Chloe Fortuna (STR), JT Paasch (STR), Sam Lasser (Draper), Philip Zucker (Draper), Chris Casinghino (Jane Street), Cody Roux (AWS)

Modifying a binary program without access to the original source code is an error-prone task. In many cases, the modified binary must be tested or otherwise validated to ensure that the change had its intended effect and no others—a process that can be labor-intensive. This paper presents CBAT, an automated tool for verifying the correctness of binary transformations. CBAT’s approach to this task is based on a differential program analysis that checks a relative correctness property over the original and modified versions of a function. CBAT applies this analysis to the binary domain by implementing it as an extension to the BAP binary analysis toolkit. We highlight several features of CBAT that contribute to the tool’s efficiency and to the interpretability of its output. We evaluate CBAT’s performance by using the tool to verify modifications to three collections of functions taken from real-world binaries.

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Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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