Chen Chen (Texas A&M University, USA), Zaiyan Xu (Texas A&M University, USA), Mohamadreza Rostami (Technische Universitat Darmstadt, Germany), David Liu (Texas A&M University, USA), Dileep Kalathil (Texas A&M University, USA), Ahmad-Reza Sadeghi (Technische Universitat Darmstadt, Germany), Jeyavijayan (JV) Rajendran (Texas A&M University, USA)

Processor designs rely on iterative modifications and reuse well-established designs. However, this reuse of prior designs also leads to similar vulnerabilities across multiple processors. As processors grow increasingly complex with iterative modifications, efficiently detecting vulnerabilities from modern processors is critical. Inspired by software fuzzing, hardware fuzzing has recently demonstrated its effectiveness in detecting processor vulnerabilities. Yet, to our best knowledge, existing processor fuzzers fuzz each design individually, lacking the capability to understand known vulnerabilities in prior processors to fine-tune fuzzing to identify similar or new variants of vulnerabilities.

To address this gap, we present ReFuzz, an adaptive fuzzing framework that leverages contextual bandit to reuse highly effective tests from prior processors to fuzz a processor-undertest (PUT) within a given ISA. By intelligently mutating tests that trigger vulnerabilities in prior processors, ReFuzz detects similar and new variants of vulnerabilities in PUTs. ReFuzz uncovered three new security vulnerabilities and two new functional bugs. ReFuzz detected one vulnerability by reusing a test that triggers a known vulnerability in a prior processor. One functional bug exists across three processors that share design modules. The second bug has two variants. Additionally, ReFuzz reuses highly effective tests to enhance efficiency in coverage, achieving an average 511.23× coverage speedup and up to 9.33% more total coverage, compared to existing fuzzers.

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Work-in-progress: Assertive Trace

Shun Kashiwa (UC San Diego), Michael Coblenz (UC San Diego), Deian Stefan (UC San Diego)

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HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilation

Yonatan Gizachew Achamyeleh (University of California, Irvine), Harsh Thomare (University of California, Irvine), Mohammad Abdullah Al Faruque (University of California, Irvine)

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Prεεmpt: Sanitizing Sensitive Prompts for LLMs

Amrita Roy Chowdhury (University of Michigan, Ann Arbor), David Glukhov (University of Toronto and Vector Institute), Divyam Anshumaan (University of Wisconsin-Madison), Prasad Chalasani (Langroid Incorporated), Nicholas Papernot (University of Toronto and Vector Institute), Somesh Jha (University of Wisconsin-Madison), Mihir Bellare (University of California, San Diego)

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