arXiv subjects

Sazzadur Rahaman

Publications and source records attributed to Sazzadur Rahaman.

At least 19 recordsLinked to original sources

arXiv2026

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↗

arXiv2026

Trust Without Boundaries: An Architectural Analysis of Satellite Flight Software

As spacecraft become more software-driven and interconnected, onboard flight software is an increasingly important security boundary. Popular flight software architectures often treat onboard components as trusted peers, simplifying integration while limiting internal isolation and access control. We analyze NASA's Core Flight Software (cFS) to examine how authority, identity, communication, observability, and persistence are distributed across onboard components. Using NASA's flight-representative NOS3 simulator, we validate these weaknesses through five experiments implemented with a malicious onboard component that abuses legitimate architectural privileges. We then compare cFS with other modular flight software frameworks to identify recurring trust assumptions and architectural weaknesses. Our results show that a single compromised component can exploit broadly shared authority in ways that are difficult to distinguish from legitimate behavior. We conclude with architectural implications and discuss mechanisms for strengthening internal trust boundaries in future flight software systems.

cs.CR↗

arXiv2026

Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers

Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthorized modifications. However, machine learning-based classifiers are vulnerable to adversarial attacks that can evade detection. In this study, we present Kelpie, a novel framework for executing mimicry attacks, a stronger type of targeted evasion attacks, on binary function classifiers in a black-box, zero-query setting. Unlike previous approaches that rely on querying the target classifier to refine untargeted evasion attacks, Kelpie leverages code transformations that preserve the functionality of malicious payloads while causing them to be misclassified as we want. Through extensive experimentation, we demonstrate that Kelpie can successfully execute mimicry attacks against six state-of-the-art binary function classifiers representing different model architectures without requiring direct interaction with them. We further validate our approach with a practical demonstration, involving a keylogger and a wiper concealed within benign-looking functions embedded in an application. This work, to our best knowledge, is the first to demonstrate such a mimicry attack in a black-box, zero-query context, raising important questions about the reliability and security of existing machine learning-based binary function classifiers.

cs.LG↗

arXiv2026

An Evaluation of Chat Safety Moderations in Roblox

Roblox is among the most popular online gaming platforms, used by hundreds of millions of users every day. A substantial portion of these users are underage, who are at a greater risk, where abusive users may utilize Roblox's real-time chat interface to make the initial contact with potential victims. Roblox employs automated chat moderation mechanisms to detect potentially abusive messages; however, to date, their effectiveness has not been independently investigated. Toward this goal, we collected approximately 2 million chat messages from four games across multiple age groups and analyzed them to evaluate the moderation system. These messages were collected from public game servers following ethical and legal norms as well as Roblox's terms of service. We use this corpus to qualitatively study which types of unsafe chats escape the moderation system and how policy-violating users evade the moderation system. Given the dataset's scale, it is prohibitively expensive to conduct qualitative content analysis manually. Therefore, we adopt a two-step approach. First, we manually labeled safe and unsafe messages (n=99.8K) and used them as a ground truth to evaluate four locally hosted state-of-the-art large language models (LLMs). Next, the best-performing LLM was applied to the entire corpus to identify potentially unsafe messages, which we manually categorized using iterative open and axial coding methods until thematic saturation was reached. Overall, our findings reveal a troublesome reality: numerous instances of unsafe chat messages related to grooming, sexualizing minors, bullying, & harassment, violence, self-harm, and sharing sensitive information, etc., escaped the current moderation. Our analysis of users whose messages were previously flagged revealed that they continue to send harmful messages by employing a wide range of techniques to evade the moderation system.

cs.CY↗

arXiv2026

Revisiting Code Debloating with Ground Truth-based Evaluation

Program debloating aims to remove unused code to reduce performance overhead, attack surfaces, and maintenance costs. Over time, debloating has evolved across multiple layers (container, library, and application), each building on the principles of application-level debloating. Despite its central role, application-level debloating continues to rely on imperfect proxies for measuring performance, such as test-case-driven evaluation for correctness, code size for runtime efficiency, and gadget count reduction for estimating security posture. While there is widespread skepticism about using such imperfect proxies, the community still lacks standardized methodologies or benchmarks to assess the true performance of application-level software debloating. This experience paper aims to address the gap. We revisit the foundations of application-level debloating through a ground-truth-based evaluation paradigm. Our analysis of eight state-of-the-art debloaters - Blade, Chisel, Cov, CovA, Lmcas, Trimmer, Occam, and Razor - uncovers insights previously unattainable through traditional evaluations. These tools collectively span the spectrum of source-to-source, IR-to-IR, and binary-to-binary transformation paradigms, characterizing a holistic reassessment across abstraction levels. Our analysis reveals that while dynamic analysis-based tools often remove up to 94% of code that should be retained, static analysis-based approaches exhibit the opposite behavior, showing high false retention rates due to coarse-grained dependency over-approximation. Additionally, static analyses may add code by introducing specialized variants of functions. False retentions and removals not only cause functional incorrectness but may also lead to systematic inconsistency, robustness failures, and exploitable vulnerabilities.

cs.SE↗

arXiv2026

GraphQLify: Automated and Type Safety-Preserving GraphQL API Adoption

GraphQL provides a schema-based, strongly typed query language that enables highly efficient client-server communication. This paper introduces GraphQLify, an automated framework designed to migrate existing REST APIs to GraphQL. Unlike prior approaches that rely on relational databases, resource description frameworks (RDF), or machine-parsable specifications, GraphQLify leverages static source code analysis for precise type inference. This novel technique generates GraphQL schemas that guarantee end-to-end type safety, preserving a core advantage of adopting GraphQL. Furthermore, existing migration tools typically generate separate adapter servers, which introduce performance overhead via dynamic request binding and network latency. GraphQLify eliminates this by generating an embedded server that directly invokes the underlying API code, significantly improving performance. We evaluated GraphQLify on 834 APIs across nine popular open-source projects, where it successfully converted 100% of the APIs with zero type mismatches. In contrast, the current state-of-the-art tool, OASGraph, exhibited a 3.5% failure rate and a 42% type mismatch rate on the same dataset. Finally, our performance evaluation demonstrates that for workflows requiring five sequential API calls, clients using GraphQLify reduce data fetching time by a factor of 2 to 4 compared to their REST counterparts.

cs.SE↗

arXiv2026

Homoglyph-based Adversarial Perturbation of Introductory Computer Science Theory Problems

Different AI tools such as ChatGPT, Gemini, and Claude are becoming very popular. Although they are helpful for many day-to-day tasks, they can be used in unexpected ways. For example, the learning objectives of a course may not be achieved if students use these tools to solve their homework problems. This paper proposes a simple method to address this issue in the lazy student model. The method uses homoglyph-based adversarial perturbation to first modify the question without changing the semantic meaning of the question. Then a few characters are perturbed by their homoglyphs. Our experimental result shows the theoretical problems of introductory computer science courses can be effectively perturbed. We also propose an interactive tool to conveniently use our method.

cs.CY↗

arXiv2025

"It's not approved, but many, like myself, ignore the rule": Investigating the Landscape and Consequences of Unsanctioned Technology Use in Educational Institutes

Educators regularly use unsanctioned technologies (apps not formally approved by their institutions) for teaching, grading, and other academic tasks. While these tools often support instructional needs, they raise significant privacy, security, and regulatory compliance concerns. Despite its importance, understanding the adoptions and risks from the perspective of educators, who serve as de facto decision makers behind unsanctioned technology use, is largely understudied in existing literature.To address this gap, we conducted two surveys: one with 375 educators who listed 1,373 unsanctioned apps, and another with 21 administrators who either often help educators to set up educational technologies (EdTechs) or observe their security or privacy incidents. Our study identified 494 unique applications used by educators, primarily for pedagogical utility (n=213) and functional convenience (n=155), and the associated risks were often ignored. In fact, despite security and privacy concerns, many educators continued using the same apps (n = 62), citing a lack of alternatives or heavy dependence as barriers to discontinuation. We also found that fewer than a third of educators were aware of any institutional policy on unsanctioned technology use (K12: 30.3%, HEI: 24.8%), and 22 knowingly violated such policies. While 107 received formal warnings, only 33 adjusted their behavior. Finally, we conclude by discussing the implications of our findings and future recommendations to minimize the risks.

cs.CY↗

arXiv2025

Investigating the Security & Privacy Risks from Unsanctioned Technology Use by Educators

Educational technologies are revolutionizing how educational institutions operate. Consequently, it makes them a lucrative target for breach and abuse as they often serve as centralized hubs for diverse types of sensitive data, from academic records to health information. Existing studies looked into how existing stakeholders perceive the security and privacy risks of educational technologies and how those risks are affecting institutional policies for acquiring new technologies. However, outside of institutional vetting and approval, there is a pervasive practice of using applications and devices acquired personally. It is unclear how these applications and devices affect the dynamics of the overall institutional ecosystem. This study aims to address this gap by understanding why instructors use unsanctioned applications, how instructors perceive the associated risks, and how it affects institutional security and privacy postures. We designed and conducted an online survey-based study targeting instructors and administrators from K-12 and higher education institutions.

cs.CY↗

arXiv2024

Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation

While Large language model (LLM)-based programming assistants such as CoPilot and ChatGPT can help improve the productivity of professional software developers, they can also facilitate cheating in introductory computer programming courses. Assuming instructors have limited control over the industrial-strength models, this paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at understanding the efficacy of such perturbations in hindering actual code generation for introductory programming assignments. The user study suggests that i) perturbations combinedly reduced the average correctness score by 77%, ii) the drop in correctness caused by these perturbations was affected based on their detectability.

cs.CL↗

arXiv2024

An Investigation into Protestware

Protests are public expressions of personal or collective discontent with the current state of affairs. Although traditional protests involve in-person events, the ubiquity of computers and software opened up a new avenue for activism: protestware. The roots of protestware date back to the early days of computing. However, recent events in the Russo-Ukrainian war has sparked a new wave of protestware. While news and media are heavily reporting on individual protestware as they are discovered, the understanding of such software as a whole is severely limited. In particular, we do not have a detailed understanding of their characteristics and their impact on the community. To address this gap, we first collect 32 samples of protestware. Then, with these samples, we formulate characteristics of protestware using inductive analysis. In addition, we analyze the aftermath of the protestware which has potential to affect the software supply chain in terms of community sentiment and usage. We report that: (1) protestware has three notable characteristics, namely, i) the "nature of inducing protest" is diverse, ii) the "nature of targeting users" is discriminatory, and iii) the "nature of transparency" is not always respected; (2) disruptive protestware may cause substantial adverse impact on downstream users; (3) developers of protestware may not shift their beliefs even with pushback; (4) the usage of protestware from JavaScript libraries has been seen to generally increase over time.

cs.SE↗

arXiv2024

Trust, Because You Can't Verify:Privacy and Security Hurdles in Education Technology Acquisition Practices

The education technology (EdTech) landscape is expanding rapidly in higher education institutes (HEIs). This growth brings enormous complexity. Protecting the extensive data collected by these tools is crucial for HEIs as data breaches and misuses can have dire security and privacy consequences on the data subjects, particularly students, who are often compelled to use these tools. This urges an in-depth understanding of HEI and EdTech vendor dynamics, which is largely understudied. To address this gap, we conducted a semi-structured interview study with 13 participants who are in EdTech leadership roles at seven HEIs. Our study uncovers the EdTech acquisition process in the HEI context, the consideration of security and privacy issues throughout that process, the pain points of HEI personnel in establishing adequate protection mechanisms in service contracts, and their struggle in holding vendors accountable due to a lack of visibility into their system and power-asymmetry, among other reasons. We discuss certain observations about the status quo and conclude with recommendations for HEIs, researchers, and regulatory bodies to improve the situation.

cs.CY↗

arXiv2024

On the Contents and Utility of IoT Cybersecurity Guidelines

Cybersecurity concerns of Internet of Things (IoT) devices and infrastructure are growing each year. In response, organizations worldwide have published IoT security guidelines to protect their citizens and customers by providing recommendations on the development and operation of IoT systems. While these guidelines are being adopted, e.g. by US federal contractors, their content and merits have not been critically examined. Specifically, we do not know what topics and recommendations they cover and their effectiveness at preventing real-world IoT failures. In this paper, we address these gaps through a qualitative study of guidelines. We collect 142 IoT cybersecurity guidelines and sample them for recommendations until reaching saturation at 25 guidelines. From the resulting 958 unique recommendations, we iteratively develop a hierarchical taxonomy following grounded theory coding principles and study the guidelines' comprehensiveness. In addition, we evaluate the actionability and specificity of each recommendation and match recommendations to CVEs and security failures in the news they can prevent. We report that: (1) Each guideline has gaps in its topic coverage and comprehensiveness; (2) 87.2% recommendations are actionable and 38.7% recommendations can prevent specific threats; and (3) although the union of the guidelines mitigates all 17 of the failures from our news stories corpus, 21% of the CVEs evade the guidelines. In summary, we report shortcomings in each guideline's depth and breadth, but as a whole they address major security issues.

cs.SE↗

arXiv2023

The Queen's Guard: A Secure Enforcement of Fine-grained Access Control In Distributed Data Analytics Platforms

Distributed data analytics platforms (i.e., Apache Spark, Hadoop) provide high-level APIs to programmatically write analytics tasks that are run distributedly in multiple computing nodes. The design of these frameworks was primarily motivated by performance and usability. Thus, the security takes a back seat. Consequently, they do not inherently support fine-grained access control or offer any plugin mechanism to enable it, making them risky to be used in multi-tier organizational settings. There have been attempts to build "add-on" solutions to enable fine-grained access control for distributed data analytics platforms. In this paper, first, we show that straightforward enforcement of ``add-on'' access control is insecure under adversarial code execution. Specifically, we show that an attacker can abuse platform-provided APIs to evade access controls without leaving any traces. Second, we designed a two-layered (i.e., proactive and reactive) defense system to protect against API abuses. On submission of a user code, our proactive security layer statically screens it to find potential attack signatures prior to its execution. The reactive security layer employs code instrumentation-based runtime checks and sandboxed execution to throttle any exploits at runtime. Next, we propose a new fine-grained access control framework with an enhanced policy language that supports map and filter primitives. Finally, we build a system named SecureDL with our new access control framework and defense system on top of Apache Spark, which ensures secure access control policy enforcement under adversaries capable of executing code. To the best of our knowledge, this is the first fine-grained attribute-based access control framework for distributed data analytics platforms that is secure against platform API abuse attacks. Performance evaluation showed that the overhead due to added security is low.

cs.CR↗

arXiv2023

Bilingual Problems: Studying the Security Risks Incurred by Native Extensions in Scripting Languages

Scripting languages are continuously gaining popularity due to their ease of use and the flourishing software ecosystems that surround them. These languages offer crash and memory safety by design, thus, developers do not need to understand and prevent low-level security issues like the ones plaguing the C code. However, scripting languages often allow native extensions, which are a way for custom C/C++ code to be invoked directly from the high-level language. While this feature promises several benefits such as increased performance or the reuse of legacy code, it can also break the language's guarantees, e.g., crash-safety. In this work, we first provide a comparative analysis of the security risks of native extension APIs in three popular scripting languages. Additionally, we discuss a novel methodology for studying the misuse of the native extension API. We then perform an in-depth study of npm, an ecosystem which is most exposed to threats introduced by native extensions. We show that vulnerabilities in extensions can be exploited in their embedding library by producing reads of uninitialized memory, hard crashes or memory leaks in 33 npm packages, simply by invoking their API with well-crafted inputs. Moreover, we identify six open-source web applications in which such exploits can be deployed remotely by a weak adversary. Finally, we were assigned seven security advisories for the work presented in this paper, most labeled as high severity.

cs.CR↗

arXiv2021

Evaluation of Static Vulnerability Detection Tools with Java Cryptographic API Benchmarks

Several studies showed that misuses of cryptographic APIs are common in real-world code (e.g., Apache projects and Android apps). There exist several open-sourced and commercial security tools that automatically screen Java programs to detect misuses. To compare their accuracy and security guarantees, we develop two comprehensive benchmarks named CryptoAPI-Bench and ApacheCryptoAPI-Bench. CryptoAPI-Bench consists of 181 unit test cases that cover basic cases, as well as complex cases, including interprocedural, field sensitive, multiple class test cases, and path sensitive data flow of misuse cases. The benchmark also includes correct cases for testing false-positive rates. The ApacheCryptoAPI-Bench consists of 121 cryptographic cases from 10 Apache projects. We evaluate four tools, namely, SpotBugs, CryptoGuard, CrySL, and Coverity using both benchmarks. We present their performance and comparative analysis. The ApacheCryptoAPI-Bench also examines the scalability of the tools. Our benchmarks are useful for advancing state-of-the-art solutions in the space of misuse detection.

cs.CR↗

arXiv2020

Coding Practices and Recommendations of Spring Security for Enterprise Applications

Spring security is tremendously popular among practitioners for its ease of use to secure enterprise applications. In this paper, we study the application framework misconfiguration vulnerabilities in the light of Spring security, which is relatively understudied in the existing literature. Towards that goal, we identify 6 types of security anti-patterns and 4 insecure vulnerable defaults by conducting a measurement-based approach on 28 Spring applications. Our analysis shows that security risks associated with the identified security anti-patterns and insecure defaults can leave the enterprise application vulnerable to a wide range of high-risk attacks. To prevent these high-risk attacks, we also provide recommendations for practitioners. Consequently, our study has contributed one update to the official Spring security documentation while other security issues identified in this study are being considered for future major releases by Spring security community.

cs.CR↗

arXiv2020

Security Certification in Payment Card Industry: Testbeds, Measurements, and Recommendations

The massive payment card industry (PCI) involves various entities such as merchants, issuer banks, acquirer banks, and card brands. Ensuring security for all entities that process payment card information is a challenging task. The PCI Security Standards Council requires all entities to be compliant with the PCI Data Security Standard (DSS), which specifies a series of security requirements. However, little is known regarding how well PCI DSS is enforced in practice. In this paper, we take a measurement approach to systematically evaluate the PCI DSS certification process for e-commerce websites. We develop an e-commerce web application testbed, BuggyCart, which can flexibly add or remove 35 PCI DSS related vulnerabilities. Then we use the testbed to examine the capability and limitations of PCI scanners and the rigor of the certification process. We find that there is an alarming gap between the security standard and its real-world enforcement. None of the 6 PCI scanners we tested are fully compliant with the PCI scanning guidelines, issuing certificates to merchants that still have major vulnerabilities. To further examine the compliance status of real-world e-commerce websites, we build a new lightweight scanning tool named PciCheckerLite and scan 1,203 e-commerce websites across various business sectors. The results confirm that 86% of the websites have at least one PCI DSS violation that should have disqualified them as non-compliant. Our in-depth accuracy analysis also shows that PciCheckerLite's output is more precise than w3af. We reached out to the PCI Security Council to share our research results to improve the enforcement in practice.

cs.CR↗