William Findlay (Carleton University) and AbdelRahman Abdou (Carleton University)

While security researchers are adept at discovering vulnerabilities and measuring their impact, disclosing vulnerabilities to affected stakeholders has traditionally been difficult. Beyond public notices such as CVEs, there have traditionally been few appropriate channels through which to directly communicate the nature and scope of a vulnerability to those directly impacted by it. Security.txt is a relatively new proposed standard that hopes to change this by defining a canonical file format and URI through which organizations can provide contact information for vulnerability disclosure. However, despite its favourable characteristics, limited studies have systematically analyzed how effective Security.txt might be for a widespread vulnerability notification campaign. In this paper, we present a large-scale study of Security.txt’s adoption over the top 1M popular domains according to the Tranco list. We measure specific features of Security.txt files such as contact information, preferred language, and RFC version compliance. We then analyze these results to better understand how suitable the current Security.txt standard is for facilitating a large-scale vulnerability notification campaign, and make recommendations for improving future version of the standard.

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Carl Magnus Bruhner (Linkoping University), David Hasselquist (Linkoping University, Sectra Communications), Niklas Carlsson (Linkoping University)

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Christopher Bennett, AbdelRahman Abdou, and Paul C. van Oorschot (School of Computer Science, Carleton University, Canada)

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Building Embedded Systems Like It’s 1996

Ruotong Yu (Stevens Institute of Technology, University of Utah), Francesca Del Nin (University of Padua), Yuchen Zhang (Stevens Institute of Technology), Shan Huang (Stevens Institute of Technology), Pallavi Kaliyar (Norwegian University of Science and Technology), Sarah Zakto (Cyber Independent Testing Lab), Mauro Conti (University of Padua, Delft University of Technology), Georgios Portokalidis (Stevens Institute of…

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MIRROR: Model Inversion for Deep Learning Network with High...

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University), Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

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