Michael Schwarz (Graz University of Technology), Moritz Lipp (Graz University of Technology), Claudio Canella (Graz University of Technology), Robert Schilling (Graz University of Technology and Know-Center GmbH), Florian Kargl (Graz University of Technology), Daniel Gruss (Graz University of Technology)

Out-of-order execution and speculative execution are among the biggest contributors to performance and efficiency of modern processors. However, they are inconsiderate, leaking secret data during the transient execution of instructions. Many solutions and hardware fixes have been proposed for mitigating transient-execution attacks. However, they either do not eliminate the leakage entirely or introduce unacceptable performance penalties.

In this paper, we propose ConTExT, a Considerate Transient Execution Technique. ConTExT is a minimal and fully backwards compatible architecture change. The basic idea of ConTExT is that secrets can enter registers, but not transiently leave them. ConTExT transforms Spectre from a problem that cannot be solved purely in software, to a problem that is not easy to solve, but solvable in software. For this, ConTExT requires minimal modifications of applications, compilers, operating systems, and the hardware. ConTExT offers full protection for secrets in memory and secrets in registers. With ConTExT-light, we propose a software-only solution of ConTExT for existing commodity CPUs protecting secrets in memory. We evaluate the security and performance of ConTExT. Even when over-approximating, we observe no performance overhead for unprotected code and data, and an overhead of 71.14% for security-critical applications, which is below the overhead of currently recommended state-of-the-art mitigation strategies.

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Arpita Patra (Indian Institute of Science, Bangalore), Ajith Suresh (Indian Institute of Science, Bangalore)

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Zhongjie Ba (Zhejiang University and McGill University), Tianhang Zheng (University of Toronto), Xinyu Zhang (Zhejiang University), Zhan Qin (Zhejiang University), Baochun Li (University of Toronto), Xue Liu (McGill University), Kui Ren (Zhejiang University)

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

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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)