Akul Goyal (Provenance Security, Inc.), Adam Bates (Provenance Security, Inc.)

Provenance-based security applications are showing tremendous promise in the academic literature, but successfully transition these technologies to practice will require community stakeholders to demonstrate the business potential of provenance analysis. Customer Discovery is a structured process through which early-stage start-ups can validate the commercial potential of an idea through direct interaction with potential customers. As a provenance-based security start-up, we conducted hundreds of customer discovery interviews, and believe that many of our findings would be of interest to the broader academic community. In this position paper, we summarize our findings and consider how they could inform future research on provenance analysis.

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Causal-Guided Detoxify Backdoor Attack of Open-Weight LoRA Models

Linzhi Chen (ShanghaiTech University), Yang Sun (Independent Researcher), Hongru Wei (ShanghaiTech University), Yuqi Chen (ShanghaiTech University)

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How to Effectively Trace Provenance on Windows Endpoint Detection...

Jason Liu (University of Illinois at Urbana-Champaign), Muhammad Adil Inam (University of Illinois at Urbana-Champaign), Akul Goyal (University of Illinois at Urbana-Champaign), Dylen Greenenwald (University of Illinois at Urbana-Champaign), Adam Bates (University of Illinois at Urbana-Champaign), Saurav Chittal (Purdue University)

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TENSURE: Fuzzing Sparse Tensor Compilers (Registered Report)

Kabilan Mahathevan (Department of Computer Science, Virginia Tech, Blacksburg), Yining Zhang (Department of Computer Science, Virginia Tech, Blacksburg), Muhammad Ali Gulzar (Department of Computer Science, Virginia Tech, Blacksburg), Kirshanthan Sundararajah (Department of Computer Science, Virginia Tech, Blacksburg)

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