Marina Moore, Aditya Sirish A Yelgundhalli (New York University), Justin Cappos (NYU)

Software supply chain attacks are a major concern and need to be addressed by every organization, including automakers. While there are many effective technologies in both the software delivery and broader software supply chain security space, combining these technologies presents challenges specific to automotive applications. We explore the trust boundaries between the software supply chain and software delivery systems to determine where verification of software supply chain metadata should occur, how to establish a root of trust, and how supply chain policy can be distributed. Using this exploration, we design Scudo, a secure combination of software over the air and software supply chain security technologies. We show that adding full verification of software supply chain metadata on-vehicle is not only inefficient, but is also largely unnecessary for security with multiple points of repository-side verification.

In addition, this paper describes a secure instantiation of Scudo, which integrates Uptane, a state of the art software update security solution, and in-toto, a comprehensive supply chain security framework. A practical deployment has shown that Scudo provides robust software supply chain protections. The client side power and processing costs are negligible, with the updated metadata comprising 0.504% of the total update transmission. The client side verification adds 0.21 seconds to the total update flow. This demonstrates that Scudo is easy to deploy in ways that can efficiently and effectively catch software supply chain attacks.

View More Papers

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More

DynPRE: Protocol Reverse Engineering via Dynamic Inference

Zhengxiong Luo (Tsinghua University), Kai Liang (Central South University), Yanyang Zhao (Tsinghua University), Feifan Wu (Tsinghua University), Junze Yu (Tsinghua University), Heyuan Shi (Central South University), Yu Jiang (Tsinghua University)

Read More

Improving the Robustness of Transformer-based Large Language Models with...

Lujia Shen (Zhejiang University), Yuwen Pu (Zhejiang University), Shouling Ji (Zhejiang University), Changjiang Li (Penn State), Xuhong Zhang (Zhejiang University), Chunpeng Ge (Shandong University), Ting Wang (Penn State)

Read More