arXiv subjects

Gaurav Varshney

Publications and source records attributed to Gaurav Varshney.

15 recordsLinked to original sources

arXiv2026

Measurement Study of Post-Quantum Readiness of Internet: 2026

The emergence of quantum computing presents a fundamental challenge to the security of current Internet communication systems. Transport Layer Security (TLS), which forms the backbone of secure web communication, predominantly relies on classical public-key cryptographic algorithms such as RSA and elliptic curve cryptography (ECC), both of which are susceptible to quantum attacks. This paper conducts a large scale empirical evaluation of post-quantum readiness across 32,011 domains, with a primary focus on real-world TLS deployments across diverse sectors by analysing negotiated TLS parameters, including protocol versions, cipher suites, key exchange mechanisms, and certificates. The results indicate that while modern protocols like TLS 1.3 and QUIC are gaining adoption, 15.70% of domains especially in critical sectors such as banking and government still rely on TLS 1.2. Furthermore, 49.3% of domains support hybrid post-quantum key exchange mechanisms (e.g., MLKEM768 with X25519), whereas 50.7% continue to use classical key exchange, reflecting partial transition. Notably, 0% adoption of hybrid post-quantum certificates was observed, leaving the authentication layer vulnerable to quantum-enabled attacks such as certificate forgery. The findings reveal uneven adoption of post-quantum mechanisms across sectors, where technology driven platforms are advancing more rapidly than legacy-dependent infrastructures. Overall, the study highlights that achieving complete quantum resilience requires a coordinated transition not only in key exchange mechanisms but also in certificate infrastructures. Without such comprehensive migration, Internet communication systems remain vulnerable to long-term threats, including Harvest-Now-Decrypt-Later (HNDL) attacks.

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arXiv2026

Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning

Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible, reliance on third-party or internet data is common. Recent studies show CL models are vulnerable to data-poisoning backdoor attacks, but their generalization and robustness are underexplored. We systematically evaluate existing data-poisoning backdoor attacks on CL, revealing limitations: poor dataset adaptability, low success rates, limited portability, and restrictive assumptions (e.g., downstream task knowledge). Interestingly, trigger samples exhibit distinguishable statistical divergence from clean samples, which inspires repurposing it as a watermark for dataset IP protection. Direct repurposing is challenging due to low success rates; we overcome this by statistical verification using a unified density metric. We further propose a multi-level watermarking scheme adapting to feature-level, soft-label, or hard-label outputs in CL. Experiments show some backdoor attacks can be repurposed as effective watermarks with trade-offs among fidelity, verifiability, and robustness. This work demonstrates weak backdoor effects become reliable signals for dataset IP protection in challenging CL settings.

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arXiv2026

GuardPhish: Securing Open-Source LLMs from Phishing Abuse

The rapid adoption of open-source Large Language Models (LLMs) in offline and enterprise environments has introduced a largely unexamined security risk like susceptibility to adversarial phishing prompts under static safety configurations. In this work, we systematically investigate this vulnerability through GuardPhish, a large scale multi-vector phishing prompt dataset comprising 70,015 samples spanning web, email, SMS, and voice attack scenarios derived from real world campaigns. Using a deterministic five model ensemble for labeling, we achieve near perfect inter model agreement (Fleiss kappa = 0.9141), with residual disagreements resolved through expert adjudication. By evaluating eight open-source LLMs under fully offline inference conditions, we uncover a substantial enforcement gap like models that correctly identify phishing intent with detection rates up to 96% nevertheless generate actionable phishing content from identical prompts, with attack success rates reaching 98.5% in voice-based scenarios. These findings demonstrate that intent classification alone does not guarantee generative refusal in the absence of dynamic guardrails. To mitigate this risk, we train transformer based classifiers on GuardPhish, achieving up to 98.27% accuracy as modular pre-generation filters deployable without modifying the underlying generative model. Our results highlight a critical weakness in current open-source LLM deployments and provide a reproducible foundation for strengthening defenses against phishing and social engineering attacks.

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arXiv2025

Jailbreaking Generative AI: Multivector Phishing Threats and Transformer based Defenses

The rise of Generative AI (GenAI) has reshaped the cybersecurity landscape by enabling new attack vectors and lowering the barrier for executing advanced social engineering campaigns. This study conducts an empirical analysis of jailbreaking vulnerabilities in ChatGPT-4o-Mini, showing that novices can bypass safeguards to generate complete multivector phishing attacks across email, web, SMS, and voice channels. Controlled experiments reveal that role-based jailbreaks produce fully operational attack paths capable of credential harvesting. User studies further demonstrate the disruptive potential of GenAI: novice participants exhibited a 240\% increase in perceived phishing competence, a 400\% improvement in task completion rates, and a 57\% reduction in implementation time when assisted by GenAI compared to traditional internet resources. To address these risks, a transformer-based detection framework was developed, achieving an F1-score of 0.9864 (XLNET) for identifying malicious prompts. The work underscores the urgency of strengthening LLM guardrails and provides an annotated dataset to support future defenses.

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arXiv2025

A Login Page Transparency and Visual Similarity Based Zero Day Phishing Defense Protocol

Phishing is a prevalent cyberattack that uses look-alike websites to deceive users into revealing sensitive information. Numerous efforts have been made by the Internet community and security organizations to detect, prevent, or train users to avoid falling victim to phishing attacks. Most of this research over the years has been highly diverse and application-oriented, often serving as standalone solutions for HTTP clients, servers, or third parties. However, limited work has been done to develop a comprehensive or proactive protocol-oriented solution to effectively counter phishing attacks. Inspired by the concept of certificate transparency, which allows certificates issued by Certificate Authorities (CAs) to be publicly verified by clients, thereby enhancing transparency, we propose a concept called Page Transparency (PT) for the web. The proposed PT requires login pages that capture users' sensitive information to be publicly logged via PLS and made available to web clients for verification. The pages are verified to be logged using cryptographic proofs. Since all pages are logged on a PLS and visually compared with existing pages through a comprehensive visual page-matching algorithm, it becomes impossible for an attacker to register a deceptive look-alike page on the PLS and receive the cryptographic proof required for client verification. All implementations occur on the client side, facilitated by the introduction of a new HTTP PT header, eliminating the need for platform-specific changes or the installation of third-party solutions for phishing prevention.

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arXiv2025

A Study of Effectiveness of Brand Domain Identification Features for Phishing Detection in 2025

Phishing websites continue to pose a significant security challenge, making the development of robust detection mechanisms essential. Brand Domain Identification (BDI) serves as a crucial step in many phishing detection approaches. This study systematically evaluates the effectiveness of features employed over the past decade for BDI, focusing on their weighted importance in phishing detection as of 2025. The primary objective is to determine whether the identified brand domain matches the claimed domain, utilizing popular features for phishing detection. To validate feature importance and evaluate performance, we conducted two experiments on a dataset comprising 4,667 legitimate sites and 4,561 phishing sites. In Experiment 1, we used the Weka tool to identify optimized and important feature sets out of 5: CN Information(CN), Logo Domain(LD),Form Action Domain(FAD),Most Common Link in Domain(MCLD) and Cookie Domain through its 4 Attribute Ranking Evaluator. The results revealed that none of the features were redundant, and Random Forest emerged as the best classifier, achieving an impressive accuracy of 99.7\% with an average response time of 0.08 seconds. In Experiment 2, we trained five machine learning models, including Random Forest, Decision Tree, Support Vector Machine, Multilayer Perceptron, and XGBoost to assess the performance of individual BDI features and their combinations. The results demonstrated an accuracy of 99.8\%, achieved with feature combinations of only three features: Most Common Link Domain, Logo Domain, Form Action and Most Common Link Domain,CN Info,Logo Domain using Random Forest as the best classifier. This study underscores the importance of leveraging key domain features for efficient phishing detection and paves the way for the development of real-time, scalable detection systems.

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arXiv2025

A Study on Malicious Browser Extensions in 2025

Browser extensions are additional tools developed by third parties that integrate with web browsers to extend their functionality beyond standard capabilities. However, the browser extension platform is increasingly being exploited by hackers to launch sophisticated cyber threats. These threats encompass a wide range of malicious activities, including but not limited to phishing, spying, Distributed Denial of Service (DDoS) attacks, email spamming, affiliate fraud, malvertising, and payment fraud. This paper examines the evolving threat landscape of malicious browser extensions in 2025, focusing on Mozilla Firefox and Chrome. Our research successfully bypassed security mechanisms of Firefox and Chrome, demonstrating that malicious extensions can still be developed, published, and executed within the Mozilla Add-ons Store and Chrome Web Store. These findings highlight the persisting weaknesses in browser's vetting process and security framework. It provides insights into the risks associated with browser extensions, helping users understand these threats while aiding the industry in developing controls and countermeasures to defend against such attacks. All experiments discussed in this paper were conducted in a controlled laboratory environment by the researchers, adhering to proper ethical guidelines. The sole purpose of these experiments is to raise security awareness among the industry, research community, and the general public.

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arXiv2025

Jailbreaking Generative AI: Empowering Novices to Conduct Phishing Attacks

The rapid advancements in generative AI models, such as ChatGPT, have introduced both significant benefits and new risks within the cybersecurity landscape. This paper investigates the potential misuse of the latest AI model, ChatGPT-4o Mini, in facilitating social engineering attacks, with a particular focus on phishing, one of the most pressing cybersecurity threats today. While existing literature primarily addresses the technical aspects, such as jailbreaking techniques, none have fully explored the free and straightforward execution of a comprehensive phishing campaign by novice users using ChatGPT-4o Mini. In this study, we examine the vulnerabilities of AI-driven chatbot services in 2025, specifically how methods like jailbreaking and reverse psychology can bypass ethical safeguards, allowing ChatGPT to generate phishing content, suggest hacking tools, and assist in carrying out phishing attacks. Our findings underscore the alarming ease with which even inexperienced users can execute sophisticated phishing campaigns, emphasizing the urgent need for stronger cybersecurity measures and heightened user awareness in the age of AI.

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arXiv2024

A Passwordless MFA Utlizing Biometrics, Proximity and Contactless Communication

Despite being more secure and strongly promoted, two-factor (2FA) or multi-factor (MFA) schemes either fail to protect against recent phishing threats such as real-time MITM, controls/relay MITM, malicious browser extension-based phishing attacks, and/or need the users to purchase and carry other hardware for additional account protection. Leveraging the unprecedented popularity of NFC and BLE-enabled smartphones, we explore a new horizon for designing an MFA scheme. This paper introduces an advanced authentication method for user verification that utilizes the user's real-time facial biometric identity, which serves as an inherent factor, together with BLE- NFC-enabled mobile devices, which operate as an ownership factor. We have implemented a prototype authentication system on a BLE-NFC-enabled Android device, and initial threat modeling suggests that it is safe against known phishing attacks. The scheme has been compared with other popular schemes using the Bonneau et al. assessment framework in terms of usability, deployability, and security.

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arXiv2024

Privacy Aware Memory Forensics

In recent years, insider threats and attacks have been increasing in terms of frequency and cost to the corporate business. The utilization of end-to-end encrypted instant messaging applications (WhatsApp, Telegram, VPN) by malicious insiders raised data breach incidents exponentially. The Securities and Exchange Board of India (SEBI) investigated reports on such data leak incidents and reported about twelve companies where earnings data and financial information were leaked using WhatsApp messages. Recent surveys indicate that 60% of data breaches are primarily caused by malicious insider threats. Especially, in the case of the defense environment, information leaks by insiders will jeopardize the countrys national security. Sniffing of network and host-based activities will not work in an insider threat detection environment due to end-to-end encryption. Memory forensics allows access to the messages sent or received over an end-to-end encrypted environment but with a total compromise of the users privacy. In this research, we present a novel solution to detect data leakages by insiders in an organization. Our approach captures the RAM of the insiders device and analyses it for sensitive information leaks from a host system while maintaining the users privacy. Sensitive data leaks are identified with context using a deep learning model. The feasibility and effectiveness of the proposed idea have been demonstrated with the help of a military use case. The proposed architecture can however be used across various use cases with minor modifications.

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arXiv2023

A Lightweight and Secure PUF-Based Authentication and Key-exchange Protocol for IoT Devices

The Internet of Things (IoT) has improved people's lives by seamlessly integrating into many facets of modern life and facilitating information sharing across platforms. Device Authentication and Key exchange are major challenges for the IoT. High computational resource requirements for cryptographic primitives and message transmission during Authentication make the existing methods like PKI and IBE not suitable for these resource constrained devices. PUF appears to offer a practical and economical security mechanism in place of typically sophisticated cryptosystems like PKI and IBE. PUF provides an unclonable and tamper sensitive unique signature based on the PUF chip by using manufacturing process variability. Therefore, in this study, we use lightweight bitwise XOR, hash function, and PUF to Authenticate IoT devices. Despite several studies employing the PUF to authenticate communication between IoT devices, to the authors' knowledge, existing solutions require intermediary gateway and internet capabilities by the IoT device to directly interact with a Server for Authentication and hence, are not scalable when the IoT device works on different technologies like BLE, Zigbee, etc. To address the aforementioned issue, we present a system in which the IoT device does not require a continuous active internet connection to communicate with the server in order to Authenticate itself. The results of a thorough security study are validated against adversarial attacks and PUF modeling attacks. For formal security validation, the AVISPA verification tool is also used. Performance study recommends this protocol's lightweight characteristics. The proposed protocol's acceptability and defenses against adversarial assaults are supported by a prototype developed with ESP32.

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arXiv2022

An Improved Authentication Scheme for BLE Devices with no I/O Capabilities

Bluetooth Low Energy (BLE) devices have become very popular because of their Low energy consumption and hence a prolonged battery life. They are being used in smart wearable devices, smart home automation system, beacons and many more areas. BLE uses pairing mechanisms to achieve a level of peer entity authentication as well as encryption. Although, there are a set of pairing mechanisms available but BLE devices having no keyboard or display mechanism (and hence using the Just Works pairing) are still vulnerable. In this paper, we propose and implement, a light-weight digital certificate based authentication mechanism for the BLE devices making use of Just Works model. The proposed model is an add-on to the already existing pairing mechanism and therefore can be easily incorporated in the existing BLE stack. To counter the existing Man-in-The-Middle attack scenario in Just Works pairing (device spoofing), our proposed model allows the client and peripheral to make use of the popular Public Key Infrastructure (PKI) to establish peer entity authentication and a secure cryptographic tunnel for communication. We have also developed a lightweight BLE profiled digital certificate containing the bare minimum fields required for resource constrained devices, which significantly reduces the memory (about 90\% reduction) and energy consumption. We have experimentally evaluated the energy consumption of the device using the proposed pairing mechanism to demonstrate that the model can be easily deployed with less changes to the power requirements of the chips. The model has been formally verified using automatic verification tool for protocol testing.

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arXiv2020

A Comprehensive Survey of Aadhar and Security Issues

The concept of Aadhaar came with the need for a unique identity for every individual. To implement this, the Indian government created the authority UIDAI to distribute and generate user identities for every individual based on their demographic and biometric data. After the implementation, came the security issues and challenges of Aadhaar and its authentication. So, our study focuses on the journey of Aadhaar from its history to the current condition. The paper also describes the authentication process, and the updates happened over time. We have also provided an analysis of the security attacks witnessed so far as well as the possible countermeasure and its classification. Our main aim is to cover all the security aspects related to Aadhaar to avoid possible security attacks. Also, we have included the current updates and news related to Aadhaar.

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arXiv2019

Understanding the Social Factors Affecting the Cryptocurrency Market

Blockchain and its application on cryptocurrency transactions have gathered a lot of attention and popularity since the birth of the pioneer Bitcoin in 2009. More than 1500 cryptocurrencies are currently circulated in the market. The technology underpinning Bitcoin and other cryptocurrencies is Blockchain and is a rapidly growing decentralized distributed ledger technology which find its major involvement in cryptocurrencies. But cryptocurrencies are of extremely volatile and fragile nature which makes it difficult to be used as a stable currency for transactions and devoid this market of human trust. Cryptocurrency market is controlled by various social and government factors which keeps it fluctuating. This paper identifies and discusses the important factors that govern the cryptocurrency market and analyzes the impact of these factors. A pilot user survey has also been presented at the end of this paper to understand and demonstrate the societal view of the acceptance of cryptocurrencies.

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arXiv2018

A Metapolicy Framework for Enhancing Domain Expressiveness on the Internet

Domain Name System (DNS) domains became Internet-level identifiers for entities (like companies, organizations, or individuals) hosting services and sharing resources over the Internet. Domains can specify a set of security policies (such as, email and trust security policies) that should be followed by clients while accessing the resources or services represented by them. Unfortunately, in the current Internet, the policy specification and enforcement are dispersed, non-comprehensive, insecure, and difficult to manage. In this paper, we present a comprehensive and secure metapolicy framework for enhancing the domain expressiveness on the Internet. The proposed framework allows the domain owners to specify, manage, and publish their domain-level security policies over the existing DNS infrastructure. The framework also utilizes the existing trust infrastructures (i.e., TLS and DNSSEC) for providing security. By reusing the existing infrastructures, our framework requires minimal changes and requirements for adoption. We also discuss the initial results of the measurements performed to evaluate what fraction of the current Internet can get benefits from deploying our framework. Moreover, overheads of deploying the proposed framework have been quantified and discussed.

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