Machine Learning
and Security
Website of the
Chair of Machine Learning and Security
View from our building over Berlin.

Welcome and Overview

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 new Berlin Institute for the Foundations of Learning and Data (BIFOLD). Previously, we have been working at Technische Universität Braunschweig and the University of Göttingen.

Teaching in Winter

SMARTLAB — Smart Security Lab

This lab is a hands-on course that explores machine learning in computer security. Students design and develop intelligent systems for security problems such as attack detection, malware clustering, and vulnerability discovery. The developed systems are trained and evaluated on real-world data, providing insight into their strengths and weaknesses in practice. The lab is a continuation of the lecture "Machine Learning for Computer Security" and thus knowledge from that course is expected.

   Course Website    Module 41116 Type: Practical course Audience: Master

STEMO — Steganography with Language Models

This project explores how large language modules, such as ChatGPT, can be used for steganography. Students will form a red team (attackers) and a blue team (defenders). The red team will develop techniques to hide secret messages in generated texts, while the blue team will develop methods to detect these messages. The color of the teams will change after some time. The project is aimed at Master students. A good understanding of language models and strong programming skills are required.

   Course Website    Module 41102 Type: Project Audience: Master

See all teaching course.

Recent publications

On the Detection of Image-Scaling Attacks in Machine Learning.
Erwin Quiring, Andreas Müller and Konrad Rieck.
Proc. of the 39th Annual Computer Security Applications Conference (ACSAC), 2023. (to appear)

PAVUDI: Patch-based Vulnerability Discovery using Machine Learning.
Tom Ganz, Erik Imgrund, Martin Härterich and Konrad Rieck.
Proc. of the 39th Annual Computer Security Applications Conference (ACSAC), 2023. (to appear)

Broken Promises: Measuring Confounding Effects in Learning-based Vulnerability Discovery.
Erik Imgrund, Tom Ganz, Martin Härterich, Niklas Risse, Lukas Pirch and Konrad Rieck.
Proc. of the 16th ACM Workshop on Artificial Intelligence and Security (AISEC), 2023. (to appear)


See all publications.

Job Applications

We are generally looking for motivated and skilled PhD students and postdocs to join our group—even if we currently do not announce open positions. If you are passionate about research and interested in combining machine learning and computer security, contact us directly at

Before writing an unsolicited email, take some time to write a good cover letter. In this letter, you should describe why you are a good fit for our group and what research you enjoy doing. Include the result of (0x62df**215)%0xf0e5 in the subject line of your email.


Technische Universität Berlin
Machine Learning and Security, TEL 8-2
Ernst-Reuter-Platz 7
10587 Berlin, Germany

Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck