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Felix Weissberg

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Technische Universität Berlin
Machine Learning and Security (MLSec)

Franklinstraße 28/29, 10587 Berlin
https://mlsec.org/

© BIFOLD

Felix Weissberg

Doctoral Researcher

Affiliation:  BIFOLD

Mohammad Ebrahimi Fard, Felix Weissberg, Erik Imgrund, Thorsten Eisenhofer, Konrad Rieck

Shape-Shifting Malicious Code in Software Backdoors via Language Models

June 03, 2026
https://mlsec.tu-berlin.de/docs/2026-asiaccs.pdf

Felix Weissberg, Lukas Pirch, Erik Imgrund, Jonas Möller, Thorsten Eisenhofer, Konrad Rieck

LLM-based Vulnerability Discovery through the Lens of Code Metrics

September 23, 2025
https://doi.org/10.48550/arXiv.2509.19117

Jonas Möller, Lukas Pirch, Felix Weissberg, Sebastian Baunsgaard, Thorsten Eisenhofer, Konrad Rieck

Adversarial Inputs for Linear Algebra Backends

July 13, 2025
https://www.mlsec.org/docs/2025-icml.pdf

News
BIFOLD Update| Jun 02, 2026

BIFOLD Research Reveals a Blind Spot in Software Security

Malicious code need not be concealed within software itself. It can be embedded in seemingly harmless documentation. BIFOLD researchers demonstrate how large language models can hide executable functionality in natural-looking files, enabling difficult-to-detect attacks during the software build process. Presented at ACM AsiaCCS 2026.

News
Cyber SecurityMachine Learning| May 07, 2026

Complexity has to pay off

To test code before deployment, large language models (LLMs) are now frequently used. A research team from BIFOLD has shown in a recent study that the enormous technical effort behind these LLMs does not always pay off. The work was presented at the 48th IEEE/ACM International Conference on Software Engineering (ICSE) 2026.