ReFuzz：基于上下文老虎机的处理器模糊测试重用
ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits
AI总结：
ReFuzz通过上下文老虎机重用先前处理器的高效测试用例，提升处理器模糊测试的效率和漏洞检测能力。
摘要：
处理器设计依赖于迭代修改和重用已确立的设计。然而，这种对先前设计的重用也导致多个处理器中出现相似的漏洞。随着处理器变得越来越复杂，通过迭代修改进行检测漏洞变得至关重要。受软件模糊测试的启发，硬件模糊测试最近已证明在检测处理器漏洞方面的有效性。然而，据我们所知，现有的处理器模糊器每个设计都单独进行模糊测试，缺乏理解先前处理器中已知漏洞的能力，无法精细调整模糊测试以识别相似或新的漏洞变种。为了解决这一差距，我们提出了ReFuzz，一个自适应的模糊测试框架，利用上下文老虎机重用先前处理器中高效的测试用例，以在给定的指令集架构（ISA）内模糊测试被测处理器（PUT）。通过智能地修改触发先前处理器中漏洞的测试用例，ReFuzz有效地检测了被测处理器中的相似和新漏洞变种。ReFuzz发现了三个新的安全漏洞和两个新的功能错误。ReFuzz通过重用触发先前处理器中已知漏洞的测试用例检测了一个漏洞。一个功能错误存在于三个共享设计模块的处理器中。第二个错误有两个变种。此外，ReFuzz通过重用高效的测试用例提高了覆盖率效率，实现了平均511.23倍的覆盖率加速，并比现有模糊器多了高达9.33%的总覆盖率。
英文摘要：
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-under-test (PUT) within a given ISA. By intelligently mutating tests that trigger vulnerabilities in prior processors, ReFuzz effectively 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 511.23x coverage speedup and up to 9.33% more total coverage, compared to existing fuzzers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
