Assurance cases are structured arguments that are commonly used to reason about the safety of a product or service. Currently, there is an ongoing push towards using assurance cases for also cybersecurity, especially in safety-critical domains, like automotive. While the industry is faced with the challenge of defining a sound methodology to build security assurance cases, the state of the art is rather immature. Therefore, we have conducted a thorough investigation of the (external) constraints and (internal) needs that security assurance cases have to satisfy in the context of the automotive industry. This has been done in the context of two large automotive companies in Sweden. The end result is a set of recommendations that automotive companies can apply in order to define security assurance cases that are (i) aligned with the constraints imposed by the existing and upcoming standards and regulations and (ii)harmonized with the internal product development processes and organizational practices. We expect the results to be also of interest for product companies in other safety-critical domains, like healthcare, transportation, and so on
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2
本报告介绍和概述了在“识别和缩小自动驾驶汽车保证差距”（Towards Identifying and Closing Gap in Assurance of Autonomous Road Vehicles，Tigars）项目中编制的技术专题说明（TTN）。这些说明旨在支持自动驾驶汽车的开发和评估。第1部分涉及：保证-概述和问题，弹性和安全要求，开放系统视角和ML系统的形式化验证和静态分析。本报告是第2部分，讨论：模拟和动态测试，深度和多样性防御，安全信息安全分析，标准和指南。
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
本报告介绍和概述了在“识别和缩小自动驾驶汽车保证差距”（Towards Identifying and Closing Gap in Assurance of Autonomous Road Vehicles，Tigars）项目中编制的技术专题说明（TTN）。这些说明旨在支持自动驾驶汽车的开发和评估。第1部分涉及：保证-概述和问题，弹性和安全要求，开放系统视角和ML系统的形式化验证和静态分析。第2部分：模拟和动态测试，深度和多样性防御，安全信息安全分析，标准和指南。
Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
RISE Research Institutes of Sweden（瑞典RISE研究院）；Lund University（隆德大学）；Semcon AB（Semcon公司）；Infotiv AB（Infotiv公司）；QRTECH AB（QRTECH公司）；Combitech AB（Combitech公司）
Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；University of Sydney（悉尼大学）；Singapore Management University（新加坡管理大学）
