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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NSFuzz: Towards Efficient and State-Aware Network Service Fuzzing

Shisong Qin (Tsinghua University), Fan Hu (State Key Laboratory of Mathematical Engineering and Advanced Computing), Bodong Zhao (Tsinghua University), Tingting Yin (Tsinghua University), Chao Zhang (Tsinghua University)

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Too Afraid to Drive: Systematic Discovery of Semantic DoS...

Ziwen Wan (University of California, Irvine), Junjie Shen (University of California, Irvine), Jalen Chuang (University of California, Irvine), Xin Xia (The University of California, Los Angeles), Joshua Garcia (University of California, Irvine), Jiaqi Ma (The University of California, Los Angeles), Qi Alfred Chen (University of California, Irvine)

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Hiding My Real Self! Protecting Intellectual Property in Additive...

Sizhuang Liang (Georgia Institute of Technology), Saman Zonouz (Rutgers University), Raheem Beyah (Georgia Institute of Technology)

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Empirical Scanning Analysis of Censys and Shodan

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

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