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Introduction
The cybercrime ecosystem is changing rapidly. Criminal groups that once depended heavily on expensive Malware-as-a-Service (MaaS) platforms are now building their own sophisticated attack tools using open-source software and artificial intelligence. A new generation of banking malware is emerging, and it is more decentralized, adaptable, and dangerous than before.
Recent security investigations uncovered two Android malware families, DevilNFC and NFCMultiPay, both designed to exploit Near Field Communication (NFC) technology to conduct banking fraud against customers across Europe and Latin America. Their emergence signals a major transformation in digital financial crime, where local threat actors no longer need to purchase infrastructure from established criminal organizations. Instead, they are developing advanced capabilities independently.
This shift demonstrates how accessible technologies, leaked codebases, and generative AI are dramatically lowering the barrier to entry for cybercriminal operations.
NFC Fraud Evolution Moves Beyond Traditional Criminal Infrastructure
Only a year ago, Chinese-speaking Malware-as-a-Service ecosystems such as SuperCard X largely controlled NFC relay fraud operations. These services offered affiliates ready-to-use infrastructure, allowing criminals worldwide to conduct attacks without extensive technical knowledge.
That model is now changing.
Security researchers recently identified DevilNFC and NFCMultiPay as examples of independently developed Android malware families specifically targeting banking users in Europe and Latin America. Rather than relying on costly MaaS subscriptions, operators behind these tools appear to have built customized attack frameworks using publicly available resources.
The result is a cybercrime environment that is becoming increasingly distributed and harder to disrupt.
NFCMultiPay Shows Criminal Independence
NFCMultiPay appears to be operated by a Portuguese-speaking cybercriminal group originating from Brazil.
The malware uses a Java-based NFC reader capable of routing communications through cloud infrastructure. Researchers discovered traces of Chinese-language code in earlier builds, indicating developers may have reused leaked components from older criminal projects.
Later versions evolved significantly.
Chinese artifacts disappeared and were replaced by English and Portuguese operational logging systems. This suggests developers gradually replaced inherited components while building a fully independent attack platform.
The evolution reflects a growing trend in cybercrime development. Criminal actors are increasingly taking leaked tools, modifying them, and building custom infrastructures that remove dependence on larger underground criminal providers.
DevilNFC Introduces a More Advanced Threat
DevilNFC represents an even more sophisticated advancement.
Researchers believe Spanish-speaking developers created DevilNFC using the open-source NFCGate framework as a foundation. However, the malware itself contains substantial original functionality beyond the underlying framework.
The threat actors appear to have engineered a highly specialized banking fraud platform optimized for stealth and efficiency.
Evidence also suggests artificial intelligence assisted parts of development.
Cybercriminals increasingly rely on uncensored local large language models to generate software components, improve logging systems, strengthen error handling mechanisms, and even produce phishing content that appears more convincing to victims.
This combination of open-source software and AI assistance creates a dangerous acceleration effect.
Threat actors no longer need elite programming teams to create advanced malware.
DevilNFC Uses Dual-Device Architecture
One of DevilNFC’s most concerning innovations is its dual-role operational design.
On an unrooted victim Android device, the malware behaves passively, reducing visibility to standard security detection mechanisms.
The attacker device operates differently.
When installed on a rooted Android phone, DevilNFC leverages the Xposed Framework to inject hooks directly into Android’s NFC infrastructure. The malware then transforms attacker hardware into a functioning card emulator using modified NFCGate-derived technology.
This architecture enables real-time NFC relay attacks while maintaining operational separation between victim and attacker environments.
The design demonstrates growing sophistication among modern cybercriminal groups.
The Attack Process Exploits Human Trust
Researchers found DevilNFC campaigns commonly begin with phishing.
Victims receive SMS or WhatsApp messages claiming urgent banking security updates are required. The message persuades users to download what appears to be a legitimate banking protection application.
After installation, the malware immediately begins controlling the victim environment.
Android Kiosk Mode functionality locks the device into a fraudulent banking interface, preventing users from exiting normally.
Meanwhile, the malware intercepts incoming one-time passwords and silently forwards authentication information to attacker-controlled Telegram infrastructure.
The attack escalates further.
Victims are instructed to physically tap their payment card against their phone under the pretense of verification.
A counterfeit banking screen then requests the PIN.
To maximize success rates, attackers intentionally display fake error messages instructing victims to hold their card against the phone longer, extending the relay communication window and improving transaction completion probability.
The social engineering component is just as important as the malware itself.
Cybercriminals are increasingly blending technical exploitation with psychological manipulation.
Infrastructure Indicators Reveal Operational Components
Researchers identified multiple infrastructure elements associated with these campaigns.
DevilNFC infrastructure reportedly included domains linked to relay and command systems.
NFCMultiPay operators also relied on dedicated command-and-control servers supporting malware communication functions.
Security professionals emphasize that infrastructure indicators should only be investigated within controlled threat intelligence environments to avoid accidental interaction with malicious systems.
Threat intelligence platforms, malware sandboxes, and security information systems remain essential tools for safely analyzing operational indicators.
What Undercode Say:
The emergence of DevilNFC and NFCMultiPay highlights a broader transformation happening across cybercrime ecosystems. Malware development is becoming democratized in much the same way software development evolved over the past decade.
Open-source frameworks have dramatically lowered technical barriers.
Artificial intelligence now accelerates development speed.
Leaked criminal code provides building blocks.
Combined together, these elements create an environment where smaller cybercriminal groups can rapidly achieve capabilities once limited to highly organized operations.
The NFC fraud landscape is particularly concerning because it targets a technology many consumers inherently trust.
Contactless payments have become routine. Users rarely question NFC interactions because the technology is associated with convenience and safety.
Attackers understand this behavioral pattern.
Instead of attacking cryptographic protections directly, criminals increasingly exploit user psychology.
Fake banking updates.
Forced urgency.
Artificial verification steps.
PIN harvesting.
All are examples of manipulating trust rather than breaking encryption.
Another major implication involves AI-assisted malware development.
The security industry has warned for years that generative AI could become a force multiplier for attackers.
Evidence emerging from DevilNFC development patterns suggests that prediction is becoming reality.
AI-generated code structures reduce development time.
Error handling becomes stronger.
Phishing templates improve.
Operational scaling becomes easier.
Defenders face a growing challenge because sophisticated malware creation no longer requires highly specialized expertise.
Cybersecurity strategies must evolve accordingly.
Traditional signature detection increasingly struggles against rapidly modified malware variants.
Behavioral monitoring becomes more important.
Mobile application validation grows more critical.
Banking institutions may need stronger transaction anomaly detection systems capable of identifying relay attack patterns before fraudulent payments complete.
Consumers also play a central defensive role.
Users should remain cautious of urgent banking notifications received through SMS or messaging applications.
Financial institutions rarely require customers to install software through links delivered by text messages.
Verification through official banking applications remains one of the strongest defenses against social engineering campaigns.
The bigger picture is clear.
Cybercrime infrastructure is decentralizing.
The next generation of digital fraud may not emerge from massive underground criminal syndicates.
It may come from smaller groups empowered by publicly available technology and artificial intelligence.
Fact Checker Results
✅ DevilNFC and NFCMultiPay are described as newer NFC relay malware families targeting banking users.
✅ The article consistently supports the claim that open-source tools and AI are reducing dependence on traditional Malware-as-a-Service ecosystems.
✅ The described attack chain aligns with modern banking malware techniques involving phishing, OTP interception, and social engineering.
Prediction
🔮 NFC banking attacks will likely expand beyond current target regions as criminal developers continue refining open-source relay frameworks.
🔮 AI-assisted malware development will become increasingly common, producing faster malware evolution cycles and harder-to-detect attack variants.
🔮 Mobile banking providers will likely strengthen fraud detection systems focused specifically on NFC transaction anomalies and device behavior analytics.
🕵️📝Let’s dive deep and fact‑check.
References:
Reported By: cyberpress.org
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