Christopher Gerking

Svetlana Pavlitska, Christopher Gerking, J. Marius Zöllner

Safety and security are essential for the admission and acceptance of automated and autonomous vehicles. Deep neural networks (DNNs) are widely used for perception and further components of the autonomous driving (AD) stack. However, they possess several limitations, including lack of generalization, efficiency, explainability, plausibility, and robustness. These insufficiencies can pose significant risks to autonomous driving systems. However, hazards, threats, and risks associated with DNN limitations in this domain have not been systematically studied so far. In this work, we propose a joint workflow for risk assessment combining the hazard analysis and risk assessment (HARA) following ISO 26262 and threat analysis and risk assessment (TARA) following the ISO/SAE 21434 to identify and analyze risks arising from inherent DNN limitations in AD perception.

Mahdi Jafari, Rahul Sharma, Sami Naim et al.

Software architecture models capture early design decisions that strongly influence system quality attributes, including security. However, architecture-level security assessment and feedback are often absent in practice, allowing security weaknesses to propagate into later phases of the software development lifecycle and, in some cases, to remain undiscovered, ultimately leading to vulnerable systems. In this paper, we bridge this gap by proposing the generation of Abstract Penetration Test Cases (APTCs) from software architecture models as an input to support architecture-level security assessment. We first introduce a metamodel that defines the APTC concept, and then investigate the use of large language models with different prompting strategies to generate meaningful APTCs from architecture models. To design the APTC metamodel, we analyze relevant standards and state of the art using two criteria: (i) derivability from software architecture, and (ii) usability for both architecture security assessment and subsequent penetration testing. Building on this metamodel, we then proceed to generate APTCs from software architecture models. Our evaluation shows promising results, achieving up to 93\% usefulness and 86\% correctness, indicating that the generated APTCs can substantially support both architects (by highlighting security-critical design decisions) and penetration testers (by providing actionable testing guidance).