#WeKnowCybersecurity

Cybersecurity Blog of Fraunhofer AISEC

Cybersecurity
Christian Banse

Automated cloud certification with EMERALD: Architecture, evidence, and trustworthy security

In the face of growing complexity and regulatory requirements, the security of cloud services is becoming increasingly challenging. However, conventional certification procedures require considerable financial and time investment to meet these requirements. That is why the EU research project EMERALD is pursuing a new approach: It is developing a framework for continuous, automated security certification based on semantically structured evidence. This article will discuss the concepts, methods, and validation approaches of the EMERALD platform.

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Christian Banse 30. October 2025
Quantum Computing
Sebastian Issel

Towards Classical Software Verification using Quantum Computers

In this post, we explore the possibility of accelerating the formal verification of classical programs using quantum computers. Common programming errors, such as null-pointer dereference and out-of-bound access, are prevalent sources of security flaws. Our approach involves generating a Satisfiability (SAT) instance from code snippets, which is satisfiable if the undesired behavior exists. This instance is then converted into an optimization problem, solved using quantum algorithms, thus potentially achieving asymptotically polynomial speedup.

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Sebastian Issel 13. August 2025
‚Industrial Security‘
Sebastian N. Peters

Gateway to the Danger Zone: Secure and Authentic Remote Reset in Machine Safety 

Modern manufacturing is rapidly digitizing, unlocking new business models and unprecedented efficiency. While remote operation has become commonplace, machine safety has still required hands-on, local intervention — until now. Our latest work at Fraunhofer AISEC bridges this gap with a secure, authentic remote reset system for safety events, blending future-proof cryptography and robust safety design. Here’s how we’re redefining the boundaries of safe, remote manufacturing.

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Sebastian N. Peters 22. July 2025
Trusted Artificial Intelligence
Dariush Wahdany

Using Prototypes for Private Machine Learning 

How can machine learning respect privacy without sacrificing fairness? Discover DPPL, a prototype-based method that provides strong privacy guarantees while boosting accuracy for underrepresented groups. By addressing bias in differentially private models, this approach ensures ethical and inclusive AI development without compromising performance.

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Dariush Wahdany 30. May 2025
Cryptography
Thomas Bellebaum

Multi-Party Computation in the Head – an Introduction

In 2016, the National Institute of Standards and Technology (NIST) announced a standardization process for quantum-secure cryptographic primitives. The goal was to find secure key encapsulation mechanisms (KEM) and signature schemes. One unique approach was the PICNIC signature scheme, a scheme utilizing the MPC-in-the-Head (MPCitH) paradigm. This made PICNIC an interesting approach, since its security relies on well researched block ciphers and hash functions. PICNIC was announced as an alternative candidate by NIST. A lot of follow-up schemes based on PICNIC, like BBQ, Banquet, and FEAST, were proposed using different block ciphers and variations on the original construction paradigm. In 2022, NIST announced a second call specifically for signature schemes. MPC-in-the-Head-based signature schemes became their own category, with multiple submissions in this call. This articel explains the core idea and functionality of early MPCitH based signature schemes and how we at Fraunhofer AISEC make use of the concepts.

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Thomas Bellebaum 30. April 2025
Trusted Artificial Intelligence
Nicolas Müller

How to build suitable datasets for successful detection of audio deepfakes

Deepfakes are a significant threat to democracy as well as private individuals and companies. They make it possible to spread disinformation, to steal intellectual property and to commit fraud, to name but a few. While robust AI detection systems offer a possible solution, their effectiveness depends largely on the quality of the underlying data, simply put: »Garbage in, garbage out.« But how do you create a dataset that is well suited to identifying the ever-evolving deepfakes and enables robust detection? And what constitutes high-quality training data?

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Nicolas Müller 31. March 2025
Cybersecurity
Stefan Tatschner

Parsing X.509 Certificates: How Secure Are TLS Libraries?

Digital certificates like X.509 are essential for secure internet communication by enabling authentication and data integrity. However, differences in how they are parsed by various TLS libraries can introduce security risks. A recent study by Fraunhofer AISEC analyzed six widely used X.509 parsers with real-world certificates. The findings reveal inconsistencies that could impact security-critical applications. In this article, we summarize the key results and explain why companies need to scrutinize their cryptographic libraries.

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Stefan Tatschner 26. February 2025
Cryptography
Ivan Gavrilan

Fortifying Cryptography with Impeccable Circuits: Impeccable Keccak Explained

Cybersecurity threats are evolving, and cryptographic implementations face growing risks from fault injection attacks. Fraunhofer AISEC’s research introduces Impeccable Keccak, a new approach to secure SPHINCS+, a post-quantum cryptography digital signature scheme that has been standardized by NIST in 2024. By leveraging impeccable circuits and ensuring active security, this represents a new approach to fault-resilient cryptography.

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Ivan Gavrilan 13. January 2025
Quantum Computing
Maximilian Wendlinger

Quantum and Classical AI Security: How to Build Robust Models Against Adversarial Attacks

The rise of quantum machine learning (QML) brings exciting advancements such as higher levels of efficiency or the potential to solve problems intractable for classical computers. Yet how secure are quantum-based AI systems against adversarial attacks compared to classical AI? A study conducted by Fraunhofer AISEC explores this question by analyzing and comparing the robustness of quantum and classical machine learning models under attack. Our findings about adversarial vulnerabilities and robustness in machine learning models form the basis for practical methods to defend against these attacks, which are introduced in this article.

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Maximilian Wendlinger 13. December 2024
IoT Security
Felix Oberhansl

Fraunhofer AISEC commissioned by the German Federal Office for Information Security (BSI): New study on the synthesis of cryptographic hardware implementations

The study by Fraunhofer AISEC on the security of cryptographic hardware implementations focuses on physical attacks on hardware, such as side-channel attacks and fault attacks, as well as measures to defend against them. These protective mechanisms can potentially be compromised by optimizations in the chip design process. The study shows that protective measures should be integrated into complex design processes and taken into account in hardware design synthesis in order to be resilient to hardware attacks. The findings will help hardware designers to develop robust and secure chips.

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Felix Oberhansl 7. November 2024

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