Research Group
Machine Learning
and Security
and Security
View from our building over Berlin.
Our research group conducts fundamental research at the intersection of computer security and machine learning. On the one end, we are interested in developing intelligent systems that can learn to protect computers from attacks and identify security problems automatically. On the other end, we explore the security and privacy of machine learning by developing novel attacks and defenses.
We are part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin. Previously, we have been working at Technische Universität Braunschweig and the University of Göttingen.
April 22, 2026 — The summer semester is here! We offer new courses, including our lecture on machine learning for security and seminars on physical AI attacks and LLMs in security. Sign up on the ISIS platform 📚.
April 18, 2026 — We are attending ICSE in Rio de Janeiro, 🇧🇷. Lukas is presenting our paper on LLM-based vulnerability discovery and its unexpected relation to code metrics.
March 23, 2026 — We are attending SaTML in Munich, 🇩🇪 with several team members. Konrad has been serving as Program Chair for the second year, and we are looking forward to the program.
See all news and updates of the research group.
A Large-Scale Study of Personalized Phishing using Large Language Models.
35th USENIX Security Symposium, 2026.
Practical Type Inference: High-Throughput Recovery of Real-World Structures and Function Signatures.
17th ACM Conference on Data and Applications Security and Privacy (CODASPY), 2026.
Shape-Shifting Malicious Code in Software Backdoors via Language Models.
21st ACM Asia Conference on Computer and Communications Security (ASIACCS), 2026.
LLM-based Vulnerability Discovery through the Lens of Code Metrics.
48th IEEE/ACM International Conference on Software Engineering (ICSE), 2026.
See all publications of the research group.
DISTEL — Differential Security Testing of LLMs
This project introduces differential security testing for LLMs. It aims to compare LLM behavior across model variants, software stacks, and hardware platforms. The goal is to assess whether differences can impact the security and trustworthiness of LLMs. The project is part of the excellence cluster CASA.
AIGENCY — Opportunities and Risks of Generative AI in Security
The project aims to systematically investigate the opportunities and risks of generative artificial intelligence in computer security. It explores generative models as a new tool as well as a new threat. The project is joint work with Fraunhofer AISEC, CISPA, FU Berlin, and Aleph Alpha.
MALFOY — Machine Learning for Offensive Computer Security
The ERC Consolidator Grant MALFOY explores the application of machine learning in offensive computer security. It is an effort to understand how learning algorithms can be used by attackers and how this threat can be effectively mitigated.
See all projects of the research group.
BIFOLD & TU Berlin
Machine Learning and Security (FR7-4)
Franklinstraße 28-29
10587 Berlin, Germany
Office: office@mlsec.tu-berlin.de
Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck