arXiv AI By Neil Archibald, Ruben Thijssen

LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics

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arXiv:2606. 06838v1 Announce Type: cross Abstract: Automatic decompilers produce functionally correct but often unreadable C code.

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arXiv AI
Aug 10

Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering

arXiv:2608. 07038v1 Announce Type: cross Abstract: Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing the cognitive burden of reverse analysis and improving efficiency.

By Xiuwei Shang, Li Hu, Xiao Jiang, Jieke Shi, Junda He, Zhou Yang, Shaoyin Cheng, Guoqiang Chen, Weiming Zhang, David Lo
arXiv AI
6d ago

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

arXiv:2608. 11469v1 Announce Type: cross Abstract: AI agents are rapidly improving in cybersecurity capabilities when the source code is available for analysis, yet much of the software most consequential to cybersecurity, including malware, firmware, and proprietary applications, is available only as binaries.

By Jeremy Spence, Nicholas Assaderaghi, Jinhao Zhu, Nikil Ravi, Raluca Ada Popa, Guannan Wei, Yangruibo Ding, Zhuo Zhang