arXiv subjects

Muhammad Wahid Akram

Publications and source records attributed to Muhammad Wahid Akram.

3 recordsLinked to original sources

arXiv2026

ALFA: A Safe-by-Design Approach to Mitigate Quishing Attacks Launched via Fancy QR Codes

Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their confidential data. Recently, we see the colorful and fancy representations of QR codes, the 2D matrix of QR codes which does not reflect a typical mixture of black-white modules anymore. Instead, they become more tempting as an attack vector for adversaries which can evade the state-of-the-art deep learning visual-based and other prevailing countermeasures. We introduce "ALFA", a safe-by-design approach, to mitigate Quishing and prevent everyone from accessing the post-scan harmful payload of fancy QR codes. Our method first converts a fancy QR code into the replica of binary grid and then identify the erroneous representation of modules in that grid. Following that, we present "FAST" method which can conveniently recover erroneous modules from that binary grid. Afterwards, using this binary grid, our solution extracts the structural features of fancy QR code and predicts its legitimacy using a pre-trained model. The effectiveness of our proposal is demonstrated by the experimental evaluation on a synthetic dataset (containing diverse variations of fancy QR codes) and achieve a FNR of 0.06% only. We also develop the mobile app to test the practical feasibility of our solution and provide a performance comparison of the app with the real-world QR readers. This comparison further highlights the classification reliability and detection accuracy of this solution in real-world environments.

cs.CR↗

arXiv2025

QR\"iS: A Preemptive Novel Method for Quishing Detection Through Structural Features of QR

Globally, individuals and organizations employ Quick Response (QR) codes for swift and convenient communication. Leveraging this, cybercriminals embed falsify and misleading information in QR codes to launch various phishing attacks which termed as Quishing. Many former studies have introduced defensive approaches to preclude Quishing such as by classifying the embedded content of QR codes and then label the QR codes accordingly, whereas other studies classify them using visual features (i.e., deep features, histogram density analysis features). However, these approaches mainly rely on black-box techniques which do not clearly provide interpretability and transparency to fully comprehend and reproduce the intrinsic decision process; therefore, having certain obvious limitations includes the approaches' trust, accountability, issues in bias detection, and many more. We proposed QR\"iS, the pioneer method to classify QR codes through the comprehensive structural analysis of a QR code which helps to identify phishing QR codes beforehand. Our classification method is clearly transparent which makes it reproducible, scalable, and easy to comprehend. First, we generated QR codes dataset (i.e. 400,000 samples) using recently published URLs datasets [1], [2]. Then, unlike black-box models, we developed a simple algorithm to extract 24 structural features from layout patterns present in QR codes. Later, we train the machine learning models on the harvested features and obtained accuracy of up to 83.18%. To further evaluate the effectiveness of our approach, we perform the comparative analysis of proposed method with relevant contemporary studies. Lastly, for real-world deployment and validation, we developed a mobile app which assures the feasibility of the proposed solution in real-world scenarios which eventually strengthen the applicability of the study.

cs.CR↗

arXiv2025

Exemplifying Emerging Phishing: QR-based Browser-in-The-Browser (BiTB) Attack

Lately, cybercriminals constantly formulate productive approaches to exploit individuals. This article exemplifies an innovative attack, namely QR-based Browser-in-The-Browser (BiTB), using proficiencies of Large Language Model (LLM) i.e. Google Gemini. The presented attack is a fusion of two emerging attacks: BiTB and Quishing (QR code phishing). Our study underscores attack's simplistic implementation utilizing malicious prompts provided to Gemini-LLM. Moreover, we presented a case study to highlight a lucrative attack method, we also performed an experiment to comprehend the attack execution on victims' device. The findings of this work obligate the researchers' contributions in confronting this type of phishing attempts through LLMs.

cs.CR↗