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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CROW: Code Diversification for WebAssembly

Javier Cabrera Arteaga, Orestis Floros, Benoit Baudry, Martin Monperrus (KTH Royal Institute of Technology), Oscar Vera Perez (Univ Rennes, Inria, CNRS, IRISA)

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Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

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Free Proxies Unmasked: A Vulnerability and Longitudinal Analysis of...

Naif Mehanna (Univ. Lille / Inria / CNRS), Walter Rudametkin (IRISA / Univ Rennes), Pierre Laperdrix (CNRS, Univ Lille, Inria Lille), and Antoine Vastel (Datadome)

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Demo #1: Security of Multi-Sensor Fusion based Perception in...

Yulong Cao (University of Michigan), Ningfei Wang (UC, Irvine), Chaowei Xiao (Arizona State University), Dawei Yang (University of Michigan), Jin Fang (Baidu Research), Ruigang Yang (University of Michigan), Qi Alfred Chen (UC, Irvine), Mingyan Liu (University of Michigan) and Bo Li (University of Illinois at Urbana-Champaign)

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