Florian Hofhammer (EPFL), Marcel Busch (EPFL), Qinying Wang (EPFL and Zhejiang University), Manuel Egele (Boston University), Mathias Payer (EPFL)

Dynamic analysis of microcontroller-based embedded firmware remains challenging. The general lack of source code availability for Commercial-off-the-shelf (COTS) firmware prevents powerful source-based instrumentation and prohibits compiling the firmware into an executable directly runnable by an analyst. Analyzing firmware binaries requires either acquisition and configuration of custom hardware, or configuration of extensive software stacks built around emulators. In both cases, dynamic analysis is limited in functionality by complex debugging and instrumentation interfaces and in performance by low execution speeds on Microcontroller Units (MCUs) and Instruction Set Architecture (ISA) translation overheads in emulators.

SURGEON provides a performant, flexible, and accurate rehosting approach for dynamic analysis of embedded firmware. We introduce transplantation to transform binary, embedded firmware into a Linux user space process executing natively on compatible high-performance systems through static binary rewriting. In addition to the achieved performance improvements, SURGEON scales horizontally through process instantiation and provides the flexibility to apply existing dynamic analysis tooling for user space processes without requiring adaptations to firmware-specific use cases. SURGEON’s key use cases include debugging binary firmware with off-the-shelf tooling for user space processes and fuzz testing.

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LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions,...

Takami Sato (University of California, Irvine), Yuki Hayakawa (Keio University), Ryo Suzuki (Keio University), Yohsuke Shiiki (Keio University), Kentaro Yoshioka (Keio University), Qi Alfred Chen (University of California, Irvine)

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Does Representation Matter? Evaluating IRs for LLM-based Binary Decompilation

Tomás Pelayo-Benedet (Universidad de Zaragoza), Kevin Borgolte (Ruhr University Bochum), Ricardo J. Rodríguez (Universidad de Zaragoza)

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Enhancing Symbolic Execution by Machine Learning Based Solver Selection

Sheng-Han Wen (National Taiwan University), Wei-Loon Mow (National Taiwan University), Wei-Ning Chen (National Taiwan University), Chien-Yuan Wang (National Taiwan University), Hsu-Chun Hsiao (National Taiwan University)

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