Pub Date : 2025-11-05DOI: 10.1016/j.cose.2025.104752
Faheem Ahmed Shaikh , Damien Joseph , Eugene Kang
Public announcements of data breaches often lead to short-lived negative stock price reactions, raising questions about firms’ incentives for sustained cybersecurity improvements. This study applies legitimacy theory to examine how investor perceptions of a firm’s security practices—termed information security legitimacy—shape firm-specific risk after such announcements. Analyzing media sentiment following 485 U.S. data breach announcements, we find that firms with stronger information security legitimacy experience significantly lower firm-specific risk over six months. Additionally, shorter delays in public breach announcements strengthen this risk reduction. By linking data breach announcements with post-breach management, this study offers a unified framework showing how proactive security actions and timely communication mitigate long-term financial risk. These findings provide actionable guidance for security managers to prioritize rapid disclosure and strategic legitimacy management, advancing theory on stakeholder perceptions in cybersecurity.
{"title":"Reassessing information security perceptions following a data breach announcement: The role of post-breach management in firm-specific risk","authors":"Faheem Ahmed Shaikh , Damien Joseph , Eugene Kang","doi":"10.1016/j.cose.2025.104752","DOIUrl":"10.1016/j.cose.2025.104752","url":null,"abstract":"<div><div>Public announcements of data breaches often lead to short-lived negative stock price reactions, raising questions about firms’ incentives for sustained cybersecurity improvements. This study applies legitimacy theory to examine how investor perceptions of a firm’s security practices—termed information security legitimacy—shape firm-specific risk after such announcements. Analyzing media sentiment following 485 U.S. data breach announcements, we find that firms with stronger information security legitimacy experience significantly lower firm-specific risk over six months. Additionally, shorter delays in public breach announcements strengthen this risk reduction. By linking data breach announcements with post-breach management, this study offers a unified framework showing how proactive security actions and timely communication mitigate long-term financial risk. These findings provide actionable guidance for security managers to prioritize rapid disclosure and strategic legitimacy management, advancing theory on stakeholder perceptions in cybersecurity.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"161 ","pages":"Article 104752"},"PeriodicalIF":5.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.cose.2025.104736
John C. John , Arobinda Gupta , Shamik Sural
With an increase in the diversity and complexity of requirements from organizations for cloud computing, there is a growing need for integrating the services of multiple cloud providers. In such multi-cloud systems, data leakage is considered to be a major security concern, which is caused by illegitimate actions of malicious users often acting in collusion. The possibility of data leakage in such environments is characterized by the number of interoperations as well as the trustworthiness of users on the collaborating clouds. In this paper, we address the problem of secure multi-cloud collaboration from an Attribute-based Access Control (ABAC) policy management perspective. In particular, we define a problem that aims to formulate ABAC policy rules for establishing a high degree of inter-cloud accesses while eliminating potential paths for data leakage. A data leakage free ABAC policy generation algorithm is proposed that first determines the likelihood of data leakage and then attempts to maximize inter-cloud collaborations. We also pose several variants of the problem by imposing additional meaningful constraints on the nature of accesses. Experimental results on several large data sets show the efficacy of the proposed approach.
{"title":"Secure multi-cloud collaboration using data leakage free attribute-based access control policies","authors":"John C. John , Arobinda Gupta , Shamik Sural","doi":"10.1016/j.cose.2025.104736","DOIUrl":"10.1016/j.cose.2025.104736","url":null,"abstract":"<div><div>With an increase in the diversity and complexity of requirements from organizations for cloud computing, there is a growing need for integrating the services of multiple cloud providers. In such multi-cloud systems, data leakage is considered to be a major security concern, which is caused by illegitimate actions of malicious users often acting in collusion. The possibility of data leakage in such environments is characterized by the number of interoperations as well as the trustworthiness of users on the collaborating clouds. In this paper, we address the problem of secure multi-cloud collaboration from an Attribute-based Access Control (ABAC) policy management perspective. In particular, we define a problem that aims to formulate ABAC policy rules for establishing a high degree of inter-cloud accesses while eliminating potential paths for data leakage. A data leakage free ABAC policy generation algorithm is proposed that first determines the likelihood of data leakage and then attempts to maximize inter-cloud collaborations. We also pose several variants of the problem by imposing additional meaningful constraints on the nature of accesses. Experimental results on several large data sets show the efficacy of the proposed approach.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"161 ","pages":"Article 104736"},"PeriodicalIF":5.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1016/j.cose.2025.104735
Jian Zhang, Ping Chen
With the widespread deployment of IoT devices, firmware update process becomes the main target of malicious attack, which could cause large-scale devices bricking, critical information leakage, and vulnerability propagation through supply chain. Due to the complexity of firmware update process and implementation flaws, there are multiple types of firmware update vulnerabilities in each phase of firmware update process. Current studies cannot effectively detect these vulnerabilities through an automatic approach. Therefore, we propose FirmUpdate in this paper, an automated static firmware update vulnerability detection approach, which could recover the entire firmware update process and detect vulnerabilities in each phase of the firmware update process. FirmUpdate first constructs UG (Update Graph) to abstract the firmware update process into flow chat. Then, performs unsafe verification discovery and update-related taint analysis on UG. With these two methods, FirmUpdate could automatically detect firmware update vulnerabilities, including firmware verification, buffer overflow, and command injection vulnerabilities. FirmUpdate performs vulnerability detection in a firmware set that contains 131 firmware images from 7 vendors. The TPR (True Positive Rate) of vulnerability detection is 89.3 %. FirmUpdate detected 12 0-day vulnerabilities that had been assigned CVE ID, covering firmware verification, buffer overflow, and command injection vulnerabilities. Therefore, FirmUpdate provides an automated static approach to detect firmware update vulnerabilities, which could help IoT device vendors identify and fix vulnerabilities.
{"title":"FirmUpdate: Automated multi-phase static analysis for detecting firmware update vulnerabilities in IoT Linux-based firmware","authors":"Jian Zhang, Ping Chen","doi":"10.1016/j.cose.2025.104735","DOIUrl":"10.1016/j.cose.2025.104735","url":null,"abstract":"<div><div>With the widespread deployment of IoT devices, firmware update process becomes the main target of malicious attack, which could cause large-scale devices bricking, critical information leakage, and vulnerability propagation through supply chain. Due to the complexity of firmware update process and implementation flaws, there are multiple types of firmware update vulnerabilities in each phase of firmware update process. Current studies cannot effectively detect these vulnerabilities through an automatic approach. Therefore, we propose FirmUpdate in this paper, an automated static firmware update vulnerability detection approach, which could recover the entire firmware update process and detect vulnerabilities in each phase of the firmware update process. FirmUpdate first constructs UG (Update Graph) to abstract the firmware update process into flow chat. Then, performs unsafe verification discovery and update-related taint analysis on UG. With these two methods, FirmUpdate could automatically detect firmware update vulnerabilities, including firmware verification, buffer overflow, and command injection vulnerabilities. FirmUpdate performs vulnerability detection in a firmware set that contains 131 firmware images from 7 vendors. The TPR (True Positive Rate) of vulnerability detection is 89.3 %. FirmUpdate detected 12 0-day vulnerabilities that had been assigned CVE ID, covering firmware verification, buffer overflow, and command injection vulnerabilities. Therefore, FirmUpdate provides an automated static approach to detect firmware update vulnerabilities, which could help IoT device vendors identify and fix vulnerabilities.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104735"},"PeriodicalIF":5.4,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1016/j.cose.2025.104742
Paweł Smaga
This study identifies main characteristics of cyber attacks on 63 central banks, based on the analysis of 84 case studies of attacks, which occurred from 2010 to 2025. Combining data on attacks from numerous publicly available databases and using the framework of rational choice and routine activity theories, reveals a rise in frequency of attacks over time, often part of broader offensives on financial institutions. In line with political risk theory, politically motivated hacktivists, by launching DDoS, were behind half the attacks (54,8 %), while financially motivated criminals, using diverse attack vectors, were responsible for one-fifth (19 %). Despite the attacks having no systemic consequences, detailed data remains scarce, which hinders research. Results point to actionable policy implications for enhancing central banks’ cyber resilience in form of both preventative and reactive strategies.
{"title":"Evaluating cyber attacks on central banks – identification of trends in cyber threat landscape","authors":"Paweł Smaga","doi":"10.1016/j.cose.2025.104742","DOIUrl":"10.1016/j.cose.2025.104742","url":null,"abstract":"<div><div>This study identifies main characteristics of cyber attacks on 63 central banks, based on the analysis of 84 case studies of attacks, which occurred from 2010 to 2025. Combining data on attacks from numerous publicly available databases and using the framework of rational choice and routine activity theories, reveals a rise in frequency of attacks over time, often part of broader offensives on financial institutions. In line with political risk theory, politically motivated hacktivists, by launching DDoS, were behind half the attacks (54,8 %), while financially motivated criminals, using diverse attack vectors, were responsible for one-fifth (19 %). Despite the attacks having no systemic consequences, detailed data remains scarce, which hinders research. Results point to actionable policy implications for enhancing central banks’ cyber resilience in form of both preventative and reactive strategies.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104742"},"PeriodicalIF":5.4,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently transfer-based black-box adversarial attacks have garnered increasing attention owing to their high practicality. However, targeted attacks suffer low transfer success rates, especially when overfitting to their source model. In this paper, we observe that perturbations with higher generalization across different styles of images tend to have higher targeted transferability. Therefore, we propose a Style-Agnostic Attack (SAA) to enhance the transferability of targeted adversarial examples. Specifically, SAA introduces a content consistency loss that stimulates the learned perturbations to be style-agnostic by aligning the content features of the adversarially perturbed original and stylized images. Accordingly, SAA enhances the generalization of adversarial perturbations across different stylized images, thereby enhancing the transferability of targeted attacks. Our experiments demonstrate that SAA significantly improves the targeted transferability of adversarial examples. Furthermore, SAA is a generalizable approach that can be readily integrated with existing adversarial attacks to further enhance targeted transferability.
{"title":"Improving the transferability of targeted adversarial examples by style-agnostic attack","authors":"Wei Zhou , Zimin Mao , Shuijun Yin , Hanwen Zhang , Zhicheng Huang , Heng Li , Tiejun Wu , Wei Yuan","doi":"10.1016/j.cose.2025.104744","DOIUrl":"10.1016/j.cose.2025.104744","url":null,"abstract":"<div><div>Recently transfer-based black-box adversarial attacks have garnered increasing attention owing to their high practicality. However, targeted attacks suffer low transfer success rates, especially when overfitting to their source model. In this paper, we observe that perturbations with higher generalization across different styles of images tend to have higher targeted transferability. Therefore, we propose a Style-Agnostic Attack (SAA) to enhance the transferability of targeted adversarial examples. Specifically, SAA introduces a content consistency loss that stimulates the learned perturbations to be style-agnostic by aligning the content features of the adversarially perturbed original and stylized images. Accordingly, SAA enhances the generalization of adversarial perturbations across different stylized images, thereby enhancing the transferability of targeted attacks. Our experiments demonstrate that SAA significantly improves the targeted transferability of adversarial examples. Furthermore, SAA is a generalizable approach that can be readily integrated with existing adversarial attacks to further enhance targeted transferability.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104744"},"PeriodicalIF":5.4,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1016/j.cose.2025.104740
Federico Loi , Lorenzo Pisu , Leonardo Regano , Davide Maiorca , Giorgio Giacinto
Race conditions (RC) pose a critical security threat to web applications by exploiting the non-deterministic behavior of multithreaded request handling. This can lead to unpredictable outcomes such as data corruption, Time of Check to Time of Use (TOCTOU) vulnerabilities, and deadlocks. While previous research has identified poor design practices that contribute to RC vulnerabilities, no existing studies have explored the factors that influence the severity or impact of race conditions. This paper introduces a comprehensive methodology for testing and quantifying how different variables affect the exploitability of race conditions in vulnerable web servers, providing a framework for future research to investigate this issue more thoroughly.
In addition, we present an experimental evaluation of our methodology under various conditions. Specifically, we examine six RC exploitation tools using four different attack techniques across both HTTP/1.1 and HTTP/2 protocols. To provide a complete overview of race conditions across all HTTP versions, we also introduce the first race condition attack tool for HTTP/3, named QUICker. Furthermore, we assess how the choice of database management systems and programming languages used in web application deployment can affect susceptibility to race condition attacks. This study offers key insights into how these factors influence the exploitability of RC vulnerabilities.
{"title":"Race against time: investigating the factors that influence web race condition exploits","authors":"Federico Loi , Lorenzo Pisu , Leonardo Regano , Davide Maiorca , Giorgio Giacinto","doi":"10.1016/j.cose.2025.104740","DOIUrl":"10.1016/j.cose.2025.104740","url":null,"abstract":"<div><div>Race conditions (RC) pose a critical security threat to web applications by exploiting the non-deterministic behavior of multithreaded request handling. This can lead to unpredictable outcomes such as data corruption, Time of Check to Time of Use (TOCTOU) vulnerabilities, and deadlocks. While previous research has identified poor design practices that contribute to RC vulnerabilities, no existing studies have explored the factors that influence the severity or impact of race conditions. This paper introduces a comprehensive methodology for testing and quantifying how different variables affect the exploitability of race conditions in vulnerable web servers, providing a framework for future research to investigate this issue more thoroughly.</div><div>In addition, we present an experimental evaluation of our methodology under various conditions. Specifically, we examine six RC exploitation tools using four different attack techniques across both HTTP/1.1 and HTTP/2 protocols. To provide a complete overview of race conditions across all HTTP versions, we also introduce the first race condition attack tool for HTTP/3, named QUICker. Furthermore, we assess how the choice of database management systems and programming languages used in web application deployment can affect susceptibility to race condition attacks. This study offers key insights into how these factors influence the exploitability of RC vulnerabilities.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104740"},"PeriodicalIF":5.4,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1016/j.cose.2025.104733
Yuchen Niu, Siew-Kei Lam
Automated Insulin Delivery (AID) systems represent a significant advancement in diabetes care and wearable physiological closed-loop control technologies, integrating continuous glucose monitoring, control algorithms, and insulin pumps to improve blood glucose level control and reduce the burden of patient self-management. However, their increasing dependence on wireless communication and automatic control introduces security risks that may compromise patient privacy or result in life-threatening treatment errors. This paper presents a comprehensive survey of the AID system security landscape, covering technical vulnerabilities, regulatory frameworks, and commercial security measures. In addition, we conduct a systematic review of attack vectors and defence mechanisms proposed in the literature, following the PRISMA framework. Our findings highlight critical gaps, including the lack of specific security evaluation frameworks, insufficient protections in real-world deployments, and the need for comprehensive, lightweight, and adaptive defence mechanisms. We further investigate available research resources and outline open research challenges and future directions to guide the development of more secure and reliable AID systems. By focusing on AID systems, this review offers a representative case study for examining and improving the cybersecurity of safety-critical medical wearable systems.
{"title":"Securing automated insulin delivery systems: A review of security threats and protective strategies","authors":"Yuchen Niu, Siew-Kei Lam","doi":"10.1016/j.cose.2025.104733","DOIUrl":"10.1016/j.cose.2025.104733","url":null,"abstract":"<div><div>Automated Insulin Delivery (AID) systems represent a significant advancement in diabetes care and wearable physiological closed-loop control technologies, integrating continuous glucose monitoring, control algorithms, and insulin pumps to improve blood glucose level control and reduce the burden of patient self-management. However, their increasing dependence on wireless communication and automatic control introduces security risks that may compromise patient privacy or result in life-threatening treatment errors. This paper presents a comprehensive survey of the AID system security landscape, covering technical vulnerabilities, regulatory frameworks, and commercial security measures. In addition, we conduct a systematic review of attack vectors and defence mechanisms proposed in the literature, following the PRISMA framework. Our findings highlight critical gaps, including the lack of specific security evaluation frameworks, insufficient protections in real-world deployments, and the need for comprehensive, lightweight, and adaptive defence mechanisms. We further investigate available research resources and outline open research challenges and future directions to guide the development of more secure and reliable AID systems. By focusing on AID systems, this review offers a representative case study for examining and improving the cybersecurity of safety-critical medical wearable systems.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104733"},"PeriodicalIF":5.4,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-28DOI: 10.1016/j.cose.2025.104741
Pan Du, Chang Su, Xinping Wang, Tiezhi Li, Taiwen Kong, Zhaoyu He
With the intelligent transformation of the mining industry, mining cyber-physical systems (CPS) face multiple, interacting threats including cyberattacks, equipment failures, and natural disasters. Such interactions can severely undermine system stability and resilience. Existing assessment methods typically address single risk factors in isolation, neglecting both the interplay among multiple threats and the benefits of dynamic emergency response strategies. This paper proposes an integrated risk assessment framework tailored to mining CPS. We develop a multi-risk evaluation model that captures interactions among diverse hazard sources and embed it within a coordinated recovery network to optimize emergency response strategies. Through MATLAB-based simulations across various risk scenarios, we validate the effectiveness of the proposed model. Results demonstrate that the synergy among multiple threats markedly accelerates system performance degradation, whereas a coordinated recovery strategy prioritizing key nodes significantly enhances restoration efficiency and reduces resource consumption. Furthermore, our optimized resource allocation scheme substantially lowers total energy use, improves utilization rates, and bolsters overall system stability. The main contributions of this work are: (1) the introduction of a comprehensive multi-risk assessment framework providing theoretical foundations for intelligent mining; (2) the design of a coordinated recovery network model that refines the system restoration process; and (3) the implementation of a dynamic emergency response mechanism that elevates recovery efficiency and curtails resource expenditure.
{"title":"Risk assessment and defense resource allocation optimization for mining cyber-physical systems under coordinated attacks","authors":"Pan Du, Chang Su, Xinping Wang, Tiezhi Li, Taiwen Kong, Zhaoyu He","doi":"10.1016/j.cose.2025.104741","DOIUrl":"10.1016/j.cose.2025.104741","url":null,"abstract":"<div><div>With the intelligent transformation of the mining industry, mining cyber-physical systems (CPS) face multiple, interacting threats including cyberattacks, equipment failures, and natural disasters. Such interactions can severely undermine system stability and resilience. Existing assessment methods typically address single risk factors in isolation, neglecting both the interplay among multiple threats and the benefits of dynamic emergency response strategies. This paper proposes an integrated risk assessment framework tailored to mining CPS. We develop a multi-risk evaluation model that captures interactions among diverse hazard sources and embed it within a coordinated recovery network to optimize emergency response strategies. Through MATLAB-based simulations across various risk scenarios, we validate the effectiveness of the proposed model. Results demonstrate that the synergy among multiple threats markedly accelerates system performance degradation, whereas a coordinated recovery strategy prioritizing key nodes significantly enhances restoration efficiency and reduces resource consumption. Furthermore, our optimized resource allocation scheme substantially lowers total energy use, improves utilization rates, and bolsters overall system stability. The main contributions of this work are: (1) the introduction of a comprehensive multi-risk assessment framework providing theoretical foundations for intelligent mining; (2) the design of a coordinated recovery network model that refines the system restoration process; and (3) the implementation of a dynamic emergency response mechanism that elevates recovery efficiency and curtails resource expenditure.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104741"},"PeriodicalIF":5.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TikTok Mini Games represent a growing class of interactive, embedded experiences within social media platforms. Delivered through filters and effects, these games offer high engagement, but raise underexplored privacy concerns. Unlike standalone mobile games, TikTok Mini Games operate entirely within the app’s ecosystem-blurring the lines between entertainment, content creation, and data collection. Despite their popularity, little is known about how these features collect, process, and expose user data. This paper presents the first comprehensive privacy analysis of TikTok Mini Games using a mixed-method framework. We combine documentation review and experimental development, developmental toolkit analysis, interface and behavioral heuristic evaluation, network traffic and code analysis, data synthesis for privacy sensitivity, and comparative analysis to examine how privacy risks are architected, operationalized, and experienced. Our analysis reveals systemic privacy gaps: AR functionalities and motion sensors operate without granular consent mechanisms, UI designs lack transparency, and sensitive data streams (facial landmarks, geolocation, behavioral telemetry) are extensively collected, often without explicit interface-level disclosures. We also identify misalignments between TikTok’s runtime data practices and disclosed privacy policies, raising concerns about informed consent and accountability. A comparative analysis with Facebook Instant Games highlights structural differences in developer access, API use, and data governance. To address these concerns, we recommend platform-level reforms, including per-feature consent and embedded transparency mechanisms for interactive content. Our findings inform both the platform design and the regulatory discourse as gamified content embedded becomes a dominant mode of digital interaction.
{"title":"The privacy cost of fun: A measurement study of user data exposure in tiktok mini-games","authors":"Sideeq Bello, Lamine Noureddine, Babangida Bappah, Aisha Ali-Gombe","doi":"10.1016/j.cose.2025.104728","DOIUrl":"10.1016/j.cose.2025.104728","url":null,"abstract":"<div><div>TikTok Mini Games represent a growing class of interactive, embedded experiences within social media platforms. Delivered through filters and effects, these games offer high engagement, but raise underexplored privacy concerns. Unlike standalone mobile games, TikTok Mini Games operate entirely within the app’s ecosystem-blurring the lines between entertainment, content creation, and data collection. Despite their popularity, little is known about how these features collect, process, and expose user data. This paper presents the first comprehensive privacy analysis of TikTok Mini Games using a mixed-method framework. We combine documentation review and experimental development, developmental toolkit analysis, interface and behavioral heuristic evaluation, network traffic and code analysis, data synthesis for privacy sensitivity, and comparative analysis to examine how privacy risks are architected, operationalized, and experienced. Our analysis reveals systemic privacy gaps: AR functionalities and motion sensors operate without granular consent mechanisms, UI designs lack transparency, and sensitive data streams (facial landmarks, geolocation, behavioral telemetry) are extensively collected, often without explicit interface-level disclosures. We also identify misalignments between TikTok’s runtime data practices and disclosed privacy policies, raising concerns about informed consent and accountability. A comparative analysis with Facebook Instant Games highlights structural differences in developer access, API use, and data governance. To address these concerns, we recommend platform-level reforms, including per-feature consent and embedded transparency mechanisms for interactive content. Our findings inform both the platform design and the regulatory discourse as gamified content embedded becomes a dominant mode of digital interaction.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104728"},"PeriodicalIF":5.4,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1016/j.cose.2025.104729
Don Nalin Dharshana Jayaratne , Qian Lu , Abdur Rakib , Muhamad Azfar Ramli , Rakhi Manohar Mepparambath , Siraj Ahmed Shaikh , Hoang Nga Nguyen
The increasing integration of digital technologies in connected vehicles introduces cybersecurity risks that extend beyond individual vehicles, with the potential to disrupt entire transportation systems. Current practice (e.g., ISO/SAE 21434 TARA) focuses on threat identification and qualitative impact ratings at the vehicle boundary, with limited systemic quantification. This study presents a systematic, simulation-based methodology for quantifying the systemic operational and safety impacts of cyber threats on connected vehicles, evaluating cascading effects across the transport network. Three representative scenarios are examined: (I) telematics-induced sudden braking causing a cascading collision, (II) remote disabling on a motorway (M25) segment, and (III) a compromised Roadside Unit (RSU) spoofing Variable Speed Limit (VSL) and phantom lane closure messages to connected and automated vehicles (CAVs). The results highlight the potential for cascading safety incidents and systemic operational degradation, as evidenced by the defined systemic operational and safety vectors, factors that are insufficiently addressed in the current scope of the ISO/SAE 21434 standard, which primarily focuses on individual vehicle-level threats. The findings underscore the need to incorporate systemic evaluation into existing frameworks to enhance cyber resilience across connected vehicle ecosystems. The framework complements ISO/SAE 21434 by supplying quantitative, reproducible evidence for the impact rating step at a systemic scale, reducing assessor subjectivity and supporting policy and operations, enabling more data-driven evaluations of systemic cyber risks.
{"title":"A quantitative methodology for systemic impact assessment of cyber threats in connected vehicles","authors":"Don Nalin Dharshana Jayaratne , Qian Lu , Abdur Rakib , Muhamad Azfar Ramli , Rakhi Manohar Mepparambath , Siraj Ahmed Shaikh , Hoang Nga Nguyen","doi":"10.1016/j.cose.2025.104729","DOIUrl":"10.1016/j.cose.2025.104729","url":null,"abstract":"<div><div>The increasing integration of digital technologies in connected vehicles introduces cybersecurity risks that extend beyond individual vehicles, with the potential to disrupt entire transportation systems. Current practice (e.g., ISO/SAE 21434 TARA) focuses on threat identification and qualitative impact ratings at the vehicle boundary, with limited systemic quantification. This study presents a systematic, simulation-based methodology for quantifying the systemic operational and safety impacts of cyber threats on connected vehicles, evaluating cascading effects across the transport network. Three representative scenarios are examined: (I) telematics-induced sudden braking causing a cascading collision, (II) remote disabling on a motorway (M25) segment, and (III) a compromised Roadside Unit (RSU) spoofing Variable Speed Limit (VSL) and phantom lane closure messages to connected and automated vehicles (CAVs). The results highlight the potential for cascading safety incidents and systemic operational degradation, as evidenced by the defined systemic operational and safety vectors, factors that are insufficiently addressed in the current scope of the ISO/SAE 21434 standard, which primarily focuses on individual vehicle-level threats. The findings underscore the need to incorporate systemic evaluation into existing frameworks to enhance cyber resilience across connected vehicle ecosystems. The framework complements ISO/SAE 21434 by supplying quantitative, reproducible evidence for the impact rating step at a systemic scale, reducing assessor subjectivity and supporting policy and operations, enabling more data-driven evaluations of systemic cyber risks.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"160 ","pages":"Article 104729"},"PeriodicalIF":5.4,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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