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Muhammad Danish

Publications and source records attributed to Muhammad Danish.

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Private Links, Public Leaks: Consequences of Frictionless User Experience on the Security and Privacy Posture of SMS-Delivered URLs

Digital service providers often prioritize a frictionless user experience by adopting technologies that simplify access to their services. One widely used mechanism is the Short Message Service (SMS) to deliver links (URLs) that enable single-click access to online services with little to no resistance. However, SMS is inherently insecure, and numerous reports have documented message interception and data leaks. Thus, attributing excessive trust in such an insecure channel opens avenues for unintended access and exploitation by adversaries. In this paper, we present a comprehensive investigation of the implications of SMS-delivered URLs from the lens of public SMS gateways. We conduct the study on more than 322K unique SMS-delivered URLs extracted from more than 33 million messages across more than 30K phone numbers, revealing critical security and privacy vulnerabilities. We identify and validate critical Personally Identifiable Information (PII) exposure in 701 endpoints affecting 177 services. Our manual investigation of the root cause of the exposure reveals a weak authentication model which hinges upon tokenized bearer links as sufficient authorization proofs, thereby allowing anyone with the URL to access private user information, including social security number, date of birth, bank account number, and credit score. Additionally, we identify 125 services allowing mass enumeration of valid URLs due to low entropy within tokens, thereby cascading the privacy risks beyond the initially compromised users. Furthermore, we identify mismatches between the GUI and data fetched by the client, extending the scale of privacy leakages. Particularly, we identify 76 services that perform data overfetching. Finally, 18 services have acknowledged and addressed the weaknesses in their services, thereby enhancing the privacy of at least 120M users.

cs.CR

Accessibility Issues in Ad-Driven Web Applications

Website accessibility is essential for inclusiveness and regulatory compliance. Although third-party advertisements (ads) are a vital revenue source for free web services, they introduce significant accessibility challenges. Leasing a website\'s space to ad-serving technologies like DoubleClick results in developers losing control over ad content accessibility. Even on highly accessible websites, third-party ads can undermine adherence to Web Content Accessibility Guidelines (WCAG). We conduct the first large-scale investigation of 430K website elements, including nearly 100K ad elements, to understand the accessibility of ads on websites. We seek to understand the prevalence of inaccessible ads and their overall impact on the accessibility of websites. Our findings show that 67% of websites experience increased accessibility violations due to ads, with common violations including Focus Visible and On Input. Popular ad-serving technologies like Taboola, DoubleClick, and RevContent often serve ads that fail to comply with WCAG standards. Even when ads are WCAG compliant, 27% of them have alternative text in ad images that misrepresents information, potentially deceiving users. Manual inspection of a sample of these misleading ads revealed that user-identifiable data is collected on 94% of websites through interactions, such as hovering or pressing enter. Since users with disabilities often rely on tools like screen readers that require hover events to access website content, they have no choice but to compromise their privacy in order to navigate website ads. Based on our findings, we further dissect the root cause of these violations and provide design guidelines to both website developers and ad-serving technologies to achieve WCAG-compliant ad integration.

cs.SE

Enhancing Cyber Security Through Predictive Analytics: Real-Time Threat Detection and Response

This study evaluates the application of predictive analytics for real-time cyber-attack detection and response, focusing on how statistical and machine learning methods can improve decision-making in Security Operations Centers (SOCs). Using a curated network-traffic dataset of 2,000 records, we analyzed key features such as attack type, packet length, anomaly scores, protocol usage, and geo-location patterns to assess their predictive value. Findings indicate that attack type has a measurable influence on response actions, while basic header metrics alone lack the precision needed for accurate classification. These results highlight the importance of incorporating richer contextual features - such as user behavior, asset criticality, and temporal patterns - into predictive models. By integrating such features into operational pipelines, organizations can improve early threat detection, reduce false positives, and optimize resource allocation. This research contributes actionable insights for advancing proactive, data-driven cyber defense strategies and outlines directions for future implementation in live SOC environments.

cs.CR