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Eduard Hirsch

Publications and source records attributed to Eduard Hirsch.

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Hidden Ciphers and Where to Find Them: Static Discovery and Assessment of Cryptographic Assets in Software

Modern software systems rely on cryptography for data protection, authentication, and trust establishment, yet organizations often lack a structured view of the cryptography deployed across source code, configuration, dependencies, and cryptographic files. This lack of visibility complicates security governance and post-quantum migration planning. This paper presents a static approach for discovering and assessing cryptographic assets in software systems. We introduce a classification of Crypto-Material, Crypto-Artifacts, and Crypto-Invocations, derive an extensible scanner-independent rule repository from it, and implement a static scanner that applies these rules to produce CBOM-oriented output. We evaluate the approach on a synthetic benchmark with known ground truth and on a real-world infrastructure of ten deployed services. The scanner achieves an F1 score of 0.75 for asset discovery and correctly annotates 91% of expected weaknesses and vulnerabilities. In the realworld setting, it processes 57 610 files in under six minutes and discovers 370 cryptographic assets, including six CVE-linked vulnerabilities and 52 post-quantum migration candidates. Real-world coverage is assessed against a manually compiled reference list rather than an exhaustive one. These results show that classification-driven static discovery can provide practical cryptographic transparency for governance and post-quantum migration planning.

cs.CR

Architecture-Derived CBOMs for Cryptographic Migration: A Security-Aware Architecture Tradeoff Method

Cryptographic migration driven by algorithm deprecation, regulatory change, and post-quantum readiness requires more than an inventory of cryptographic assets. Existing Cryptographic Bills of Materials (CBOMs) are typically tool- or inventory-derived. They lack architectural intent, rationale, and security context, limiting their usefulness for migration planning. This paper introduces Security-Aware Architecture Tradeoff Analysis Method (SATAM), a security-aware adaptation of scenario-based architecture evaluation that derives an architecture-grounded, context-sensitive CBOM. SATAM integrates established approaches: ATAM, arc42, STRIDE, ADR, and CARAF. These are included to identify and analyze security-relevant cryptographic decision points and document them as explicit architectural decisions. These artifacts are used to annotate CBOM entries with architectural context, security intent, and migration-critical metadata using CycloneDX-compatible extensions. Following a Design Science Research approach, the paper presents the method design, a conceptual traceability model, and an illustrative application. The results demonstrate that architecture-derived CBOMs capture migration-relevant context that is typically absent from inventory-based approaches. Thereby, SATAM improves availability of information required for informed cryptographic migration planning and long-term cryptographic agility.

cs.CR

Detecting Cryptographically Relevant Software Packages with Collaborative LLMs

IT systems are facing an increasing number of security threats, including advanced persistent attacks and future quantum-computing vulnerabilities. The move towards crypto-agility and post-quantum cryptography (PQC) requires a reliable inventory of cryptographic assets across heterogeneous IT environments. Due to the sheer amount of packets, it is infeasible to manually detect cryptographically relevant software. Further, static code analysis pipelines often fail to address the diversity of modern ecosystems. Our research explores the use of large language models (LLMs) as heuristic tools for cryptographic asset discovery. We propose a collaborative framework that employs multiple LLMs to assess software relevance and aggregates their outputs through majority voting. To preserve data privacy, the approach operates on-premises without reliance on external servers. Using over 65,000 Fedora Linux packages, we evaluate the reliability of this method through statistical analysis, inter-model agreement, and manual validation. Preliminary results suggest that~LLM ensembles can serve as an efficient first-pass filter for identifying cryptographic software, resulting in reduced manual workload and assisting PQC transition. The study also compares on-premises and online LLM configurations, highlighting key advantages, limitations, and future directions for automated cryptographic asset discovery.

cs.CR

Toward a Common Understanding of Cryptographic Agility -- A Systematic Review

Cryptographic agility is gaining attention due to its crucial role in maintaining cryptographic security in a rapidly evolving technological landscape. However, despite its increasing importance, the term cryptographic agility remains vaguely defined and there is no clear consensus on its exact meaning. This lack of clarity poses a challenge since the need for agility becomes more urgent as new cryptographic vulnerabilities and advanced computing threats emerge, emphasizing the need for a systematic approach to clarify and refine the notion on cryptographic agility. In this paper, we systematize the concept of cryptographic agility by providing three research contributions. First, we review current definitions across academic and gray literature, identifying six distinct categories to differentiate every aspect within the definitions. Second, we synthesize these insights to establish a comprehensive, canonical definition of cryptographic agility. Third, we explore the relationship between cryptographic agility and the related concepts cryptographic versatility and interoperability. In our discussion, we examine the relevance of cryptographic agility, highlight its trade-offs with complexity, assess its individual applicability, and illustrate its various contexts by offering an additional application-specific definition. Our work provides a new perspective on cryptographic agility and related concepts, based on systematical research to clarify and enhance its future use.

cs.CR

On the Formalization of Cryptographic Migration

We present a novel approach to gaining insight into the structure of cryptographic migration problems which are classic problems in applied cryptography. We use a formal model to capture the inherent dependencies and complexities of such transitions. Using classical mathematical results from combinatorics, probability theory, and combinatorial analysis, we evaluate the challenges of migrating large cryptographic IT infrastructures and prove that - in a suitable sense - cryptographic migration exhibits a certain expected complexity. We also provide numerical data for selected parameter sets. Furthermore, we analyze the proposed model in terms of real-world patterns and its practical applicability. Additionally, we discuss the challenges of modeling real-world migration projects. As concrete examples we examine the transition to post-quantum cryptography of the CI/CD system GitLab and the multi-level technological transition of distribution power grids. This work paves the way for future advancements in both the theoretical understanding and practical implementation of cryptographic migration strategies.

cs.CR

An OPC UA-based industrial Big Data architecture

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

cs.IR

Digital Twins of Business Processes as Enablers for IT / OT Integration

The vision of Industry 4.0 introduces new requirements to Operational Technology (OT) systems. Solutions for these requirements already exist in the Information Technology (IT) world, however, due to the different characteristics of both worlds, these solutions often cannot be directly used in the world of OT. We therefore propose an Industrial Business Process Twin (IBPT), allowing to apply methods of one world to another not directly but, instead, to a representation, that is in bidirectional exchange with the other world. The proposed IBPT entity acts as an intermediary, decoupling the worlds of IT and OT, thus allowing for an integration of IT and OT components of different manufacturers and platforms. Using this approach, we demonstrate the four essential Industry 4.0 design principles information transparency, technical assistance, interconnection and decentralized decisions based on the gamified Industry 4.0 scenario of playing the game of Nine Men's Morris. This scenario serves well for agent based Artificial Intelligence (AI)-research and education. We develop an Open Platform Communications Unified Architecture (OPC UA) information and communication model and then evaluate the IBPT component with respect to the different views of the Reference Architecture Model Industry 4.0 (RAMI4.0).

cs.SE

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about connecting several machines. For the latter option often experience is missing with the data science staff in order to navigate through the software jungle. In this technical report, we illustrate the decision process towards an on-premises infrastructure, our implemented system architecture, and the transformation of the software stack towards a scaleable GPU cluster system.

cs.DC

Immutable and Democratic Data in permissionless Peer-to-Peer Systems

Conventional data storage methods like SQL and NoSQL offer a huge amount of possibilities with one major disadvantage, having to use a centralized authority. This authority may be in the form of a centralized or decentralized master server or a permissioned peer-to-peer setting. This paper looks at different technologies on how to persist data without using a central authority, mainly looking at permissionless peer-to-peer networks, primarily Distributed Ledger Technologies (DLTs) and a combination of DLTs with conventional databases. Afterwards it is shown how a system like this might be implemented in two prototypes which are then evaluated against conventional databases.

cs.DB