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Bhupendra Acharya

Publications and source records attributed to Bhupendra Acharya.

10 recordsLinked to original sources

The Tragedy of Convenience: Cascading User-Data Leakage from SMS-Delivered URLs

Digital Services are increasingly sending private URLs over Short Message Service (SMS) to allow users to resume sessions with a single click. While convenient, this design shifts trust from explicit authentication to a potentially vulnerable communication channel. Basically, the vulnerability lies in the assumption that the private link can only be accessed by the intended user. In this paper, we demonstrate that this assumption can be easily violated. In particular, we show how seemingly isolated link exposure can cascade into a wider data leak. Using public SMS gateways as an ethical lens, we analyze more than 322K unique private URLs extracted from over 33 million messages across 30K+ phone numbers. Across 701 URLs, we find that at least 177 web services effectively treat private URLs as bearer credentials, enabling unauthorized access to sensitive user information (e.g., financial details, national IDs) once the link is exposed. Alarmingly, we show that 125 services are potentially enumerable, i.e., a single URL can lead to a cascading effect, resulting in the data leakage of their entire user base. Moreover, we observe that 5 services that implement authentication partially reveal account information before authentication is completed and rely on lightweight parameters (e.g., date of birth, ZIP code). Even worse, in 4 out of these 5 services, the authentication is vulnerable to brute-force attacks. Further, we uncover that 20 services grant privileged access: 14 allow modification of Personally Identifiable Information (PII), 5 grant account access, and 1 allows both. We also find 84 services that expose additional PII beyond the landing page. Our disclosures led to acknowledgments from 18 services, 7 of which have already been fixed, positively impacting at least 120 million users.

cs.CR

MTD-Playground: An Attacker-Aware Evaluation Framework for Network Moving Target Defense

Moving Target Defense (MTD) has emerged as a proactive network cyber defense paradigm that increases attacker uncertainty through dynamic network reconfiguration techniques such as Software-Defined Networking (SDN)-enabled path randomization. However, existing evaluations remain fragmented due to inconsistent attacker assumptions, attack scenarios, and evaluation metrics, limiting reproducibility and deployment-oriented comparison. In this paper, we present MTD-Playground, an attacker-aware evaluation framework for benchmarking SDN-enabled path-randomization (PR) MTD techniques under realistic enterprise-style multi-stage attack scenarios. Beyond isolated security and performance metrics, MTD-Playground introduces a composite evaluation methodology for analyzing deployment effectiveness, mutation-interval trade-offs, and defender-attacker operational balance. Using periodic path randomization as a representative PR-MTD strategy, our evaluation shows that aggressive mutation intervals reduce attack success rates to 4-20% while increasing attack completion time to 160-311s across evaluated attack scenarios. At the same time, PR-MTD improves throughput by up to 30.9% and reduces internal-path latency without service interruption. Composite analysis further shows that shorter mutation intervals consistently achieve the highest deployment effectiveness and positive defender advantage. These results demonstrate that SDN-based PR-MTD can substantially disrupt multi-stage attack progression while remaining practically deployable in enterprise environments.

cs.CR

Achievement Unlocked: Let's Get Hacked! An Empirical Study of Cybercrime in the Video Gaming Ecosystem

The ubiquity of the video game industry and its large user base have transformed video games into complex social and economic ecosystems. Unfortunately, this growing popularity also attracts cybercriminals who deliberately exploit game-specific mechanisms to target players. Despite this growing threat, cybercrime in the gaming ecosystem has received little systematic attention in prior research. In this work, we present an empirical study of cybercrime affecting video game players, combining qualitative and observational analyses to characterize gaming-related attacks, identify common attack vectors and motivations, and examine player responses. Our study is based on an online survey with 57 international participants, semi-structured interviews with two confirmed victims of gaming-related cybercrime, and an analysis of 2,574 publicly available posts reporting cybercrime incidents across multiple online gaming platforms. Our findings indicate that the theft of digital items is a prevalent motivation for attacks. We further observe that gaming-related features and services, such as item trading, team voting, and tournaments, create incentives for players to engage in risky interactions. In addition, our results highlight the targeted exploitation of weaknesses in customer support processes and reveal that certain security mechanisms provide only a false sense of protection.

cs.CR

Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives

As LLMs are increasingly integrated into systems that browse, retrieve, summarize, and act on web content, webpages have become an untrusted input vector for downstream model behavior. This enables site owners, contributors, and adversaries to embed instructions directly in web resources, i.e., indirect prompt injections. While prior work demonstrates such attacks in controlled settings, their prevalence, deployment, and real-world impact remain unclear. We present one of the first large-scale empirical analyses of indirect prompt injections in webpages and HTTP responses. Analyzing 1.2B URLs from 24.8M hosts, we identify 15.3K validated instances across 11.7K pages. These are not isolated cases: a small number of recurring templates account for most cases. We characterize their objectives, delivery mechanisms, visibility, persistence, and impact, revealing a heterogeneous ecosystem spanning disruptive prompts, reputation manipulation, content-protection directives, and AI-bot detection, targeting systems such as crawlers, search pipelines, customer-support agents, and hiring workflows. A key finding is that most instructions target machines rather than humans: about 70% appear in non-rendered HTML (e.g., headers, comments, metadata), and many visible cases are hidden via rendering techniques. To assess practical risk, we run 5,200 controlled experiments across 13 models and four webpage representations. Our results show compliance is limited but non-negligible, reaching up to 8% for smaller models on plain-text inputs, while structured representations reduce compliance by preserving structural cues. Overall, prompt-based interference is already present in the web ecosystem and represents a growing source of tension between LLM-driven automation and the sites it consumes.

cs.CR

From Harm to Healing: Understanding Individual Resilience after Cybercrimes

How do individuals recover from cybercrimes? Victims experience various types of harm after cybercrimes, including monetary loss, data breaches, negative emotions, and even psychological trauma. The aspects that support their recovery process and contribute to individual cyber resilience remain underinvestigated. To address this gap, we interviewed 18 cybercrime victims from Western Europe using a trauma-informed approach. We identified four common stages following victimization: recognition, coping, processing, and recovery. Participants adopted various strategies to mitigate the impact of cybercrime and used different indicators to describe recovery. While they mostly relied on social support and self-regulation for emotional coping, service providers largely determined whether victims were able to recover their money. Internal factors, external support, and context sensitivity collectively contribute to individuals' cyber resilience. We recommend trauma-informed support for cybercrime victims. Extending our conceptualization of individual cyber resilience, we propose collaborative and context-sensitive strategies to address the harmful impacts of cybercrime.

cs.HC

Pirates of Charity: Exploring Donation-based Abuses in Social Media Platforms

With the widespread use of social media, organizations, and individuals use these platforms to raise funds and support causes. Unfortunately, this has led to the rise of scammers in soliciting fraudulent donations. In this study, we conduct a large-scale analysis of donation-based scams on social media platforms. More specifically, we studied profile creation and scam operation fraudulent donation solicitation on X, Instagram, Facebook, YouTube, and Telegram. By collecting data from 151,966 accounts and their 3,053,333 posts related to donations between March 2024 and May 2024, we identified 832 scammers using various techniques to deceive users into making fraudulent donations. Analyzing the fraud communication channels such as phone number, email, and external URL linked, we show that these scamming accounts perform various fraudulent donation schemes, including classic abuse such as fake fundraising website setup, crowdsourcing fundraising, and asking users to communicate via email, phone, and pay via various payment methods. Through collaboration with industry partners PayPal and cryptocurrency abuse database Chainabuse, we further validated the scams and measured the financial losses on these platforms. Our study highlights significant weaknesses in social media platforms' ability to protect users from fraudulent donations. Additionally, we recommended social media platforms, and financial services for taking proactive steps to block these fraudulent activities. Our study provides a foundation for the security community and researchers to automate detecting and mitigating fraudulent donation solicitation on social media platforms.

cs.CR

Exploration of the Dynamics of Buy and Sale of Social Media Accounts

There has been a rise in online platforms facilitating the buying and selling of social media accounts. While the trade of social media profiles is not inherently illegal, social media platforms view such transactions as violations of their policies. They often take action against accounts involved in the misuse of platforms for financial gain. This research conducts a comprehensive analysis of marketplaces that enable the buying and selling of social media accounts. We investigate the economic scale of account trading across five major platforms: X, Instagram, Facebook, TikTok, and YouTube. From February to June 2024, we identified 38,253 accounts advertising account sales across 11 online marketplaces, covering 211 distinct categories. The total value of marketed social media accounts exceeded \$64 million, with a median price of \$157 per account. Additionally, we analyzed the profiles of 11,457 visible advertised accounts, collecting their metadata and over 200,000 profile posts. By examining their engagement patterns and account creation methods, we evaluated the fraudulent activities commonly associated with these sold accounts. Our research reveals these marketplaces foster fraudulent activities such as bot farming, harvesting accounts for future fraud, and fraudulent engagement. Such practices pose significant risks to social media users, who are often targeted by fraudulent accounts resembling legitimate profiles and employing social engineering tactics. We highlight social media platform weaknesses in the ability to detect and mitigate such fraudulent accounts, thereby endangering users. Alongside this, we conducted thorough disclosures with the respective platforms and proposed actionable recommendations, including indicators to identify and track these accounts. These measures aim to enhance proactive detection and safeguard users from potential threats.

cs.CR

ScamChatBot: An End-to-End Analysis of Fake Account Recovery on Social Media via Chatbots

Social media platforms have become the hubs for various user interactions covering a wide range of needs, including technical support and services related to brands, products, or user accounts. Unfortunately, there has been a recent surge in scammers impersonating official services and providing fake technical support to users through these platforms. In this study, we focus on scammers engaging in such fake technical support to target users who are having problems recovering their accounts. More specifically, we focus on users encountering access problems with social media profiles (e.g., on platforms such as Facebook, Instagram, Gmail, and X) and cryptocurrency wallets. The main contribution of our work is the development of an automated system that interacts with scammers via a chatbot that mimics different personas. By initiating decoy interactions (e.g., through deceptive tweets), we have enticed scammers to interact with our system so that we can analyze their modus operandi. Our results show that scammers employ many social media profiles asking users to contact them via a few communication channels. Using a large language model (LLM), our chatbot had conversations with 450 scammers and provided valuable insights into their tactics and, most importantly, their payment profiles. This automated approach highlights how scammers use a variety of strategies, including role-playing, to trick victims into disclosing personal or financial information. With this study, we lay the foundation for using automated chat-based interactions with scammers to detect and study fraudulent activities at scale in an automated way.

cs.CR

An Explorative Study of Pig Butchering Scams

In the recent past, so-called pig-butchering scams are on the rise. This term is based on a translation of the Chinese term "Sha Zhu Pan", where scammers refer to victims as "pig" which are to be "fattened up before slaughter" so that scammer can siphon off as much monetary value as possible. In this type of scam, attackers perform social engineering tricks on victims over an extended period to build credibility or relationships. After a certain period, when victims transfer larger amounts of money to scammers, the fraudsters' platforms or profiles go permanently offline and the victims' money is lost. In this work, we provide the first comprehensive study of pig-butchering scams from multiple vantage points. Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets. Between March 2024 to October 2024, we collected data related to pig butchering scams from (i) four social media platforms comprised of more than 430,000 social media accounts and 770,000 posts; (ii) more than 3,200 public abuse reports narratives, and (iii) about 1,000 news articles. Through automated and qualitative evaluation, we provide an evaluation of victims of pig-butchering scams, finding 146 social media scammed users, 2,570 abuse reports narratives, and 50 case studies of 834 souls from news outlets. In total, we approximated losses of over \$521 million related to such scams. To complement this analysis, we performed a survey on crowdsourcing platforms with 584 users to broaden the insights on comparative analysis of pig-butchering scams with other types of scams. Our research highlights that these attacks are sophisticated and often require multiple entities, including policymakers and law enforcement, to work together alongside user education to create a proactive detection of such scams.

cs.CR

Conning the Crypto Conman: End-to-End Analysis of Cryptocurrency-based Technical Support Scams

The mainstream adoption of cryptocurrencies has led to a surge in wallet-related issues reported by ordinary users on social media platforms. In parallel, there is an increase in an emerging fraud trend called cryptocurrency-based technical support scam, in which fraudsters offer fake wallet recovery services and target users experiencing wallet-related issues. In this paper, we perform a comprehensive study of cryptocurrency-based technical support scams. We present an analysis apparatus called HoneyTweet to analyze this kind of scam. Through HoneyTweet, we lure over 9K scammers by posting 25K fake wallet support tweets (so-called honey tweets). We then deploy automated systems to interact with scammers to analyze their modus operandi. In our experiments, we observe that scammers use Twitter as a starting point for the scam, after which they pivot to other communication channels (eg email, Instagram, or Telegram) to complete the fraud activity. We track scammers across those communication channels and bait them into revealing their payment methods. Based on the modes of payment, we uncover two categories of scammers that either request secret key phrase submissions from their victims or direct payments to their digital wallets. Furthermore, we obtain scam confirmation by deploying honey wallet addresses and validating private key theft. We also collaborate with the prominent payment service provider by sharing scammer data collections. The payment service provider feedback was consistent with our findings, thereby supporting our methodology and results. By consolidating our analysis across various vantage points, we provide an end-to-end scam lifecycle analysis and propose recommendations for scam mitigation.

cs.CR