Davide Rusconi (University of Milan), Osama Yousef (University of Milan), Mirco Picca (University of Milan), Danilo Bruschi (University of Milan), Flavio Toffalini (Ruhr-Universitat Bochum),  Andrea Lanzi (University of Milan)

In this paper, we show E-FuzzEdge, a novel fuzzing architecture targeted towards improving the throughput of fuzzing campaigns in contexts where scalability is unavailable. E-FuzzEdge addresses the inefficiencies of hardware-in-the-loop fuzzing for microcontrollers by optimizing execution speed. We evaluated our system against both real-world embedded libraries and state-of-the-art benchmarks, demonstrating significant performance improvements. A key advantage of the E-FuzzEdge architecture is its compatibility with other embedded fuzzing techniques that perform on device testing instead of firmware emulation. This means that the broader embedded fuzzing community can integrate E-FuzzEdge into their workflows to enhance overall testing efficiency.

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Muhammad Hassan (University of Illinois Urbana Champaign), Carl Gunter (University of Illinois Urbana Champaign), Susan Landau (Tufts University), Masooda Bashir (University of Illinois Urbana Champaign)

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Achieving Zen: Combining Mathematical and Programmatic Deep Learning Model...

David Oygenblik (Georgia Institute of Technology), Dinko Dermendzhiev (Georgia Institute of Technology), Filippos Sofias (Georgia Institute of Technology), Mingxuan Yao (Georgia Institute of Technology), Haichuan Xu (Georgia Institute of Technology), Runze Zhang (Georgia Institute of Technology), Jeman Park (Kyung Hee University), Amit Kumar Sikder (Iowa State University), Brendan Saltaformaggio (Georgia Institute of Technology)

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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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