Yuqiao Yang (UESTC), Yongzhao Zhang (UESTC), Wenhao Liu (GoGoByte Technology), Jun Li (GoGoByte Technology), Pengtao Shi (GoGoByte Technology), DingYu Zhong (UESTC), Jie Yang (UESTC), Ting Chen (UESTC), Sheng Cao (UESTC), Yuntao Ren (UESTC), Yongyue Wu (UESTC), Xiaosong Zhang (UESTC)

As modern vehicles evolve into intelligent and connected systems, their growing complexity introduces significant cybersecurity risks. Threat Analysis and Risk Assessment (TARA) has therefore become essential for managing these risks under mandatory regulations. However, existing TARA automation methods rely on static threat libraries, limiting their utility in the detailed, function-level analyses demanded by industry. This paper introduces DefenseWeaver, the first system that automates function-level TARA using component-specific details and large language models (LLMs). DefenseWeaver dynamically generates attack trees and risk evaluations from system configurations described in an extended OpenXSAM++ format, then employs a multi-agent framework to coordinate specialized LLM roles for more robust analysis. To further adapt to evolving threats and diverse standards, DefenseWeaver incorporates Low-Rank Adaptation (LoRA) fine-tuning and Retrieval-Augmented Generation (RAG) with expert-curated TARA reports. We validated DefenseWeaver through deployment in four automotive security projects, where it identified 11 critical attack paths, verified through penetration testing, and subsequently reported and remediated by the relevant automakers and suppliers. Additionally, DefenseWeaver demonstrated cross-domain adaptability, successfully applying to unmanned aerial vehicles (UAVs) and marine navigation systems. In comparison to human experts, DefenseWeaver outperformed manual attack tree generation across six assessment scenarios. Integrated into commercial cybersecurity platforms such as UAES and Xiaomi, DefenseWeaver has generated over 8,200 attack trees. These results highlight its ability to significantly reduce processing time, and its scalability and transformative impact on cybersecurity across industries.

View More Papers

Learning from Leakage: Database Reconstruction from Just a Few...

Peijie Li (Delft University of Technology), Huanhuan Chen (Delft University of Technology), Kaitai Liang (University of Turku and Delft University of Technology), Evangelia Anna Markatou (Delft University of Technology)

Read More

SoK: Analysis of Accelerator TEE Designs

Chenxu Wang (Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, China, Department of Computer Science and Engineering, Southern University of Science and Technology, China and Department of Computing, The Hong Kong Polytechnic University, China), Junjie Huang (Department of Computer Science and Engineering, Southern University of Science and Technology, China), Yujun Liang…

Read More

WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence...

Ronghua Li (The Hong Kong Polytechnic University), Shinan Liu (The University of Hong Kong), Haibo Hu (The Hong Kong Polytechnic University, PolyU Research Centre for Privacy and Security Technologies in Future Smart Systems), Qingqing Ye (The Hong Kong Polytechnic University), Nick Feamster (University of Chicago)

Read More