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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Property Inference Attacks Against GANs

Junhao Zhou (Xi'an Jiaotong University), Yufei Chen (Xi'an Jiaotong University), Chao Shen (Xi'an Jiaotong University), Yang Zhang (CISPA Helmholtz Center for Information Security)

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Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks

Chongzhou Fang (University of California, Davis), Han Wang (University of California, Davis), Najmeh Nazari (University of California, Davis), Behnam Omidi (George Mason University), Avesta Sasan (University of California, Davis), Khaled N. Khasawneh (George Mason University), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California, Davis)

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Detecting CAN Masquerade Attacks with Signal Clustering Similarity

Pablo Moriano (Oak Ridge National Laboratory), Robert A. Bridges (Oak Ridge National Laboratory) and Michael D. Iannacone (Oak Ridge National Laboratory)

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Fine-Grained Coverage-Based Fuzzing

Bernard Nongpoh (Université Paris Saclay), Marwan Nour (Université Paris Saclay), Michaël Marcozzi (Université Paris Saclay), Sébastien Bardin (Université Paris Saclay)

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