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Item type:Publication, Access status: with files , Emerging challenges in threat modeling for GenAI-augmented systems: a view from the trenches(2026-10); ; ; 15 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automatic rule checking for microservices: supporting security analysis with explainability(Association for Computing Machinery (ACM), 2026-07-16); ; ; ; Software security analysis is often done manually, raising performance and correctness issues. Introducing automation is challenging because human verification of the outcomes is often required, especially for security assessment and certification. The distributed nature of microservice applications further increases these concerns. We present an approach for automatically checking architectural security rules on models of microservice applications. It provides explainability for verdicts of rules that are expressed as model queries in our rule specification language. This comprehensible, step-by-step evidence leverages traceability information from the input models to link to artifacts in code. Hence, the complete analysis process from source code via model to rule verdict can be traced and verified. Custom rules can be formulated in addition to a library of 25 best-practice architectural security rules. We evaluated the approach’s correctness by checking the 25 rules on 16 dataflow diagrams of microservice applications with a prototype (called MicroCertiSec) and observed promising results (precision=0.98; recall=1). Additionally, we performed an evaluation with industry experts and academics to gain initial insights into the approach’s usefulness for real-world security analysis. The nine participants gave highly positive feedback on usefulness and usability and stated they would use such an approach in their daily work.Publicationtype: Journal Article35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Prompting techniques for secure code generation: a systematic investigation(Association for Computing Machinery (ACM), 2025-10-04); ; ; ; Large Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from Natural Language (NL) instructions. However, studies have questioned their ability to produce secure code and, thereby, the quality of prompt-generated software. Alongside, various prompting techniques that carefully tailor prompts have emerged to elicit optimal responses from LLMs. Still, the interplay between such prompting strategies and secure code generation remains underexplored and calls for further investigations. Objective: In this study, we investigate the impact of different prompting techniques on the security of code generated from NL instructions by LLMs. Method: First, we perform a systematic literature review to identify the existing prompting techniques that can be used for code generation tasks. A subset of these techniques are evaluated on GPT-3, GPT-3.5, and GPT-4 models for secure code generation. For this, we used an existing dataset consisting of 150 NL security-relevant code generation prompts. Results: Our work (i) classifies potential prompting techniques for code generation (ii) adapts and evaluates a subset of the identified techniques for secure code generation tasks, and (iii) observes a reduction in security weaknesses across the tested LLMs, especially after using an existing technique called Recursive Criticism and Improvement (RCI), contributing valuable insights to the ongoing discourse on LLM-generated code security.Publicationtype: Journal Article40 50 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The Good, the Bad, and the (Un)Usable: A Rapid Literature Review on Privacy as Code(2025); ; Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.Publicationtype: Conference Paper29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , On the understandability of design-level security practices in infrastructure-as-code scripts and deployment architectures(Association for Computing Machinery (ACM), 2024-12-26); ; ; ; Infrastructure as Code (IaC) automates IT infrastructure deployment, which is particularly beneficial for continuous releases, for instance, in the context of microservices and cloud systems. Despite its flexibility in application architecture, neglecting security can lead to vulnerabilities. The lack of comprehensive architectural security guidelines for IaC poses challenges in adhering to best practices. We studied how developers interpret IaC scripts (source code) in two IaC technologies, Ansible and Terraform, compared to semi-formal IaC deployment architecture models and metrics regarding design-level security understanding. In a controlled experiment involving ninety-four participants, we assessed the understandability of IaC-based deployment architectures through source code inspection compared to semi-formal representations in models and metrics. We hypothesized that providing semi-formal IaC deployment architecture models and metrics as supplementary material would significantly improve the comprehension of IaC security-related practices, as measured by task correctness. Our findings suggest that semi-formal IaC deployment architecture models and metrics as supplementary material enhance the understandability of IaC security-related practices without significantly increasing duration. We also observed a significant correlation between task correctness and duration when models and metrics were provided.Publicationtype: Journal Article18 36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , CATMA: Conformance Analysis Tool for Microservice Applications(Association for Computing Machinery (ACM), 2024-04); ; ; ; The microservice architecture allows developers to divide the core functionality of their software system into multiple smaller services. However, this architectural style also makes it harder for them to debug and assess whether the system's deployment conforms to its implementation. We present CATMA, an automated tool that detects non-conformances between the system's deployment and implementation. It automatically visualizes and generates potential interpretations for the detected discrepancies. Our evaluation of CATMA shows promising results in terms of performance and providing useful insights. CATMA is available at https://cyberanalytics. nl/catma.github.io/, and a demonstration video is available at https://youtu.be/WKP1hG-TDKc.Publicationtype: Conference Paper22 65 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , How dataflow diagrams impact software security analysis : an empirical experimentModels of software systems are used throughout the software development lifecycle. Dataflow diagrams (DFDs), in particular, are well-established resources for security analysis. Many techniques, such as threat modelling, are based on DFDs of the analysed application. However, their impact on the performance of analysts in a security analysis setting has not been explored before. In this paper, we present the findings of an empirical experiment conducted to investigate this effect. Following a within-groups design, participants were asked to solve security-relevant tasks for a given microservice application. In the control condition, the participants had to examine the source code manually. In the model-supported condition, they were additionally provided a DFD of the analysed application and traceability information linking model items to artefacts in source code. We found that the participants (n = 24) performed significantly better in answering the analysis tasks correctly in the model-supported condition (41 % increase in analysis correctness). Further, participants who reported using the provided traceability information performed better in giving evidence for their answers (315% increase in correctness of evidence). Finally, we identified three open challenges of using DFDs for security analysis based on the insights gained in the experiment.Publicationtype: Conference Paper18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , What can self-admitted technical debt tell us about security? A mixed-methods study(IEEE, 2024); ; Self-Admitted Technical Debt (SATD) encompasses a wide array of sub-optimal design and implementation choices reported in software artefacts (e.g., code comments and commit messages) by developers themselves. Such reports have been central to the study of software maintenance and evolution over the last decades. However, they can also be deemed as dreadful sources of information on potentially exploitable vulnerabilities and security flaws. Objective: This work investigates the security implications of SATD from a technical and developer-centred perspective. On the one hand, it analyses whether security pointers disclosed inside SATD sources can be used to characterise vulnerabilities in Open-Source Software (OSS) projects and repositories. On the other hand, it delves into developers' perspectives regarding the motivations behind this practice, its prevalence, and its potential negative consequences. Method: We followed a mixed-methods approach consisting of (i) the analysis of a preexisting dataset containing 8,812 SATD instances and (ii) an online survey with 222 OSS practitioners. Results: We gathered 201 SATD instances through the dataset analysis and mapped them to different Common Weakness Enumeration (CWE) identifiers. Overall, 25 different types of CWEs were spotted across commit messages, pull requests, code comments, and issue sections, from which 8 appear among MITRE's Top-25 most dangerous ones. The survey shows that software practitioners often place security pointers across SATD artefacts to promote a security culture among their peers and help them spot flaky code sections, among other motives. However, they also consider such a practice risky as it may facilitate vulnerability exploits. Implications: Our findings suggest that preserving the contextual integrity of security pointers disseminated across SATD artefacts is critical to safeguard both commercial and OSS solutions against zero-day attacks.CCS CONCEPTS•Security and privacy → Human and societal aspects of security and privacy; Software security engineering; • Software and its engineering → Maintaining software.Publicationtype: Conference Paper34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Cybersecurity discussions in Stack Overflow: a developer-centred analysis of engagement and self-disclosure behaviour(2023-12-22); ; ; Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users’ engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site’s relevance and reputation into jeopardy. Hence, it is important to understand users’ participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users’ engagement and their privacy practices in SO. Particularly, it analyses developers’ self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers’ engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.Publicationtype: Journal Article43 69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Developers Need Protection, Too: Perspectives and Research Challenges for Privacy in Social Coding PlatformsSocial Coding Platforms (SCPs) like GitHub have become central to modern software engineering thanks to their collaborative and version-control features. Like in mainstream Online Social Networks (OSNs) such as Facebook, users of SCPs are subjected to privacy attacks and threats given the high amounts of personal and project-related data available in their profiles and software repositories. However, unlike in OSNs, the privacy concerns and practices of SCP users have not been extensively explored nor documented in the current literature. In this work, we present the preliminary results of an online survey (N=105) addressing developers' concerns and perceptions about privacy threats steaming from SCPs. Our results suggest that, although users express concern about social and organisational privacy threats, they often feel safe sharing personal and project-related information on these platforms. Moreover, attacks targeting the inference of sensitive attributes are considered more likely than those seeking to re-identify source-code contributors. Based on these findings, we propose a set of recommendations for future investigations addressing privacy and identity management in SCPs.Publicationtype: Conference Paper28