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In today’s fast-paced software development environments, DevOps has revolutionized the way teams build, test, and deploy applications by emphasizing automation, collaboration, and continuous integration/continuous delivery (CI/CD). However, with these advancements comes an increased need to address security proactively, giving rise to the DevSecOps movement, which integrates security practices into every phase of the software development lifecycle. DevOps security remains underrepresented in academic curricula despite its growing importance in the industry. To address this gap, this paper presents a handson learning module that combines Chaos Engineering and Whitebox Fuzzing to teach core principles of secure DevOps practices in an authentic, scenario-driven environment. Chaos Engineering allows students to intentionally disrupt systems to observe and understand their resilience, while White-box Fuzzing enables systematic exploration of internal code paths to discover cornercase vulnerabilities that typical tests might miss. The module was deployed across three academic institutions, and both pre- and post-surveys were conducted to evaluate its impact. Pre-survey data revealed that while most students had prior experience in software engineering and cybersecurity, the majority lacked exposure to DevOps security concepts. Post-survey responses gathered through ten structured questions showed highly positive feedback 66.7% of students strongly agreed, and 22.2% agreed that the hands-on labs improved their understanding of secure DevOps practices. Participants also reported increased confidence in secure coding, vulnerability detection, and resilient infrastructure design. These findings support the integration of experiential learning techniques like chaos simulations and white-box fuzzing into security education. By aligning academic training with realworld industry needs, this module effectively prepares students for the complex challenges of modern software development and operations. 

With the rapid growth of technology, accessing digital health records has become increasingly easier. Especially mobile health technology like mHealth apps help users to manage their health information, as well as store, share and access medical records and treatment information. Along with this huge advancement, mHealth apps are increasingly at risk of exposing protected health information (PHI) when security measures are not adequately implemented. The Health Insurance Portability and Accountability Act (HIPAA) ensures the secure handling of PHI, and mHealth applications are required to comply with its standards. But it is unfortunate to note that many mobile and mHealth app developers, along with their security teams, lack sufficient awareness of HIPAA regulations, leading to inadequate implementation of compliance measures. Moreover, the implementation of HIPAA security should be integrated into applications from the earliest stages of development to ensure data security and regulatory adherence throughout the software lifecycle. This highlights the need for a comprehensive framework that supports developers from the initial stages of mHealth app development and fosters HIPAA compliance awareness among security teams and end users. An iOS framework has been designed for integration into the Integrated Development Environment(IDE), accompanied by a web application to visualize HIPAA security concerns in mHealth app development. The web application is intended to guide both developers and security teams on HIPAA compliance, offering insights on incorporating regulations into source code, with the IDE framework enabling the identification and resolution of compliance violations during development. The aim is to encourage the design of secure and compliant mHealth applications that effectively safeguard personal health information. 

Large Language Models (LLMs) have demonstrated exceptional capabilities in the field of Artificial Intelligence (AI) and are now widely used in various applications globally. However, one of their major challenges is handling high-concurrency workloads, especially under extreme conditions. When too many requests are sent simultaneously, LLMs often become unresponsive which leads to performance degradation and reduced reliability in real-world applications. To address this issue, this paper proposes a queue-based system that separates request handling from direct execution. By implementing a distributed queue, requests are processed in a structured and controlled manner, preventing system overload and ensuring stable performance. This approach also allows for dynamic scalability, meaning additional resources can be allocated as needed to maintain efficiency. Our experimental results show that this method significantly improves resilience under heavy workloads which prevents resource exhaustion and enables linear scalability. The findings highlight the effectiveness of a queue-based web service in ensuring LLMs remain responsive even under extreme workloads. 

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through practical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities. 

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, handson, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, handson, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe our experiences in conducting an authentic learning based exercise related to white-box testing. From a conducted survey with 124 students, we observe the authentic learning-based exercise to be helpful for students to learn about white-box testing. 

The field of DevOps security education necessitates innovative approaches to effectively address the ever evolving challenges of cybersecurity. Adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post-lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities.