arXiv subjects

Elmar Padilla

Publications and source records attributed to Elmar Padilla.

5 recordsLinked to original sources

arXiv2025

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.

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arXiv2024

Mens Sana In Corpore Sano: Sound Firmware Corpora for Vulnerability Research

Firmware corpora for vulnerability research should be scientifically sound. Yet, several practical challenges complicate the creation of sound corpora: Sample acquisition, e.g., is hard and one must overcome the barrier of proprietary or encrypted data. As image contents are unknown prior analysis, it is hard to select high-quality samples that can satisfy scientific demands. Ideally, we help each other out by sharing data. But here, sharing is problematic due to copyright laws. Instead, papers must carefully document each step of corpus creation: If a step is unclear, replicability is jeopardized. This has cascading effects on result verifiability, representativeness, and, thus, soundness. Despite all challenges, how can we maintain the soundness of firmware corpora? This paper thoroughly analyzes the problem space and investigates its impact on research: We distill practical binary analysis challenges that significantly influence corpus creation. We use these insights to derive guidelines that help researchers to nurture corpus replicability and representativeness. We apply them to 44 top tier papers and systematically analyze scientific corpus creation practices. Our comprehensive analysis confirms that there is currently no common ground in related work. It shows the added value of our guidelines, as they discover methodical issues in corpus creation and unveil miniscule step stones in documentation. These blur visions on representativeness, hinder replicability, and, thus, negatively impact the soundness of otherwise excellent work. Finally, we show the feasibility of our guidelines and build a new, replicable corpus for large-scale analyses on Linux firmware: LFwC. We share rich meta data for good (and proven) replicability. We verify unpacking, deduplicate, identify contents, provide ground truth, and show LFwC's utility for research.

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arXiv2023

Comprehensively Analyzing the Impact of Cyberattacks on Power Grids

The increasing digitalization of power grids and especially the shift towards IP-based communication drastically increase the susceptibility to cyberattacks, potentially leading to blackouts and physical damage. Understanding the involved risks, the interplay of communication and physical assets, and the effects of cyberattacks are paramount for the uninterrupted operation of this critical infrastructure. However, as the impact of cyberattacks cannot be researched in real-world power grids, current efforts tend to focus on analyzing isolated aspects at small scales, often covering only either physical or communication assets. To fill this gap, we present WATTSON, a comprehensive research environment that facilitates reproducing, implementing, and analyzing cyberattacks against power grids and, in particular, their impact on both communication and physical processes. We validate WATTSON's accuracy against a physical testbed and show its scalability to realistic power grid sizes. We then perform authentic cyberattacks, such as Industroyer, within the environment and study their impact on the power grid's energy and communication side. Besides known vulnerabilities, our results reveal the ripple effects of susceptible communication on complex cyber-physical processes and thus lay the foundation for effective countermeasures.

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arXiv2023

Open SESAME: Fighting Botnets with Seed Reconstructions of Domain Generation Algorithms

An important aspect of many botnets is their capability to generate pseudorandom domain names using Domain Generation Algorithms (DGAs). A cyber criminal can register such domains to establish periodically changing rendezvous points with the bots. DGAs make use of seeds to generate sets of domains. Seeds can easily be changed in order to generate entirely new groups of domains while using the same underlying algorithm. While this requires very little manual effort for an adversary, security specialists typically have to manually reverse engineer new malware strains to reconstruct the seeds. Only when the seed and DGA are known, past and future domains can be generated, efficiently attributed, blocked, sinkholed or used for a take-down. Common counters in the literature consist of databases or Machine Learning (ML) based detectors to keep track of past and future domains of known DGAs and to identify DGA-generated domain names, respectively. However, database based approaches can not detect domains generated by new DGAs, and ML approaches can not generate future domain names. In this paper, we introduce SESAME, a system that combines the two above-mentioned approaches and contains a module for automatic Seed Reconstruction, which is, to our knowledge, the first of its kind. It is used to automatically classify domain names, rate their novelty, and determine the seeds of the underlying DGAs. SESAME consists of multiple DGA-specific Seed Reconstructors and is designed to work purely based on domain names, as they are easily obtainable from observing the network traffic. We evaluated our approach on 20.8 gigabytes of DNS-lookups. Thereby, we identified 17 DGAs, of which 4 were entirely new to us.

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arXiv2022

dewolf: Improving Decompilation by leveraging User Surveys

Analyzing third-party software such as malware or firmware is a crucial task for security analysts. Although various approaches for automatic analysis exist and are the subject of ongoing research, analysts often have to resort to manual static analysis to get a deep understanding of a given binary sample. Since the source code of encountered samples is rarely available, analysts regularly employ decompilers for easier and faster comprehension than analyzing a binary's disassembly. In this paper, we introduce our decompilation approach dewolf. We developed a variety of improvements over the previous academic state-of-the-art decompiler and some novel algorithms to enhance readability and comprehension, focusing on manual analysis. To evaluate our approach and to obtain a better insight into the analysts' needs, we conducted three user surveys. The results indicate that dewolf is suitable for malware comprehension and that its output quality noticeably exceeds Ghidra and Hex-Rays in certain aspects. Furthermore, our results imply that decompilers aiming at manual analysis should be highly configurable to respect individual user preferences. Additionally, future decompilers should not necessarily follow the unwritten rule to stick to the code-structure dictated by the assembly in order to produce readable output. In fact, the few cases where dewolf already cracks this rule lead to its results considerably exceeding other decompilers. We publish a prototype implementation of dewolf and all survey results on GitHub.

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