Christian Mainka (Ruhr University Bochum), Vladislav Mladenov (Ruhr University Bochum), Simon Rohlmann (Ruhr University Bochum)

Digitally signed PDFs are used in contracts and invoices to guarantee the authenticity and integrity of their content. A user opening a signed PDF expects to see a warning in case of *any* modification. In 2019, Mladenov et al. revealed various parsing vulnerabilities in PDF viewer implementations. They showed attacks that could modify PDF documents without invalidating the signature. As a consequence, affected vendors of PDF viewers implemented countermeasures preventing *all* attacks.

This paper introduces a novel class of attacks, which we call *shadow* attacks. The *shadow* attacks circumvent all existing countermeasures and break the integrity protection of digitally signed PDFs. Compared to previous attacks, the *shadow* attacks do not abuse implementation issues in a PDF viewer. In contrast, *shadow* attacks use the enormous flexibility provided by the PDF specification so that *shadow* documents remain standard-compliant. Since *shadow* attacks abuse only legitimate features, they are hard to mitigate.

Our results reveal that 16 (including Adobe Acrobat and Foxit Reader) of the 29 PDF viewers tested were vulnerable to *shadow* attacks. We introduce our tool *PDF-Attacker* which can automatically generate *shadow* attacks. In addition, we implemented *PDF-Detector* to prevent *shadow* documents from being signed or forensically detect exploits after being applied to signed PDFs.

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DOVE: A Data-Oblivious Virtual Environment

Hyun Bin Lee (University of Illinois at Urbana-Champaign), Tushar M. Jois (Johns Hopkins University), Christopher W. Fletcher (University of Illinois at Urbana-Champaign), Carl A. Gunter (University of Illinois at Urbana-Champaign)

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From WHOIS to WHOWAS: A Large-Scale Measurement Study of...

Chaoyi Lu (Tsinghua University; Beijing National Research Center for Information Science and Technology), Baojun Liu (Tsinghua University; Beijing National Research Center for Information Science and Technology; Qi An Xin Group), Yiming Zhang (Tsinghua University; Beijing National Research Center for Information Science and Technology), Zhou Li (University of California, Irvine), Fenglu Zhang (Tsinghua University), Haixin Duan…

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Mondrian: Comprehensive Inter-domain Network Zoning Architecture

Jonghoon Kwon (ETH Zürich), Claude Hähni (ETH Zürich), Patrick Bamert (Zürcher Kantonalbank), Adrian Perrig (ETH Zürich)

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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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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)