SearcharxivSearch

arXiv subjects

Martin Lambertz

Publications and source records attributed to Martin Lambertz.

3 recordsLinked to original sources

Do you dare to try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE

Digital forensic relies on validated tools and established procedures, yet the underlying operating systems, applications, and analysis tools evolve rapidly. This evolution can cause artifact behavior and tool outputs to drift, silently degrading repeatability and confidence in long-lived forensic interpretations. We present test-driven forensics, a practical approach that treats forensic expectations as executable specifications: expected artifacts and expected tool outputs are encoded as tests that can be rerun across versions to detect regressions. Crucially, our approach also enables State Transition Testing, validating the system's expected state after each user action rather than only performing post-mortem checks on a final disk image; this supports causal attribution and makes transient behavior testable. We implement the methodology in ADARE, an open-source framework that runs controlled experiments in virtual machines and simulates realistic user activity via computer-vision-guided GUI automation. ADARE includes a companion web platform for sharing experiments, environments, and results to facilitate independent reruns and peer verification. We evaluate ADARE in five case studies spanning artifact research and tool validation. In particular, a 25-version regression study of Autopsy reveals substantial, largely undocumented changes in exported report outputs, demonstrating how executable tests make drift measurable and reproducible at scale.

cs.CR

What's Next, Cloud? A Forensic Framework for Analyzing Self-Hosted Cloud Storage Solutions

Self-hosted cloud storage platforms like Nextcloud are gaining popularity among individuals and organizations seeking greater control over their data. However, this shift introduces new challenges for digital forensic investigations, particularly in systematically analyzing both client and server components. Despite Nextcloud's widespread use, it has received limited attention in forensic research. In this work, we critically examine existing cloud storage forensic frameworks and highlight their limitations. To address the gaps, we propose an extended forensic framework that incorporates device monitoring and leverages cloud APIs for structured, repeatable evidence acquisition. Using Nextcloud as a case study, we demonstrate how its native APIs can be used to reliably access forensic artifacts, and we introduce an open-source acquisition tool that implements this approach. Our framework equips investigators with a more flexible method for analyzing self-hosted cloud storage systems, and offers a foundation for further development in this evolving area of digital forensics.

cs.CR

Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support the meaningful use of LLMs in digital forensics. Through a theoretical analysis, we examine how MCP can be integrated across various forensic scenarios - ranging from artifact analysis to the generation of interpretable reports. We also outline both technical and conceptual considerations for deploying an MCP server in forensic environments. Our analysis reveals a wide range of use cases in which MCP not only strengthens existing forensic workflows but also facilitates the application of LLMs to areas of forensics where their use was previously limited. Furthermore, we introduce the concept of the inference constraint level - a way of characterizing how specific MCP design choices can deliberately constrain model behavior, thereby enhancing both auditability and traceability. Our insights demonstrate that MCP has significant potential as a foundational component for developing LLM-assisted forensic workflows that are not only more transparent, reproducible, and legally defensible, but also represent a step toward increased automation in digital forensic analysis. However, we also highlight potential challenges that the adoption of MCP may pose for digital forensics in the future.

cs.CR