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arXiv 最近完成扫描:2026-11-09 18:35 · 全站最近入库:2026-11-09 13:22各信源进度 →

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版本更新 · v2arXiv:2601.20638

Supply Chain Insecurity: Exposing Vulnerabilities in iOS Dependency Management Systems

David Schmidt, Gabriel K. Gegenhuber, Edgar Weippl, Sebastian Schrittwieser

原文摘要

Dependency management systems are a critical component in software development, enabling the incorporation of existing functionality. However, misconfigurations and malicious actors in these systems pose security risks, leading to supply chain attacks. Despite the widespread use of smartphone apps, the security of dependency management systems in the iOS software supply chain has received limited attention. In this paper, we focus on CocoaPods, one of the most widely used dependency management systems for iOS app development, but also examine the security of Carthage and Swift Package Manager (SwiftPM). We demonstrate that iOS apps expose internal dependency names and versions. Attackers can exploit this information to register previously unclaimed dependencies in CocoaPods, enabling remote code execution (RCE) on developer machines and build servers. Additionally, we show that attackers can compromise dependencies by reclaiming abandoned domains and GitHub URLs. Analyzing 9,491 real-world iOS applications, we find that a significant fraction is vulnerable to these attacks, putting millions of users at risk, including applications from major organizations for which we confirmed exploitation via responsible disclosure. Finally, we compare iOS dependency management systems with Cargo, Go modules, npm, pip, and Maven, and discuss conceptual issues and practical mitigation strategies.

cs.CR

新论文 · v1arXiv:2610.36946

Too many clients spoil the logs: distributed parsing, aggregation techniques and metrics

André García Gómez, Viktor Beck, Max Landauer, Markus Wurzenberger, Florian Skopik, Andreas Rauber, Edgar Weipp

原文摘要

Log messages are employed to identify anomalies in IoT systems and have been widely used across many applications. To enable collaboration among infrastructures belonging to different organizations, federated log anomaly detection has been thoroughly investigated for this purpose, often assuming the availability of ground truth log templates. However, this is typically not the case, as it is still necessary to depend on log parsers to extract log templates at inference time, while the design and deployment of such parsers in distributed environments remains largely an open problem. To this end, we report on an empirical investigation of distributed parsing and its key challenges, providing insights and lessons learned towards closing the gap between research and application. To further refine and contextualize the resulting benchmarks, we examine and introduce new metrics that provide a more comprehensive view for evaluating distributed parsers and how they behave in distributed environments. We propose a set of lessons learned and recommendations to support the design, deployment, and evaluation of distributed parsers. In particular, we evaluate the performance of five distinct parsing algorithms in combination with aggregation techniques that allow a unified agreement of event-template mappings across all participating clients, a prerequisite for federated log anomaly detection. In total, 6,720 distinct distributed configurations were tested for this purpose.

cs.CR