Why QR codes are a hacker’s playground and how to stay safe

The study identifies multiple security threats associated with QR codes, many of which exploit human vulnerabilities and the obscured nature of the encoded data. The primary attack types include QRishing (QR code phishing), malware propagation, counterfeiting, and information leakage. Cybercriminals have weaponized QR codes to distribute malicious links, execute cross-site scripting (XSS), initiate SQL injections, and launch command-based attacks.

Quick Response (QR) codes have become an integral part of modern digital interactions, from contactless payments and marketing campaigns to authentication and logistics tracking. However, their widespread adoption has also exposed vulnerabilities that cybercriminals can exploit. QR code-based attacks have surged, exploiting users' inability to recognize malicious content before scanning. As a result, understanding the nature of these threats and effective mitigation strategies has become crucial for cybersecurity researchers and technology users alike.

In their study, "Quick Response Code Security Attacks and Countermeasures: A Systematic Literature Review," researchers David Njuguna and John Ndia from the School of Computing and Information Technology, Murang’a University of Technology, explore the various attack vectors targeting QR codes. Published in the Journal of Cyber Security in 2025, the study systematically reviews 50 relevant studies from 2010 to 2024 to categorize major QR code attacks and the techniques used to detect and prevent them.

Identifying key QR code-based attacks

The study identifies multiple security threats associated with QR codes, many of which exploit human vulnerabilities and the obscured nature of the encoded data. The primary attack types include QRishing (QR code phishing), malware propagation, counterfeiting, and information leakage. Cybercriminals have weaponized QR codes to distribute malicious links, execute cross-site scripting (XSS), initiate SQL injections, and launch command-based attacks.

A common form of QR code manipulation is QR-in-QR attacks, where a malicious QR code is embedded within a legitimate one, leading unsuspecting users to harmful websites or fraudulent payment pages. Additionally, malware propagation via QR codes leverages users’ trust, enticing them to scan codes that download malicious payloads onto their devices. Other sophisticated attacks include barcode tampering, counterfeiting, and attacks on reader applications, which exploit vulnerabilities in QR code scanning software to gain unauthorized access to sensitive data.

Detection and prevention techniques for QR code threats

To counter QR code-related threats, various detection and mitigation techniques have been proposed, ranging from cryptographic security measures to artificial intelligence (AI)-powered recognition systems. The study categorizes these countermeasures into cryptographic solutions, machine learning-based detection, two-factor authentication, and user-awareness approaches.

Cryptographic techniques, such as the use of the Elliptic Curve Digital Signature Algorithm (ECDSA), ensure the authenticity and integrity of QR codes, making it difficult for attackers to modify their contents. Similarly, AES encryption and blockchain-based verification offer enhanced security layers for QR code authentication. Meanwhile, AI-driven security mechanisms, including decision tree classifiers and deep learning models, help identify malicious QR codes based on behavioral analysis and URL filtering.

Additionally, multi-factor authentication (MFA) and one-time password (OTP) systems provide an extra layer of security for QR code transactions, preventing unauthorized access even if a user mistakenly scans a malicious code. Moreover, awareness campaigns and browser-based security enhancements educate users about QR code phishing risks, encouraging caution before scanning codes from unverified sources.

Implications for cybersecurity and future research directions

As QR code technology continues to evolve, so do the methods used by cybercriminals to exploit it. The study emphasizes the need for automated real-time detection mechanisms, particularly AI-driven frameworks that can analyze QR codes dynamically before execution. Implementing blockchain-backed authentication and integrating machine learning with web security protocols will significantly reduce the risks associated with QR-based attacks.

Furthermore, the study highlights the importance of standardized QR code security guidelines, urging developers and organizations to incorporate cryptographic verification into QR code generation processes. Moving forward, continued research into adaptive QR code security models that adjust based on emerging cyber threats will be critical in ensuring safe digital interactions.

By shedding light on the vulnerabilities of QR codes and the corresponding countermeasures, Njuguna and Ndia’s study provides valuable insights for both researchers and industry professionals. As QR codes remain an indispensable tool in the digital landscape, proactive security measures will be vital in mitigating risks and ensuring user trust in this ubiquitous technology.