Fix it – If you Can! Towards Understanding the Impact of Tool Support and Domain Owners’ Reactions to SSHFP Misconfigurations
Hennig, A.; Neef, S.; Mayer, P.
2025. Kapil Singh, Gianluca Stringhini, Nick Nikiforakis, 104682, Elsevier
Research at KASTEL SRL
Research at KASTEL Security Research Labs is distinguished by its broad scope. Privacy and reliability aspects are naturally part of IT security; basic and applied research complement each other, and a comprehensive approach to IT security needs to look not just at the technical system, but also at its social environment as well as its human aspects.
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666 Publications from 2021 to 2025
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148 Papers at 42 Top Conferences 2021 to 2025
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65 Awards from 2021 to 2025
Our research covers a broad spectrum of cybersecurity. It combines the view of different disciplines in order to adress not only technical aspects, but also human factors and the legal framework.
Research Projects
Security for the Internet of Medical Things
Together with Wibu-Systems AG, KASTEL Security Research Labs is researching the project “Security for the Internet of Medical Things”, or “Sec4IoMT” for short. The aim is to find and exemplarily implement a solution approach for the long-term preservation of the security of Internet of Medical Things (IoMT) devices in critical infrastructures. When updating the security of individual components, the interoperability of the devices should be maintained.. At the same time, a security update must not lead to a loss of certification for the medical devices.
Research Projects
Secure Federated Machine Learning
Machine learning models require large amounts of data to be trained on, leaving little training data available in use cases with strong privacy requirements.The Secure Federated Machine Learning project of SAP and KASTEL Security Research Labs is investigating approaches for training a neural network on data from multiple parties without having to reveal their data to the other parties. These approaches can then be used in use cases with strong privacy requirements.
KASTEL Security Research Labs conduct interdisciplinary and application-oriented research. Therefore, we permanently maintain and expand numerous partnerships with industrial enterprises and research institutions. The resulting synergy effects flow into our research.
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156 Academic Cooperations in 25 Countries
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24 Ongoing Thirt-Party Funded Projects
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92 Cooperations with Companies
Cooperations and Exchanges
Propolis Privacy for Smart Cities
2021, the German-French cooperation PROPOLIS was launched. Prof. Thorsten Strufe from KASTEL Security Research Labs at KIT, together with SAP, the Urban Software Institute GmbH, Chemnitz, and EURECOM Frankreich, is analyzing AI based data protection for smart cities and citizens. The project will be concluded in 2024. Read more about PROPOLIS
Cooperations and Exchanges
SofDCar – Software-Defined Car
Since August 2021, a total of 13 companies and research institutions have been working in the publicly funded “Software-Defined Car” project (SofDCar for short). The experts from industry and science want to create standardized rules and processes together for the car of the future and its effective use of data. The project is publicly funded by the German Federal Ministry of Economic Affairs and Climate Action (BMWK). Read more about SofDCar
Publications
Security of the Lightning Network: Model Checking a Stepwise Refinement with TLA+
Grundmann, M.; Hartenstein, H.
2025. 20th International Conference on Integrated Formal Methods, iFM 2025, Paris, 17th-21st November 2025, Springer
Towards Bringing Vitruvius into the Cloud II: Against Attacks from the Internet
Armbruster, M.; Chebbi, F.; Weber, T.; Koziolek, A.
2025. Softwaretechnik-Trends, Gesellschaft für Informatik, Gesellschaft für Informatik (GI)
HubLink: A Novel Question Answering Retrieval Approach over Knowledge Graphs
Kaplan, A.; Keim, J.; Schneider, M.; Reussner, R. H.
2025. RAGE-KG 2025: The Second International Workshop on Retrieval-Augmented Generation Enabled by Knowledge Graphs, co-located with ISWC 2025, November 2–6, 2025, Nara, Japan. Ed.: Jennifer D., CEUR-WS
Quest for quality: a review of design knowledge on gamified AI training data annotation systems
Krohmann, S.; Rank, S.; Schmidt-Kraepelin, M.; Thiebes, S.; Sunyaev, A.
2025. Behaviour & Information Technology, 1–26.
How Toxic Can You Get? Search-based Toxicity Testing for Large Language Models
Corbo, S.; Bancale, L.; Gennaro, V. D.; Lestingi, L.; Scotti, V.; Camilli, M.
2025. IEEE Transactions on Software Engineering.