips content, which clears the protection but also deletes any stored secret keys. These methods are only an arms race – at some point, you can cut open the chip and inspect its memory contents using a scanning electron microscope. Sure, that is expensive and complex, but not entirely impossible.

On other platforms, like PCs or smartphones, you don’t even have to got that far, since malware or even the operating system could just read the memory sections used by our program and steal the UID. Protection: Failed. The ID is transmitted in clear text, and anyone listening to the NFC wireless communication can receive it. However, since the ID can just be read by any reader anyway, there is little need to actually eavesdrop instead of just reading the ID.

Feasibility : Moderate. Depends on the arrangement of the reader and the range of the antenna. In our next iteration, we want to protect the system against cloning attacks, as they are the most commonly used easy attacks.All we need to do is not expose the ID / the secret key in any way to the outside world. But how do we know which keycard is the correct one if we cant compare the key? Enter: challenge-response based authentication.

Essentially, we need a way to communicate data that can be used to verify which card is the correct one, without an attacker being able to derive the secret key (which would be needed for cloning the card) from the data. The HMAC-SHA1 algorithm (a hash-based message authentication code using the SHA1 hashing function) can be used to perform this kind of exchange. Now you might think, what, SHA1? Isn’t SHA1 insecure, in that it is possible to find collisions? For this I redirect you to this StackOverflow answer , which explains the topic way better that I ever could. Even though HMAC-SHA1 is still secure, you could just as well implement HMAC-SHA256 if you would want to.

Now, this kind of computation requires a more complex card than the simple data carrying ones used above. I prefer JavaCards , for example the Fidesmo card or any generic NXP card . These cards are able to be programmed, implementing whatever logic you want. They usually privide a contact interface (gold pads), and sometimes a NFC interface (internal antenna in contactless cards).

4. Practical Deployment, Analysis & Case Studies The Yubikey implements HMAC-SHA1 as well, which makes it ideal for this kind of application, even though its firmware is not public. Both peers, the lock and the keycard, store the same secret key in their respective memory. When the keycard wants to authenticate with the lock, the lock sends a randomly generated challenge to the keycard, which then uses its stores secret key to derive a response from this challenge. This response can only be generated if the secret key is used, and the secret key cannot be derived from neither the challenge nor the response.

The lock performs the same operation using its copy of the secret key, and compares the received response with the one is computed. If the responses match, the secret keys must too and thus the keycard is validated. Each time a new challenge is generated and used, which means that even if an attacker would listen to the communication, no useful information could be extracted. However, this requires a god random number generator. If the attacker knows which challenges are generated after e.g. a power outage or reboot, they could record responses and perform a replay attack without ever knowing the actual key.

I adapted code for JavaCards to implement this kind of functionality, you can find it at GitHub . It implements the Yubikey-style HMAC-SHA1 protocol via NFC. Protection: Good. Without knowing the secret key, the keycard cannot be cloned. This assumes that the keycard itself cannot be attacked in a similar manner as the reader chip above. Although the protocol has no option to read the secret key, a determined attacker could still somehow try to extract the key. However, the manufactures of secure tokes like YubiKeys or the NXP cards claim a very high degree of data security. Excluding maybe the electron microscope or some obscure power analysis attacks, there should be pretty much no way of extracting the key directly from the hardware.

Protection: Moderate. For the same reasons, see above. The key is still stored in clear text and can be extracted by malware or experts. Protection : Perfect. The HMAC-SHA1 algorithm prevents deriving the key from public information (as of now). A good random number generator is required to prevent replay attacks.

The reader still has to store the secret key somewhere in its memory, making it vulnerable to data extraction (in theory). This is especially a concern when the system is implemented on a PC, where other programs might steal data. However, there is a way to authenticate the keycard using HMAC-SHA1 without ever knowing the secret key. When a new key is enrolled, a random challenge is generated, and the response of the key is stored in runtime writable memory, together with this initial challenge. At the next authentication, the same challenge is sent again and the response is compared. Using the same challenge over and over again would make the system very vulnerable to replay attacks, where an attacker would just capture and send the same response again.

To prevent these replay attacks a new challenge is generated after each successful authentication. This new challenge is then sent to the keycard and the response is stored for the next time a keycard wants to authenticate. You might have noticed that each challenge is used twice then, once when it is generated to receive the correct response, and again when the next authentication takes place. So while it reduces the frame in which a replay attack can be executed, it does not eliminate all possibilities.

Protection : Moderate. Although the key cannot be derived, replay attacks are possible because each challenge is used twice. Equipment: High. AirSpy Mini SDR, Antenna, PC, SmartCard development platform or SmartCard emulator, modified firmware Feasibility : Moderate. Depends on the arrangement of the reader and the range of the antenna. In addition, modified firmware has to be provided, which sends a pre-defined response. However, remember, the source code is open-source and not that hard to modify. A combination of vsmartcard and jcardsim can be used to emulate a SmartCard, or a real SmartCard can be programmed.

As you can see, the challenge and response appear in clear text in the captured RF data. Unfortunately, in order to use each challenge only once, we have to store the secret key inside the lock. Otherwise, there would be no way of knowing if a given response is the correct one for a generated challenge. So, are we back at method 2, where we have to store the secret key in clear text? It is possible to encrypt the stored secret key in a way such that is continues being usable without requiring a second encryption key. The PAM module by Eugene Crosser implements an algorithm I chose to adapt.

The secret key is encrypted using AES-128-CBC and stored next to a randomly generated challenge. However, the encryption key used is the response to this challenge using the secret key. Setting this kind of conundrum up is easier than it sounds, it requires the user to input the secret key, which is then encrypted using the computed response to a random challenge.

At the next keycard authentication, the stored challenge is used to receive a response from the keycard. This response is then used to decrypt the stored secret key. Whatever the result of this decryption is, is is used to compute a response to the used challenge. If the responses match, the key was decrypted correctly, which means that the response was correct, which means that the keycard carries the correct secret key.

The new response is then used to re-encrypt the secret key, and the new challenge is stored alongside it. Protection: Good. The key does exist on the lock, but only encrypted. The unencrypted key does exist in the RAM of the lock, but only for a very short time. On PCs and smartphones, RAM protection mechanisms can be used. It is possible to generate the secret key when having access to the stored encrypted key, the stored challenge, and the keycard to compute the response. So an attacker would have to physically steal instead of copy the keycard as well – in addition to somehow extracting the memory of the lock.

If the keycard is e.g. implanted into a human , or otherwise very hard to steal without the use of considerable effort and force, the secret key is very securely stored. We arrive at the conclusion that HMAC-SHA1 challenge response using a AES-128 encrypted secret key with rolling codes is the way to go.

I implemented an Arduino project and an agnostic library, which is able to interface with Yubikeys and my vk-ykhmac applet. The library also handles the authentication flow discussed in method 4. Check out the code if you are interested. The library is written independent of frameworks or used hardware, and available on PlatformIO here . All of the challenge-response methods here require some amount of persistent storage. While you could store everything in RAM, then the keycard would have to be re-enrolled every time the lock looses power. On the Arduino Uno, the persistent EEPROM is used for demonstration purposes.

Using a second factor, such as a PIN and a keypad would be desired. This factor can be used to further protect and encrypt the key. I am not a crypto expert, so do your own math and development if you plan to use any of this in a production environment. I made this purely as a learning exercise.

This is exactly what I’m looking for, but you might be a touch higher in technology level – no actually way higher – than me. Are you able to detail implementation a bit further. I’ve got a gate that secures my property (before any doors with key locks). At the moment, I use a gate phone with static pin(s) to enter, that are audibly toned through the speaker – yeah I need your help I This project implements a secure cryptocurrency payment system integrating blockchain technology , NFC (Near Field Communication) , and hardware modules (Arduino/ESP32). It provides a safer, user-friendly way to perform crypto transactions using multi-factor verification like OTP and NFC scans.

/Arduino # NFC hardware code with Arduino + RC522 module /backend # Backend APIs for OTP and NFC verification /frontend # React frontend app for wallet & transaction UI /contract # Solidity smart contracts and deployment scripts Create .env files inside /backend, /frontend, and /contract folders with appropriate variables, e.g.: 5. Performance Evaluation & Extended Technical Observations INFURA_URL= https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID PRIVATE_KEY=YOUR_WALLET_PRIVATE_KEY REACT_APP_CONTRACT_ADDRESS=YOUR_DEPLOYED_CONTRACT_ADDRESS JWT_SECRET=YOUR_JWT_SECRET -Arduino MFRC522 NFC/RFID Tutorial: https://randomnerdtutorials.com/how-to-use-rfid-rc522-with-arduino/ -Connecting Arduino with NFC RC522: https://lastminuteengineers.com/rc522-arduino-nfc-rfid-tutorial/ -Handles wallet balances, transfers, and records transaction logs on the Ethereum blockchain.

-Deploy using Hardhat, configured to work with the Holesky test network (or Ethereum mainnet/testnets). Backend -Node.js Express server handles OTP generation/validation and receives NFC scan data. -On detecting an NFC tag, the Arduino sends tag data to the backend server via serial or network connection. As digital transactions continue to evolve, NFC payment solutions are becoming a standard for fast and secure transactions.

Businesses adopting smart terminals must ensure seamless payment system integration to meet security, performance, and compatibility requirements. At rCloud, we specialize in secure payment integration for smart terminals , offering tailored solutions that comply with global payment security standards. This guide will walk you through hardware and software requirements, PCI DSS compliance, and API and SDK considerations to build a reliable payment system.

NFC (Near Field Communication) payment solutions allow users to complete transactions by tapping their smartphones, smart cards, or wearables on an NFC-enabled terminal. This contactless technology enhances transaction speed and security, making it a preferred choice for modern businesses. To successfully implement a smart terminal NFC system, businesses must focus on hardware compatibility, software security, and regulatory compliance. The integration process requires careful planning to ensure a smooth user experience and system reliability.

Before developing an NFC system, businesses must ensure that their smart terminals support NFC-enabled payment processing. Key hardware components include: NFC Reader Module – Enables contactless communication with NFC-enabled cards and devices. Secure Element (SE) or Trusted Execution Environment (TEE) – Protects sensitive payment data from security threats.

High-Speed Processor – Ensures fast NFC system processing for a seamless customer experience. To enable PCI DSS compliant payment, the software must meet strict security and performance standards. Key software components include: NFC Payment SDKs – Pre-built software development kits for integrating NFC transaction processing.

Tokenization Engine – Replaces card details with secure digital tokens for fraud prevention. EMVCo-Certified Payment Application – Ensures compatibility with global payment networks. Remote Update Mechanism – Allows software patches and security updates without disrupting transactions. By combining fast NFC payment processing hardware with secure software, businesses can deliver a seamless NFC system that meets customer expectations.

The Payment Card Industry Data Security Standard (PCI DSS) sets security requirements for handling payment card data. To develop a secure NFC payment integration, businesses must follow these guidelines: Implement Multi-Layer Authentication – Strengthen security with biometric or PIN verification. Secure Customer Data Storage – Prevents unauthorized access to stored payment credentials.

With the right approach, businesses can build a reliable NFC payment development framework that protects customer data and meets regulatory requirements. To integrate payment solutions, businesses must select an API that supports secure and scalable transactions. The ideal API should offer: Many payment processors provide pre-built SDKs for NFC payment solutions, streamlining development. An ideal payment SDK should include: For businesses using POS terminals, ensuring smart terminal NFC payment compatibility is essential. Considerations include: By choosing the right API, SDK, and POS machine, businesses can create a fast and seamless NFC payment system that works across multiple platforms.

PCI DSS-Compliant Payment Systems – Ensuring compliance with global security standards. With our expertise in secure NFC payment integration, businesses can confidently adopt fast, reliable, and compliant NFC payment solutions. Integrating NFC payment solutions on smart terminals requires the right hardware, secure software, and compliance with PCI DSS standards. By selecting compatible APIs, SDKs, and POS machines, businesses can build a reliable NFC payment system that enhances customer experience and transaction security.

At rCloud, we provide custom NFC payment solutions designed for modern businesses. Whether you need secure payment integration or seamless POS compatibility, our experts are ready to help. Contact us today to explore the best NFC payment solutions for your business! Harvested Online Documentation & Reference Sources • https://en.wikipedia.org/wiki/Flipper%20Zero • https://en.wikipedia.org/wiki/List%20of%20computing%20and%20IT%20abbreviations • https://en.wikipedia.org/wiki/Credit%20card%20fraud • https://nfcfyi.com/guide/python-nfc/ • https://github.com/Wissemtriki/NFC-Card-Payment • https://github.com/nfcpy/nfcpy • https://nfcpy.readthedocs.io/en/latest/ • https://payprobe.io/nfc/payment-flow • https://undercodetesting.com/the-hidden-attack-surface-of-contactless-payments-a-red-teams-guide-to-nfc-tokenization-vuln erabilities/ • https://github.com/AbdulCyber-sec/NFC_Payment_Security_Analysis