Yusuke Kubo (NTT DOCOMO BUSINESS, Inc. / Waseda University), Fumihiro Kanei (NTT DOCOMO BUSINESS, Inc.), Mitsuaki Akiyama (NTT, Inc.), Takuro Wakai (Waseda University), Tatsuya Mori (Waseda University / NICT / RIKEN AIP)

GitHub Actions has become a dominant Continuous Integration/Continuous Delivery (CI/CD) platform, yet recent supply chain attacks like SolarWinds and tj-actions/changed-files highlight critical security vulnerabilities in such systems. While GitHub provides official security practices to mitigate these risks, the extent of their real-world implementation remains unknown. We present a mixed-methods study analyzing 338,812 public repositories and surveying over 100 developers to understand security practice implementation in GitHub Actions. Our findings reveal alarmingly low implementation rates across five key security practices, ranging from 0.6% to 52.9%. We identify three primary barriers: lack of awareness (up to 71.6% of non-adopters were unaware of practices), misconceptions about applicability, and concerns about operational costs. Repository characteristics such as organization ownership and recent development activity significantly correlate with better security practice implementation. Based on these empirical insights, we derive actionable recommendations that align intervention strategies with appropriate levels of automation, improve notification design to support awareness, strengthen platform- and IDE-level assistance, and clarify documentation on risks and applicability.

View More Papers

Fuzzilicon: A Post-Silicon Microcode-Guided x86 CPU Fuzzer

Johannes Lenzen (Technical University of Darmstadt), Mohamadreza Rostami (Technical University of Darmstadt), Lichao Wu (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More

The Case for LLM-Enhanced Backward Tracking

Jiahui Wang (Zhejiang University, Hangzhou, China), Xiangmin Shen (Hofstra University, Hempstead, NY, USA), Zhengkai Wang (Zhejiang University, Hangzhou, China), Zhenyuan Li (Zhejiang University, Hangzhou, China)

Read More

Was My Data Used for Training? Membership Inference in...

Xue Tan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Hao Luan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Mingyu Luo (Institute of Big Data, Fudan University, Shanghai, China and…

Read More